AGA-LSTM: An Optimized LSTM Neural Network Model Based on Adaptive Genetic Algorithm

Abstract

With the increase of the hidden layer, the weight update of the LSTM neural network model depends heavily on the gradient descent algorithm, and the convergence speed is slow, resulting in the local extremum of the weight adjustment, which affects the prediction performance of the model. Based on this, this paper proposes an optimized LSTM neural network model based on adaptive genetic algorithm (AGA-LSTM). In this model, the mean squared error is designed as the fitness function, and the adaptive genetic algorithm (AGA) is used to globally optimize the weights between neuron nodes of the LSTM model to improve the generalization ability. The experimental results show that, on the UCI dataset, the prediction accuracy of the AGA-LSTM model is greatly improved compared to the standard LSTM model, which verifies the rationality of the model.