Optimized neural network combined model based on the induced ordered weighted averaging operator for vegetable price forecasting

Abstract

Vegetable price predictions are of great significance to vegetable growers, particularly regarding production and management to ensure a balance in the regional supply and demand of vegetables. In the current paper, in order to improve the accuracy and efficiency of vegetable price forecasting, we propose an optimized neural network combined model based on the induced ordered weighted averaging operator. Our frameworks integrate the fruit fly algorithm (FOA) with the induced ordered weighted averaging (IWOA) operator for an enhanced performance. In particular, the FOA is employed for the parameter optimization of the generalized regression neural network (GRNN) and radial basis function neural network (RBFNN), reducing the adverse influence of man-induced factors in the model construction process and improving the learning ability of both GRNN and RBFNN. The IWOA operator calculates the weights of the single GRNN and RBFNN to address the problem of fixed weights in combination forecasting models. Monthly vegetable price data in Beijing was used to compare our method with nine single forecasting models, revealing that the optimization of the GRNN and RBFNN parameters by the FOA, the prediction accuracy of the FOA-GRNN model and FOA-RBFNN model surpass those of GRNN and RBFNN, respectively. Furthermore, results from four evaluation indexes reveal that the IOWA-based optimized neural network model exhibited a stronger predictive ability than the other nine prediction models. Results demonstrate the effectiveness of our framework for the prediction of vegetable price series, with potential applications in agricultural products of similar characteristics.