Abstract
The study established a BP neural network prediction model to test the effect of the application to predict the food safety Index. The GA was used to optimize the weights and thresholds of BP neural network. The theoretical analysis and experimental results prove that the BP neural network prediction is feasible for the food safety Index. The index prediction has some value in the field of food index forecast.
Highlights
With the economic development and transformation of people's sense about investing, the food index is a product of the market economy
Artificial intelligence techniques of genetic algorithms, artificial neural network and supporting vector machine methods are applied to short-term prediction of the food market by many scholars
Experiments and analysis: In order to verify the BP neural network optimized by Genetic Algorithms (GA) which is feasible and effective in food prediction, comparing the performance of the method proposed in this study with the existing approaches, such as BP neural network and BP neural network optimized by GA with conducting the same experiment (Takens, 1981)
Summary
With the economic development and transformation of people's sense about investing, the food index is a product of the market economy. The food price is determined by its value, but influenced by economic, political and social many factors. Existing research indicates that intelligent forecasting models outperform traditional models, especially in short-term forecasting. Artificial intelligence techniques of genetic algorithms, artificial neural network and supporting vector machine methods are applied to short-term prediction of the food market by many scholars. Guresen et al (2011) evaluated the effectiveness of neural network models which were known to be dynamic and effective in food-market prediction. Hassan et al (2012) proposed a fusion model by combining the Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to predict food price. Shen et al (2003) used the Artificial Fish Swarm Algorithm (AFSA) to optimize RBF to forecast food indices. Hassan et al (2012) proposed a fusion model by combining the Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to predict food price. Shen et al (2003) used the Artificial Fish Swarm Algorithm (AFSA) to optimize RBF to forecast food indices. Armano et al (2005) optimized ANN with GA to forecast food indices. Lee (2009)
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