A Combination Forecasting Model of Extreme Learning Machine Based on Genetic Algorithm Optimization

Abstract

After studying the working principle of feed-forward neural network and analyzing network structure and the learning mechanism of BP neural network and the extreme learning machine (ELM), a prediction model, GA-ELM, is proposed based on genetic algorithm to optimize the learning machine limit. The genetic algorithm is used to select the weights and thresholds of ELM neural network, and the optimal weights and thresholds are used to determine the connection weights between the hidden layer and the output layer. Further, this model is combined with the grey system model to correct the residual of GM, and then GM-GA-ELM combination forecasting model is established. Compared with BP model, GA-BP model and standard ELM model, it is further verified that the predicting accuracy and running time of the proposed model are better.