Adaptive system identification using multilayer neural networks and Gaussian potential function networks

This paper deals with the characteristics of multilayer neural networks and radial basis function networks, and provides their hybridization by considering their advantages and disadvantages. The hybrid networks show their effectiveness in system identification as well as alleviate problems of error backpropagation algorithm such as local minima, slow speed, and size of structure by adopting other networks effectively. Potential performance improvement is demonstrated by computer simulation for two general problems of identification: static and dynamical system identification.