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Market revenue prediction and error analysis of products based on fuzzy logic and artificial intelligence algorithms

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Abstract

Neural networks can approximate the neuron information of all quantitative or qualitative nonlinear relationship, any complex is stored in the potential distribution in the network. It has strong robustness and fault tolerance, using the parallel distribution processing method, making quick lots of computing is possible. The mature prediction method in artificial intelligence, or the neural network method to forecast, this kind of algorithm has theoretical support to mature, reliable predictions of the information. In this paper, the authors analyze the market revenue prediction and error analysis of products based on fuzzy logic and artificial intelligence algorithms. The results of this paper can be concluded that the neural network algorithm has a high accuracy in predicting the future sales of the product, and the prediction error can be controlled within 4%. Through the establishment of the neural network model of future product sales forecast, we can predict the future product sales, can grasp the market direction, and make the enterprise get the maximum profit.

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Acknowledgements

Heilongjiang Province Philosophical and Social Sciences Research Planning Project (18JYB138): Identification and Optimization Mechanism of Key Obstacles in The Green Food Industry Chain in Heilongjiang Province.

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Authors and Affiliations

  1. College of Economics and Management, Northeast Agricultural University, Harbin, 150030, China

    Zhao Jian, Zhang Qingyuan & Tian Liying

Authors
  1. Zhao Jian
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  2. Zhang Qingyuan
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  3. Tian Liying
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Correspondence to Zhang Qingyuan.

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Jian, Z., Qingyuan, Z. & Liying, T. Market revenue prediction and error analysis of products based on fuzzy logic and artificial intelligence algorithms. J Ambient Intell Human Comput 11, 4011–4018 (2020). https://doi.org/10.1007/s12652-019-01650-2

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  • DOI: https://doi.org/10.1007/s12652-019-01650-2

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