Equalizer Using Evolutionary Programming to Optimize the Structure of the Radial Basis Function Neural Network

Stochastic gradient (SG) algorithm is used for training the radial basis function (RBF)neural network equalizer. The structure of the neural network is appointed at first, and the training samples used appeared too long. To solve the problem, the evolutionary programming method is introduced to find out the neural network's structure, and the adaptive algorithm based on the least-mean-square(LMS) error criterion is used to adjust the linking weights from the neurons to the output. Monte-Carlo simulations demonstrate that the performance of the proposed algorithm is the same as that of the SG algorithm, the training samples used become much shorter, and the structure of the network need not to be appointed beforehand.