Predicting the Yield of Spring Maize Based on an Optimized Wavelet Neural Network with an Improved Double-Chain Quantum Genetic Algorithm

Abstract

To overcome the shortcomings of the traditional wavelet neural network, an improved double-chain quantum genetic algorithm is used to optimize its parameters. This paper presents a prediction model for the optimized wavelet neural network that is applied to spring maize yields in the northeast of China. The results show that the coupled model is better than the traditional wavelet neural network, and achieves good prediction performance for the spring maize yield.