A novel fuzzy based algorithm for radial basis function neural network

An endeavour is made to propose a novel fuzzy variation of the learning parameter in order to improve the performance of the least mean square (LMS) algorithm in connection with the radial basis function neural networks (RBFNN). The error and the change in error are the parameters based on which the learning parameter is modified using the fuzzy principles. The learning parameter is allowed to change in a fixed range only. This modified algorithm is used for a nonlinear system identification problem. It is clearly shown by test examples that the performance of this new method is not only better than the LMS algorithm but also better than the normalised least mean square (NLMS) algorithm which is certainly superior to LMS. Further this does not require extra computation which is an unavoidable feature of NLMS. The convergence during learning is almost similar to that of NLMS. Thus it has been clearly established that this algorithm is most certainly superior to the existing ones in terms of the most desirable characteristics of capturing the plant characteristics and lower computational requirements