Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO |
| |
Affiliation: | 1. Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran;2. Department of Hydrology and Water Resources, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran;3. Department of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran |
| |
Abstract: | In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms (GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy inference systems are used to estimate the type-2 fuzzy weights of backpropagation neural networks. Simulation results and a comparative study among neural networks with type-2 fuzzy weights without optimization of the type-2 fuzzy inference systems, neural networks with optimized type-2 fuzzy weights using genetic algorithms, and neural networks with optimized type-2 fuzzy weights using particle swarm optimization are presented to illustrate the advantages of the bio-inspired methods. The comparative study is based on a benchmark case of prediction, which is the Mackey-Glass time series (for τ = 17) problem. |
| |
Keywords: | Type-2 fuzzy logic Neural networks Time series prediction Genetic algorithm Particle swarm optimization |
本文献已被 ScienceDirect 等数据库收录! |
|