Coordinates and their metric coefficients.
\r\n\tThe book will present up to date knowledge on mentioned ADHD topics in order to be implemented in every day clinical practice.
",isbn:"978-1-83962-495-7",printIsbn:"978-1-83962-475-9",pdfIsbn:"978-1-83962-496-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"176f5275d9e1e06b24e0ae07b90c424f",bookSignature:"Prof. Hojka Gregoric Kumperscak",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9499.jpg",keywords:"Clinical Picture, Symptomatology, Symptoms, Clinical Presentation, Comorbidity, Pharmacotherapy, Nonpharmacological, Nutrition and Diet, Genetics, Neuroimaging, Neurotransmitters, Hormones",numberOfDownloads:493,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 10th 2020",dateEndSecondStepPublish:"August 6th 2020",dateEndThirdStepPublish:"October 5th 2020",dateEndFourthStepPublish:"December 24th 2020",dateEndFifthStepPublish:"February 22nd 2021",remainingDaysToSecondStep:"7 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Prof. Kumperscak, MD, PhD graduated from the Faculty of Medicine in Ljubljana, Slovenia. She was trained in child and adolescent psychiatry in Slovenia and abroad. She has held the Chair of the Department of Psychiatry in the University of Maribor in Slovenia (2017) and has been Head of the Child and Adolescent Psychiatry Unit, University Clinical Center in Maribor (2008). She is a President of the Slovenian Association for Child and Adolescent Psychiatry and Adolescent Identity Treatment psychotherapist.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53417",title:"Prof.",name:"Hojka",middleName:null,surname:"Gregoric Kumperscak",slug:"hojka-gregoric-kumperscak",fullName:"Hojka Gregoric Kumperscak",profilePictureURL:"https://mts.intechopen.com/storage/users/53417/images/system/53417.jpg",biography:"Prof. Hojka Gregoric Kumperscak, MD, PhD was born in Maribor, Slovenia in 1970. She finished Faculty of Medicine in Ljubljana, Slovenia in 1996. She was trained in child and adolescent psychiatry in Slovenia and abroad (Italy, UK, Germany and Switzerland). \r\nShe has held the Chair of the Department of Psychiatry in the Faculty of Medicine, University of Maribor in Slovenia, since January 2017, and has been Head of the Child and Adolescent Psychiatry Unit, University Clinical Center in Maribor since 2008. She is a President of Slovenian Association for Child and Adolescent Psychiatry and Adolescent Identity Treatment psychotherapist. Her clinical work is mainly with adolescents with ADHD, personality and psychotic disorders.",institutionString:"University of Maribor",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Maribor",institutionURL:null,country:{name:"Slovenia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:[{id:"73389",title:"Adult Attention-Deficit/Hyperactivity Disorder and Substance Use Disorder: A Systematic Review of the Literature",slug:"adult-attention-deficit-hyperactivity-disorder-and-substance-use-disorder-a-systematic-review-of-the",totalDownloads:93,totalCrossrefCites:0,authors:[null]},{id:"73816",title:"Role of Copy Number Variations in ADHD",slug:"role-of-copy-number-variations-in-adhd",totalDownloads:58,totalCrossrefCites:0,authors:[null]},{id:"73908",title:"Traditional Scales Diagnosis and Endophenotypes in Attentional Deficits Disorders: Are We on the Right Track?",slug:"traditional-scales-diagnosis-and-endophenotypes-in-attentional-deficits-disorders-are-we-on-the-righ",totalDownloads:109,totalCrossrefCites:0,authors:[null]},{id:"73881",title:"Comorbidity in Children and Adolescents with ADHD",slug:"comorbidity-in-children-and-adolescents-with-adhd",totalDownloads:57,totalCrossrefCites:0,authors:[null]},{id:"73105",title:"ADHD and Impact on Language",slug:"adhd-and-impact-on-language",totalDownloads:182,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247041",firstName:"Dolores",lastName:"Kuzelj",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247041/images/7108_n.jpg",email:"dolores@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. Mauricio Barría",coverURL:"https://cdn.intechopen.com/books/images_new/6550.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"70602",title:"Numerical Simulation of the Spin Coating of the Interior of Metal Beverage Cans",doi:"10.5772/intechopen.90381",slug:"numerical-simulation-of-the-spin-coating-of-the-interior-of-metal-beverage-cans",body:'When a liquid is applied to a spinning substrate, or if a pre-wetted substrate is spun, centrifugal forces act to drive any irregularities in the film thickness outward, away from the axis of rotation. The result is that the film becomes thinner and more uniform as the rotation proceeds. Consequently spin coating is used in such applications as coating magnetic storage discs, optical devices, and semiconductor wafers to obtain very thin but uniform films on flat substrates.
When the substrate is curved, centrifugal forces will act to produce a uniform layer only in horizontal regions where the substrate is perpendicular to the axis of rotation. But the coating layer may be very irregular in regions where the substrate is highly curved and the normal vector from the surface is not parallel to the axis of rotation.
In this work we will derive the lubrication form of the fluid mechanical equations for a thin liquid film sprayed on an arbitrarily curved, rotating, axisymmetric substrate. Our goal is to predict how the coating thickness changes with time as a function of the substrate geometry, the rotation rate, the rheological properties of the coating liquid, and the geometry and flux of the spray gun. We then discretize the equations and solve the partial differential equations governing the flow. Using an implicit method of solving the finite difference representation of the partial differential equations, we require a minimum of computer resources.
The theory we develop in this work is used to analyze one specific application: the spray/spin coating of the interior of aluminum beverage containers. When a spinning can is spray painted, centrifugal forces help to cause a more uniform coating layer on the can substrate. Indeed this is the purpose of rotating the can a high spin rates while spray coating the interior. But centrifugal forces can also cause such coating irregularities as drop formation. In this work we will demonstrate how such parameters as the spray gun placement and the rotation rate contribute to how long the beverage can may remain in the spin phase of its coating process before this potential defect occurs.
Consider an arbitrarily curved, axisymmetric substrate with the parameter s representing arc length along the substrate. The parameter
Curvilinear coordinate system. ϕ is the circumferential angle, es is the unit vector parallel to the substrate, en the unit vector perpendicular to the substrate, and rs is the distance from the centerline to a given point on the substrate. θ is the angle of the substrate with respect to the horizontal, ω is the rotation rate, and g is the acceleration of gravity.
Metric coefficient | Coordinate | Velocity |
---|---|---|
Coordinates and their metric coefficients.
If
Here
If
We define
At the free surface, the substantial derivative of F must be zero. Consequently the kinematic condition on the free surface is given by
Employing the substitution
the mean curvature of the free surface for this geometry is given by
at
If the atmospheric pressure is zero, then the tensor equation relating the change in pressure across the free surface due to surface tension is given by
Here
If we sum the three equations found in Eq. (5) over
all evaluated at the free surface
We now scale the dependent and independent variables with various characteristic lengths of the substrate geometry. These include
If
We can eliminate the pressure from the
The scaled form of the continuity equation is given by
At the substrate,
The scaled form of the kinematic condition is shown to be
at
at
Instead of transforming the equations representing the pressure discontinuity across the liquid interface, given by Eq. (9) and Eq. (10), we will eliminate the pressure term in Eqs. (9) and (10) using the momentum equations and the identities for the partial differentiation of implicit functions. Eq. (7) gives the pressure in the liquid at the free surface
Plugging the pressure at the interface, found by Eq. (7), into the momentum equations yields
where
Here the
We now expand the scaled velocities in a regular perturbation series expansion in powers of the small parameter
We then use boundary conditions to determine the appropriate constants of integration and solve for
where
with the constants
Using the appropriate boundary conditions, the order
The evolution equation, to order
The dimensional evolution equation is found to be given by
We shall employ the maximum radius of the substrate as our length scale,
Substituting these scale factors in the dimensional evolution equation, Eq. (22), leads to the nondimensional evolution equation
in nondimensional units. Here
The geometry of the substrate is delineated by a schematic drawing which gives us the radius of each circular arc,
Substrate made up of straight segments and circular arcs of radius Ri and subtended angle φi.
We assume that the coating layer is laid down over time by a spray gun that emits a fan of gas that is directed toward the can substrate. Thus the accumulation of coating with time due to this fan is a function of s and t:
Assumed spray pattern.
If
If the flux does not vary significantly in the
Beverage can and spray pattern geometry. Here υ is the distance from the orifice of the spray gun to a given point on the can substrate.
When expressed as a function of
From Figure 4 we have the relation
For a sufficiently thin fan, where the point on the substrate is sufficiently far from the centerline
When we are near the centerline, this formula must be modified to
to account for the fact that here the substrate is constantly being reached by the spray fan.
We also assume that there is a secondary “gas” which is uniform in the interior of the can and results in a constant
where
In an effort to determine the maximum value a droplet can form without detaching from the substrate, we will consider an axisymmetric droplet forming on the underside of a ceiling, as illustrated in Figure 5. Here
Profile view of axisymmetric pendant drop.
If we nondimensionalize the problem by scaling
Here
Using a standard Runge–Kutta method, we can numerically integrate Eq. (27) assuming
If
Approximately 400 billion two-piece, all-aluminum cans are produced annually for the purpose of storing beverages for distribution worldwide. The interior of each of these cans must be coated to protect the aluminum from onslaught due to corrosive elements in the contained beverage, and the beverage must be protected from picking up metal ions or other off-flavors from the aluminum substrate. Consequently the coating must be as uniform as possible for thick regions may slough off and thin regions may not offer adequate protection. To achieve a uniform film thickness, spin coating is employed using a spray fan to distribute the coating on the can substrate. But because the can is highly curved due to structural considerations, achieving a uniform final film thickness is much more complicated than for a flat substrate. In this section we will apply the analytical and numerical model we developed in previous sections to determine how the many parameters are influencing the flow of the paint coating. These parameters include the rotation rate, the shape of the can, the coating fluids physiochemical properties, and the geometry and flux of the spray fan, all affecting the final film thickness distribution.
The can body is initially punched from sheet aluminum and then goes through a washing process to produce a substrate suited for the spray coating. The can is then spun at between 2500 and 3500 rotations per minute, and one or two spray guns spray the interior with the liquid paint film. Centrifugal and gravitational forces redistribute this liquid layer as the can continues to spin after the initial spray process. The can is then placed in an oven where the solvent is allowed to evaporate leaving only the hardened resin. Then the can is filled with the beverage and the top of the can is attached in place. This conveyer process can produce as many as 1700 filled cans per minute.
In practice, one or two spray guns are used to coat the interior of the spinning cans. These are oriented at between 5° and 30° with respect to the vertical axis of the can and placed between 0.5 and 1.5 cm vertically from the top of the sidewall [6, 7]. Typically the can is sprayed for between 0.05 and 0.2 s and spun for an additional 0.1–0.5 s [6] so that centrifugal forces can act to redistribute the coating layer.
The industry uses schematic drawings which plot the substrate as a function of circular arcs of radius Ri, and subtended angles
Profile of the can substrate in red lines and the coating layer in blue lines at t̂=.10, in the middle of fast spin with spray phase. Dimensionless variables are used.
Profile of the can substrate in red lines and coating layer in blue at t̂=.20, in the middle of the fast spin phase. Dimensionless variables are used.
Profile of the can substrate in red lines and coating layer in blue at t̂=t̂max=.45, near the end of the fast spin phase. Dimensionless variables are used.
We will assume that the spray fan has an elliptical cross section with a ratio of the major axis to the length of the minor axis of 10. The parameters determining the placement and orientation of the spray gun are illustrated in Figure 9. For the simulation considered in this work, the nondimensional parameters are listed in Table 1, the dimensional parameters in Table 2, and typical properties of the coating liquid in Table 3.
Profile view of can and spray gun placement and orientation parameters.
Parameter | Symbol | Value |
---|---|---|
Distance of spray gun from centerline: fan #1, fan #2 | −0.15, −0.45 | |
Distance of spray gun above can: fan #1, fan #2 | 0.15, 0.45 | |
Angle of spray gun wrt vertical: fan #1, fan #2 | 28°, 15° | |
Subtended angle of spray fan | 100° | |
Time spray gun acts in fast spin phase | 0.15 | |
Percent of secondary spray | 5% |
Nondimensional parameters for this simulation.
Parameter | Symbol | Value |
---|---|---|
Can radius | R | 3.33 cm |
Rotation rate | 2500–3500 RPM | |
Distance of spray gun from centerline: fan #1, fan #2 | A | −0.5 cm, −1.5 cm |
Distance of spray gun above can: fan #1, fan #2 | B | 0.5 cm, 1.5 cm |
Average wet coating thickness | 0.0028 cm | |
Time spray gun acts in fast spin phase | 0.05 s |
Dimensional parameters for this simulation.
From Eq. (26), we see that the coating applied by the spray gun is an inverse function of the radius of the can substrate
The dimensionless parameter
Critical time for droplet formation, t̂max, versus centrifugal force parameter. Here dimensionless variables are used.
The surface tension, density, and viscosity of the coating liquid are difficult to significantly alter as they depend on the required organic solvent content and surfactant levels in the paint formula. Similarly, R, the beverage can radius, is fixed by industry production standards. This leaves the rate of rotation as the only significant production parameter for changing the nondimensional (centrifugal force)/(surface tension force) parameter.
The centrifugal ejection of coating liquid from the inner wall of the moat is also predicted to be a strong function of the position and orientation of the spray gun. In the above example, the gun is placed 0.5 to the left of the centerline, is 0.5 cm above the top of the can, and is angled at
Critical time for droplet formation, t̂max, versus centrifugal force parameter for two different spray gun placements. For fan #1, the angle of inclination is 28∘, A1=−0.15, B1=0.45. For fan #2, the angle of inclination is 15∘, A2=−0.45, B2=0.45. Dimensionless variables are used.
In this work we have used scaling arguments and perturbation theory to derive the lubrication form of the governing fluid mechanical equations for a thin liquid film coating an arbitrarily curved, axisymmetric, rotating substrate. Our main purpose has been to develop mathematical model that can be employed to numerically simulate the application of a paint film to the interior of beverage cans, though the basic algorithm may be useful in other applications. We have used our algorithm to predict the time of centrifugal ejection of coating from the inner moat wall as a function of several input parameters: the physiochemical properties of the coating liquid, the rotation rate, and the spray gun placement. The model can also be used to predict other coating defects and how the input parameters can be used to avoid them. The effect of solvent evaporation during the drying phase, when gravity and surface tension forces affect the coating distribution as the viscosity increases until only a final, hard, film remains, may also be modeled. With so many parameters regulating the final coating thickness, using experiments to model the coating evolution and measure the final, dry, film thickness is an almost impossible task. Instead we can utilize the power and versatility of computer simulation to predict the coating profile as a function of input parameters. This understanding will be useful in optimizing the current application process. It may also be essential in acquiring a satisfactory coating when environmental regulations require a change to high solids and latex paints.
Artificial neural networks (ANN), which are mathematical models for function approximation, classification, pattern recognition, nonlinear control, etc., have been successfully applied in the field of time series analysis and forecasting instead of linear models such as 1970s ARIMA [1] since 1980s [2, 3, 4, 5, 6, 7]. In [2], Casdagli used a radial basis function network (RBFN) which is a kind of feed-forward neural network with Gaussian hidden units to predict chaotic time series data, such as the Mackey-Glass, the Ikeda map, and the Lorenz chaos in 1989. In [3, 4], Lendasse et al. organized a time series forecasting competition for neural network prediction methods with a five-block artificial time series data named CATS since 2004. The goal of CATS competition was to predict 100 missing values of the time series data in five sets which included 980 known values and 20 successive unknown values in each set (details are in Section 3.1). There were 24 submissions to the competition, and five kinds of methods were selected by the IJCNN2004: filtering techniques including Bayesian methods, Kalman filters, and so on; recurrent neural networks (RNNs); vector quantization; fuzzy logic; and ensemble methods. As the comment of the organizers, the different prediction precisions were reported though the similar prediction methods were used for the know-how and experience of the authors. So the development of time series forecasting by ANN is still on the way.
\nAs a kind of classifiers or a kind of function approximators, the advances of the ANN are bought out by the nonlinear transforms to the input space. In fact, units (or neurons) with nonlinear firing functions connected to each other usually produce higher dimensional output space and various feature spaces in the networks. Additionally, as a connective system, it is not necessary to design fixed mathematical models for different nonlinear phenomena, but adjusting the weights of connections between units. So according to the report of NN3—Artificial Neural Networks and Computational Intelligence Forecasting Competition [5], there have been more than 5000 publications of time series forecasting using ANN till 2007.
\nTo find the suitable parameters of ANN, such as weights of connections between neurons, error back-propagation (BP) algorithm [6] is generally utilized in the training process of ANN. However, due to every sample data (a pair of the input data and the output data) is used in the BP method, noise data influences the optimization of the model, and robustness of the model becomes weak for unknown input. Another problem of ANN models is how to determine the structure of the network, i.e., the number of layers and the number of neurons in each layer. To overcome these problems of BP, Kuremoto et al. [7] adopted a reinforcement learning (RL) method “stochastic gradient ascent (SGA)” [8] to adjust the connection weights of units and the particle swarm optimization (PSO) to find the optimal structure of ANN. SGA, which is proposed by Kimura and Kobayshi, improved Williams’ REINFORCE [9], which uses rewards to modify the stochastic policies (likelihood). In SGA learning algorithm, the accumulated modification of policies named “eligibility trace” is used to adjust the parameters of model (see Section 2). In the case of time series forecasting, the reward of RL system can be defined as a suitable error zone to instead of the distance (error) between the output of the model and the teach data which is used in BP learning algorithm. So the sensitivity to noise data is possible to be reduced, and the robustness to the unknown data may be raised. As a deep learning method for time series forecasting, Kuremoto et al. [10] firstly applied Hinton and Salakhutdinov’s deep belief net (DBN) which is a kind of stacked auto-encoder (SAE) composed by multiple restricted Boltzmann machines (RBMs) [11]. An improved DBN for time series forecasting is proposed in [12], which DBN is composed by multiple RBMs and a multilayer perceptron (MLP) [6]. The improved DBN with RBMs and MLP [6] gives its priority to the conventional DBN [5] for time series forecasting due to the continuous output unit is used; meanwhile the conventional one had a binary value unit in the output layer.
\nAs same as the RL method, SGA adopted to MLP, RBFN, and self-organized fuzzy neural network (SOFNN) [7]; the prediction precision of DBN utilized SGA may also be raised comparing to the BP learning algorithm. Furthermore, it is available to raise the prediction precision by a hybrid model which forecasts the future data by the linear model ARIMA at first and modifying the forecasting by the predicted error given by an ANN which is trained by error time series [13, 14].
\nIn this chapter, we concentrate to introduce the DBN which is composed by multiple RBMs and MLP and show the higher efficiency of the RL learning method SGA for the DBN [15, 16] comparing to the conventional learning method BP using the results of time series forecasting experiments. Kinds of benchmark data including artificial time series data CATS [3], natural phenomenon time series data provided by Aalto University [18], and TSDL [18] were used in the experiments.
\nThe model of time series forecasting is given as the following:
\nDenote t = 1, 2, 3, …, where T is the time, n is the dimensionality of the input of function f(x), \n
A deep belief net (DBN) composed by restricted Boltzmann machines (RBMs) and multilayer perceptron (MLP) is shown in Figure 1.
\nThe structure of DBN for time series forecasting.
Restricted Boltzmann machine (RBM) is a kind of probabilistic generative neural network which composed by two layers of units: visible layer and hidden layer (see Figure 2).
\nThe structure of RBM.
Units of different layers connect to each other with weights \n
Here \n
where \n
Multilayer perceptron (MLP) is the most popular neural network which is generally composed by three layers of units: input layer, hidden layer, and output layer (see Figure 3).
\nThe structure of MLP.
The output of the unit \n
Here n is the dimensionality of the input, K is the number of hidden units, and \n
The learning rules of MLP using error back-propagation (BP) method [5] are given as follows:
\nwhere \n
The learning algorithm of MLP using BP is as follows:
\nStep 1. Observe an input \n
Step 2. Predict a future data \n
Step 3. Calculate the modification of connection weights, \n
Step 4. Modify the connections,
\nStep 5. For the next time step \n
As same as the training process proposed in [10], the training process of DBN is performed by two steps. The first one, pretraining, utilizes the learning rules of RBM, i.e., Eqs. (4–6), for each RBM independently. The second step is a fine-tuning process using the pretrained parameters of RBMs and BP algorithm. These processes are shown in Figure 4 and Eqs. (11)–(13).
\nThe training of DBN by BP method.
In the case of reinforcement learning (RL), the output is decided by a probability distribution, e.g., the Gaussian distribution \n
The learning algorithm of stochastic gradient ascent (SGA) [7] is as follows.
\nStep 1. Observe an input \n
Step 2. Predict a future data \n
Step 3. Receive a scalar reward/punishment \n
where \n
Step 4. Calculate characteristic eligibility \n
where \n
Step 5. Calculate the modification \n
where \n
Step 6. Improve the policy Eq. (16) by renewing its internal variable \n
where \n
Step 7. For the next time step \n
Characteristic eligibility \n
The calculation of \n
The \n
The learning rate \n
where is \n
The learning errors given by different learning rates.
The number of RBM that constitute the DBN and the number of neurons of each layer affects prediction performance seriously. In [9], particle swarm optimization (PSO) method is used to decide the structure of DBN, and in [13] it is suggested that random search method [16] is more efficient. In the experiment of time series forecasting by DBN and SGA shown in this chapter, these meta-parameters were decided by the random search, and the exploration limits are shown as the following.
The number of RBMs: [0–3]
The number of units in each layer of DBN: [2–20]
Fixed learning rate of SGA in Eq. (21): [10−5–10−1]
Discount factor in Eq. (19): [10−5–10−1]
Coefficient in Eq. (27) [0.5–2.0]
The optimization algorithm of these meta-parameters by the random search method is as follows:
\nStep 1. Set random values of meta-parameters beyond the exploration limitations.
\nStep 2. Predict a future data \n
Step 3. If the error between \n
or else if the error is not changed,
\nstop the exploration,
\nelse return to step 1.
\nCATS time series data is the artificial benchmark data for forecasting competition with ANN methods [3, 4].This artificial time series is given with 5000 data, among which 100 are missed (hidden by competition the organizers). The missed data exist in five blocks:
Elements 981 to 1000
Elements 1981 to 2000
Elements 2981 to 3000
Elements 3981 to 4000
Elements 4981 to 5000
The mean square error \n
where \n
CATS benchmark data.
The prediction results of different blocks of CATS data are shown in Figure 7. Comparing to the conventional learning method of DBN, i.e., using Hinton’s RBM unsupervised learning method [6, 8] and back-propagation (BP), the proposed method which used the reinforcement learning method SGA instead of BP showed its superiority in the sense of the average prediction precision E1 (see Figure 7f). In addition, the proposed method, DBN with SGA, yielded the highest prediction (E1 measurement) comparing to all previous studies such as MLP with BP, the best prediction of CATS competition IJCNN’04 [4], the conventional DBNs with BP [9, 11], and hybrid models [13]. The details are shown in Table 1.
\nThe prediction results of different methods for CATS data: (a) block 1; (b) block 2; (c) block 3; (d) block 4; (e) block 5; and (f) results of the long-term forecasting.
Method | \nE1 | \n
---|---|
DBN(SGA) [18] | \n170 | \n
DBN(BP) + ARIMA [14] | \n244 | \n
DBN [11] (BP) | \n257 | \n
Kalman Smoother (the best of IJCNN ‘04) [4] | \n408 | \n
DBN [9] (2 RBMs) | \n1215 | \n
MLP [9] | \n1245 | \n
A hierarchical Bayesian learning (the worst of IJCNN ‘04) [4] | \n1247 | \n
ARIMA [1] | \n1715 | \n
ARIMA+MLP(BP) [12] | \n2153 | \n
ARIMA+DBN(BP) [14] | \n2266 | \n
The long-term forecasting error comparison of different methods using CATS data.
The meta-parameters obtained by random search method are shown in Table 2. And we found that the MSE of learning, i.e., given by one-ahead prediction results, showed that the proposed method has worse convergence compared to the conventional BP training. In Figure 8, the case of the first block learning MSE of two methods is shown. The convergence of MSE given by BP converged in a long training process and SGA gave unstable MSE of prediction. However, as the basic consideration of a sparse model, the better results of long-term prediction of the proposed method may successfully avoid the over-fitting problem which is caused by the model that is built too strictly by the training sample and loses its robustness for unknown data.
\n\n | DBN with SGA | \nDBN with BP | \n
---|---|---|
The number of RBMs | \n3 | \n1 | \n
Learning rate of RBM | \n0.048-0.055-0.026 | \n0.042 | \n
Structure of DBN (the number of units and layers) | \n14-14-18-19-18-2 | \n5-11-2-1 | \n
Learning rate of SGA or BP | \n0.090 | \n0.090 | \n
Discount factor \n | \n0.082 | \n— | \n
Coefficient \n | \n1.320 | \n— | \n
Meta-parameters of DBN used for the CATS data (block 1).
Change of the learning error during fine-tuning (CATS data [1–980]).
Three types of natural phenomenon time series data provided by Aalto University [17] were used in the one-ahead forecasting experiments of real time series data.
CO2: Atmospheric CO2 from continuous air samples weekly averages atmospheric CO2 concentration derived from continuous air samples, Hawaii, 2225 data
Sea level pressures: Monthly values of the Darwin sea level pressure series, A.D. 1882–1998, 1300 data
Sunspot number: Monthly averages of sunspot numbers from A.D. 1749 to the present 3078 values
The prediction results of these three datasets are shown in Figure 9. Short-term prediction error is shown in Table 3. DBN with the SGA learning method showed its priority in all cases.
\nPrediction results by DBN with BP and SGA. (a) Prediction result of CO2 data. (b) Prediction result of Sea level pressure data. (c) Prediction result of Sun spot number data.
Data | \nDBN with BP | \nDBN with SGA | \n
---|---|---|
CO2 | \n0.2671 | \n0.2047 | \n
Sea level pressure | \n0.9902 | \n0.9003 | \n
Sun spot number | \n733.51 | \n364.05 | \n
Prediction MSE of real time series data [17].
The efficiency of random search to find the optimal meta-parameters, i.e., the structure of RBM and MLP, learning rates, discount factor, etc. which are explained in Section 2.5 is shown in Figure 10 in the case of DBN with SGA learning algorithm. The random search results are shown in Table 4.
\nChanges of learning error by random search for DBN with SGA.
Data series | \nTotal data | \nTesting data | \nDBN with BP (the number of units) | \nDBN with SGA (the number of units) | \n
---|---|---|---|---|
CO2 | \n2225 | \n225 | \n15-17-17-1 | \n20-18-7-2 | \n
Sea level pressure | \n1400 | \n400 | \n16-18-18-1 | \n16-20-8-7-2 | \n
Sun spot number | \n3078 | \n578 | \n20-20-17-18-1 | \n19-19-20-10-2 | \n
Meta-parameters of DBN used for real time series forecasting.
We also used seven types of natural phenomenon time series data of TSDL [18]. The data to be predicted was chosen based on [19] which are named as Lynx, Sunspots, River flow, Vehicles, RGNP, Wine, and Airline. The short-term (one-ahead) prediction results are shown in Figure 11 and Table 5.
\nPrediction results of natural phenomenon time series data of TSDL. (a) Prediction result of Lynx; (b) prediction result of sunspots; (c) prediction result of river flow; (d) prediction result of vehicles; (e) prediction result of RGNP; (f) prediction result of wine; and (g) prediction result of airline.
Data | \nDBN with BP | \nDBN with SGA | \n
---|---|---|
Lynx | \n0.6547 | \n0.3593 | \n
Sunspots | \n999.54 | \n904.35 | \n
River flow | \n24262.24 | \n16980.46 | \n
Vehicles | \n6.0670 | \n6.1919 | \n
RGNP | \n771.79 | \n469.72 | \n
Wine | \n138743.80 | \n224432.02 | \n
Airline | \n380.60 | \n375.25 | \n
Prediction MSE of time series data of TSDL.
From Table 5, it can be confirmed that SGA showed its priority to BP except the cases of Vehicles and Wine. From Table 6, an interesting result of random search for meta-parameter showed that the structures of DBN for different datasets were different, not only the number of units on each layer but also the number of RBMs. In the case of SGA learning method, the number of layer for Sunspots, River flow, and Wine were more than DBN using BP learning.
\nSeries | \nTotal data | \nTesting data | \nDBN with BP | \nDBN with SGA | \n
---|---|---|---|---|
Lynx | \n114 | \n14 | \n19-16-1 | \n7-14-2 | \n
Sunspots | \n288 | \n35 | \n20-18-11-1 | \n10-12-12-17-2 | \n
River flow | \n600 | \n100 | \n20-17-18-1 | \n19-20-5-18-5-2 | \n
Vehicles | \n252 | \n52 | \n20-13-20-1 | \n20-11-5-2 | \n
RGNP | \n85 | \n15 | \n18-20-1 | \n19-15-2 | \n
Wine | \n187 | \n55 | \n16-15-12-1 | \n18-12-13-11-2 | \n
Airline | \n144 | \n12 | \n15-4-1 | \n13-7-2 | \n
Size of time series data and structure of prediction network.
The experiment results showed the DBN composed by multiple RBMs and MLP is the state-of-the-art predictor comparing to all conventional methods in the case of CATS data. Furthermore, the training method for DBN may be more efficient by the RL method SGA for real time series data than using the conventional BP algorithm. Here let us glance back at the development of this useful deep learning method.
Why the DBN composed by multiple RBMs and MLP [11, 13] is better than the DBN with multiple RBMs only [9]?
The output of the last RBM of DBN, a hidden unit of the last RBM in DBN, has a binary value during pretraining process. So the weights of connections between the unit and units of the visible layer of the last RBM are affected and with lower complexity than using multiple units with continuous values, i.e., MLP, or so-called full connections in deep learning architecture.
How are RL methods active at ANN training?
In 1992, Williams proposed to adopt a RL method named REINFORCE to modify artificial neural networks [8]. In 2008, Kuremoto et al. showed the RL method SGA is more efficient than the conventional BP method in the case of time series forecasting [6]. Recently, researchers in DeepMind Ltd. adopted RL into deep neural networks and resulted a famous game software AlphaGo [20, 21, 22, 23].
Why SGA is more efficient than BP?
Generally, the training process for ANN by BP uses mean square error as loss function. So every sample data affects the learning process and results including noise data. Meanwhile, SGA uses reward which may be an error zone to modify the parameters of model. So it has higher robustness for the noisy data and unknown data for real problems.
\nA deep belief net (DBN) composed by multiple restricted Boltzmann machines (RBMs) and multilayer perceptron (MLP) for time series forecasting were introduced in this chapter. The training method of DBN is also discussed as well as a reinforcement learning (RL) method; stochastic gradient ascent (SGA) showed its priority to the conventional error back-propagation (BP) learning method. The robustness of SGA comes from the utilization of relaxed prediction error during the learning process, i.e., different from the BP method which adopts all errors of every sample to modify the model. Additionally, the optimization of the structure of DBN was realized by random search method. Time series forecasting experiments used benchmark CATS data, and real time series datasets showed the effectiveness of the DBN. As for the future work, there are still some problems that need to be solved such as how to design the variable learning rate and reward which influence the learning performance strongly and how to prevent the explosion of characteristic eligibility trace in SGA.
\nIntechOpen's Authorship Policy is based on ICMJE criteria for authorship. An Author, one must:
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