Optimization of BP neural network model by chaotic krill herd algorithm

Taking kidney bean as the research object, row spacing, fertilizer application and planting density were selected as experimental factors, production for the response indicators, the chaos theory, krill herd algorithm is introduced into the BP neural network, the minimum error in training as a target, the model of weight and threshold as variables to optimize the BP neural network and chaotic krill herd algorithm BP neural network prediction model was set up (C-KHA-BP). The RMSE of C-KHA-BP model is 191.93 kg/hm(2)?MAE is 153.18 kg/hm(2), and MAPE is 12.67%, the correlation coefficient R-2 is 0.95.By solving the global optimal solution of C-KHA-BP model, the optimal row spacing of kidney bean was 72.63 cm, the fertilizer application rate was 103.91 kg/hm(2), and the planting density was 30 x 10(4) plants /hm(2). The next year, the validation test was conducted in the same test area, and the yield of kidney bean under the test scheme was 2843.2 kg /hm(2), the relative error between the test result and the simulation optimization result (2949.5 kg /hm(2)) was only-3.65%, indicating that the fitting function of C-KHA-BP prediction model was precision and the optimization result was accurate. The results of this study can provide a new approach to the prediction and optimization of similar models in the field of grain production. (C) 2022 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

Automated summary

This study focuses on the research of kidney beans and explores the optimization of the BP neural network model using chaos theory and krill herd algorithm. The C-KHA-BP model shows high accuracy in predicting the yield of kidney beans, and the results provide a new approach for similar models in the field of grain production.