An RBF neural network based on improved black widow optimization algorithm for classification and regression problems

Abstract

IntroductionRegression and classification are two of the most fundamental and significant areas of machine learning.MethodsIn this paper, a radial basis function neural network (RBFNN) based on an improved black widow optimization algorithm (IBWO) has been developed, which is called the IBWO-RBF model. In order to enhance the generalization ability of the IBWO-RBF neural network, the algorithm is designed with nonlinear time-varying inertia weight.DiscussionSeveral classification and regression problems are utilized to verify the performance of the IBWO-RBF model. In the first stage, the proposed model is applied to UCI dataset classification, nonlinear function approximation, and nonlinear system identification; in the second stage, the model solves the practical problem of power load prediction.ResultsCompared with other existing models, the experiments show that the proposed IBWO-RBF model achieves both accuracy and parsimony in various classification and regression problems.

Research on Downstream Safety Risk Warning Model for Small Reservoirs Based on Granger Probabilistic Radial Basis Function Neural Network

“…In the equation, p a , p b , and p c are the lag times of each effective influencing factor on the water level, which can be obtained from the analysis results of the Granger test. The nonlinear function Y(t) can be fitted using neural network training functions, and the predicted water level of small reservoirs can be achieved through the fitted functions [32].…”