Optimization of BP neural network model based on genetic algorithm in nonlinear prediction

In the application research of nonlinear prediction, BP neural network prediction model with simple structure and powerful function has been widely used. About the local optimization research, improving BP neural network with intelligent optimization algorithm is a valuable research direction. Some intelligent optimization algorithms are sensitive to parameter Settings and highly dependent on information. The GA-BP neural network model is constructed by using genetic optimization algorithm to simulate the reproduction process of biological population. The parameters in the network model are regarded as genes, and the parameters with the best fitness are selected according to the rule of survival of the fittest through the operation of selection, crossover and mutation, and then the model parameters are retrained by BP neural network using the method of error gradient descent, and the optimal model is finally obtained. The GA-BP neural network addresses the vulnerability of standalone BP neural networks to converging into local optima, while simultaneously addressing the sluggishness or stagnation issues that can arise during the later stages of genetic optimization algorithm training, resulting in an overall improved performance.