To purchase hard copies of this book, please email:
orders@intechopen.com
By only printing on demand InTech ensures our carbon footprint is kept to a minimum.
The data below shows the environmental impact of printing one single book:
57.72 kg wood
3.1 g CO2
52.41 ml Water
Share this page
Recurrent Neural Networks
Edited by Xiaolin Hu and P. Balasubramaniam, ISBN 978-953-7619-08-4, Hard cover, 400 pages, Publisher: InTech, Published: September 01, 2008 under CC BY-NC-SA 3.0 license, in subject Numerical Analysis and Scientific Computing
DOI: 10.5772/68
The concept of neural network originated from neuroscience, and one of its primitive aims is to help us understand the principle of the central nerve system and related behaviors through mathematical modeling. The first part of the book is a collection of three contributions dedicated to this aim. The second part of the book consists of seven chapters, all of which are about system identification and control. The third part of the book is composed of Chapter 11 and Chapter 12, where two interesting RNNs are discussed, respectively.The fourth part of the book comprises four chapters focusing on optimization problems. Doing optimization in a way like the central nerve systems of advanced animals including humans is promising from some viewpoints.
This book is indexed in:
Book contents
- Chapter 1Aperiodic (Chaotic) Behavior in RNN with Homeostasis as a Source of Behavior Novelty: Theory and Applications
- Chapter 2Biological Signals Identification by a Dynamic Recurrent Neural Network: from Oculomotor Neural Integrator to Complex Human Movements and Locomotion
- Chapter 3Linguistic Productivity and Recurrent Neural Networks
- Chapter 4Recurrent Neural Network Identification and Adaptive Neural Control of Hydrocarbon Biodegradation Processes
- Chapter 5Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control
- Chapter 6Recurrent Fuzzy Neural Networks and Their Performance Analysis
- Chapter 7Recurrent Interval Type-2 Fuzzy Neural Network Using Asymmetric Membership Functions
- Chapter 8Rollover Control in Heavy Vehicles via Recurrent High Order Neural Networks
- Chapter 9A New Supervised Learning Algorithm of Recurrent Neural Networks and L2 Stability Analysis in Discrete-Time Domain
- Chapter 10Application of Recurrent Neural Networks to Rainfall-runoff Processes
- Chapter 11Recurrent Neural Approach for Solving Several Types of Optimization Problems
- Chapter 12Applications of Recurrent Neural Networks to Optimization Problems
- Chapter 13Neurodynamic Optimization: towards Nonconvexity
- Chapter 14An Improved Extremum Seeking Algorithm Based on the Chaotic Annealing Recurrent Neural Network and Its Application
- Chapter 15Stability Results for Uncertain Stochastic High-Order Hopfield Neural Networks with Time Varying Delays
- Chapter 16Dynamics of Two-Dimensional Discrete-Time Delayed Hopfield Neural Networks
- Chapter 17Case Studies for Applications of Elman Recurrent Neural Networks
- Chapter 18Partially Connected Locally Recurrent Probabilistic Neural Networks