Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO

• Optimization of type-2 fuzzy inference systems using GAs and PSO are presented.
• Optimized type-2 fuzzy systems are used to estimate the type-2 fuzzy weights.
• Simulation results and a comparative study are presented to illustrate the method.
• Bio-inspired optimization of the type-2 fuzzy systems is viable for this problem.

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.

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