Learning algorithm for constructing fuzzy neural networks with application to regression problems

In this paper, we present a new learning algorithm for self-constructing fuzzy neural networks (FNN). First, an initial network starts with no hidden neurons and grows neurons based on the growth criteria. After the generation process, a neuron pruning algorithm based on optimal brain surgeon (OBS) is employed to reduce the size of the FNN. After the structure design process, weight adjustment method is adopted to tune all the consequent parameters. Applications to regression problems are carried out. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.