A Stochastic, Fuzzy, Neural Network for Unknown Dynamic Systems Available to Purchase

A stochastic, fuzzy, neural network (SFNN) to model unknown nonlinear dynamic systems is developed. In this network, a non-singleton fuzzifier with Gaussian membership functions instead of the singleton fuzzifier in the fuzzy logic is introduced. Based on these membership functions, an online supervised parameter learning algorithm of the SFNN is proposed to overcome the local minimum of learning process in current neural networks, and an off-line algorithm for the structure learning of the SFNN is presented to reduce the amount of computation of the SFNN. This new network provides a universal approximator and it is also applicable to stochastic control and decision systems and the identification of chaos in nonlinear systems.