Neural network implementation of a new fuzzy system
Chu Kwong Chak, Gang Feng · 1994
The architecture and learning scheme of a new fuzzy logic system implemented in the framework of neural network is proposed. The proposed network can construct its rules and optimise its membership functions by training data pairs. Both back error propagation and least squares estimation are applied to the learning scheme. The convergence of training is expected to be faster since the least squares estimation is applied to the estimation of the consequence parameters of the system and backpropagation is applied only to the estimation of premise parameters. Due to new architecture, even a high order fuzzy system can be implemented with this learning scheme. In our simulation, the proposed network is employed to model nonlinear functions.>