Application of improved BP neural network based on e-commerce supply chain network data in the forecast of aquatic product export volume |
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| Institution: | 1. Department of Electronics and Communication Engineering, Sethu Institute of Technology, Madurai 625019, India;2. Department of Electronics and Communication Engineering, VMKV Engineering College, Vinayaka Mission''s Research Foundation, Salem 636308, India;3. Ciddse Technologies Pvt Ltd, Chennai 600087, India;1. Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, India;2. Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India;1. Department of Electronics and Communication, Narayanaguru College of Engineering, Kuzhithurai, Kanyakumari, India;2. Department of Electronics and Communication, St. Xavier’s College of Engineering, Chunkankadai, Kanyakumari, India;1. International Centre for Trade and Sustainable Development, 7 chemin de Balexert 1219 Geneva, Switzerland;2. Fisheries Economics Research Unit, Global Fisheries Cluster, IOF, the University of British Columbia, Vancouver, B.C. V6T 1Z4, Canada;1. Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville QLD 4811, Australia;2. WorldFish, Jalan Batu Maung, Batu Maung, 11960 Bayan Lepas, Penang, Malaysia;3. University of Technology Sydney, Ultimo NSW 2007, Australia;4. Department of Sociology, Peking University, 5 Yiheyuan Road, 100871, Beijing, China |
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| Abstract: | Aiming at the existing problems in the production and export scale prediction of aquaculture, a model of yield prediction based on BP Neural network algorithm is proposed, and a set of algorithms is proposed to optimize BP neural network (BPNN). Based on the traditional BP neural network, it is easy to get into the local optimal problem due to the long training time of the model. By using the simple Johnson algorithm, the dimensionality of the input neuron is reduced, and then the hidden layer neural network is determined by this method. At the same time, the data mining method is used to filter the Data.Particle swarm optimization algorithm is used to optimize the parameters. At the same time, based on the domestic e-commerce Sales network data, the results show that the average square root error of the model is less than the traditional BP neural network and the learning efficiency is higher than the traditional BP neural network. The results show that the model has a great advantage in building up a large number of historical data, and it can shorten the modeling time and get good prediction result by combining the sales data of e-commerce. It provides a new feasible method for the export prediction of aquatic products. |
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| Keywords: | Export scale Neural network Electronic commerce Predictive model |
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