Finding the near optimal learning rates of Fuzzy Neural Networks (FNNs) via its equivalent fully connected neural networks (FFNNs)
Jing Wang, C. L. Philip Chen, Chi‐Hsu Wang · 2012
In this paper, Fuzzy Neural Network (FNN) is transformed into an equivalent fully connected three layer neural network, or FFNN. Based on the FFNN, BP training algorithm is derived. To improve convergent rate, a new method to find near optimal learning rates for FFNN is proposed. Illustrative examples are presented to check the validity of the proposed theory and algorithms. Simulation results show satisfactory results. Finding near optimal learning rates for FNN via its equivalent FFNN has its emerging values in all engineering applications using FNN, such as intelligent adaptive control, pattern recognition, and signal processing,..., etc.