An improved neural network algorithm was proposed and applied to the inverse radiative problems. A multi-strategy particle swarm optimization was applied to improve the performance of the back propagation multi-layer feed-forward neural network algorithm. Three commonly used particle size distribution (PSD) functions in a one-dimensional particle system were retrieved using the proposed algorithm. In addition, the optical constant was also estimated, and the measurement errors were considered. Results show that the proposed algorithm can be applied to the retrieval of PSDs and optical constant even with measurement errors. Finally, the proposed algorithm was applied to the simultaneous estimation of the PSDs and optical constant using the multi-wavelength and multi-thickness method.
Part of the book: New Applications of Artificial Intelligence
Three different Particle Swarm Optimization (PSO) algorithms—standard PSO, stochastic PSO (SPSO) and differential evolution PSO (DEPSO)—are applied to solve the inverse geometry design problems of radiative enclosures. The design purpose is to satisfy a uniform distribution of radiative heat flux on the designed surface. The design surface is discretized into a series of control points, the PSO algorithms are used to optimize the locations of these points and the Akima cubic interpolation is utilized to approximate the changing boundary shape. The retrieval results show that PSO algorithms can be successfully applied to solve inverse geometry design problems and SPSO achieves the best performance on computational time. The influences of the number of control points and the radiative properties of the media on the retrieval geometry design results are also investigated.
Part of the book: Optimization Algorithms