A self-organizing RBF neural network based on distance concentration immune algorithm

Radial basis function neural network &#x0028 RBFNN &#x0029 is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm &#x0028 DCIA &#x0029 is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly, the information processing strength &#x0028 IPS &#x0029 algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm &#x0028 DCIA-SORBFNN &#x0029 is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIA-SORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.