Agricultural Information Service Quality Evaluation Algorithm Based on Genetic Algorithm, BP Neural Network and Multiple Regressions

Abstract:

Information service objects in agriculture relatively have a complex demand due to agricultural regional and seasonal. The construction of information service quality evaluation model contributes to analyze the influencing factors that influence the quality of information service, proving guidance for agricultural information service. Combined with genetic Algorithm, BP neural network and multiple regression, a hybrid BP network based on the integration of BP Network and multiple regression models is proposed, and the initial weights of hybrid BP network is optimized by hybrid genetic algorithm, effectively avoid the flaws when these methods used separately. Proved by the experiment, information service quality evaluation model constructed by a hybrid BP network based on the optimization of genetic Algorithm has a good accuracy and generalization ability, the mean error within 5%.

• Periodical: Applied Mechanics and Materials (Volumes 433-435)
• Pages: 713-719
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.433-435.713
• Online since: October 2013
• Authors: Cheng Chen, Hua Rui Wu*
• Keywords: Agricultural Information Service, BP Neural Network (BPNN), Genetic Algorithm (GA), Multiple Regressions, Quality Evaluation Algorithm

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