In this paper

the soft-competition learning algorithms (SCLA) of RBF neural networks are designed.The main ideas of the algorithms are:firstly

membership functions are introduced into the training procedure of the center vectors of the Gaussian basis functions

and for each input sample

all center vectors are self-adaptively adjusted according to the values of the membership functions

in what degree the sample belongs to the classes that the center vectors represent;secondly

the reciprocal of the fuzzy factor of membership function are considered as the temperature of the simulated annealing algorithm

and increasingly adjusting method is used to the fuzzy factor during the learning procedure.SCLA are soft-competition schemes of the learning algorithms

in which the center vectors are trained based on the

k

-means algorithm

and can reme

dy the problems of the dead-node and the sensitivity to initial weight vectors that the latter algorithms have.The simulation results show that SCLA are efficient.