Adaptive training of Radial Basis Function Networks based on cooperative evolution and evolutionary programming

Neuro-fuzzy systems based on Radial Basis Function Networks (RBFN) and other hybrid artificial intelligence techniques are currently under intensive investigation. This paper presents a RBFN training algorithm based on evolutionary programming and cooperative evolution. The algorithm alternatively applies basis function adaptation and backpropagation training until a satisfactory error is achieved. The basis functions are adjusted through an error goal function obtained through training and testing of the second part of the network. The algorithm is tested on bench-mark data,sets. It is applicable to on-line adaptation of RBFN and building adaptive intelligent systems.