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Design and Implementation of Fishery Forecasting System Based on Radial Basis Function Neural Network
conference contribution
posted on 2023-05-23, 06:38 authored by Yuan, H, Wang, J, Chen, Y, Chen, XThis article introduces the design and implementation of a fishery forecasting system based on Radial Basis Function (RBF) neural network. The system was developed using the Client/Server architecture, the C# programming language in the environment of Visual Studio 2008 on the Windows7 platform. It draws knowledge from RBF neural network theory, the production historical data of pelagic fishery and the marine environment data. The system uses the Object-Oriented analysis and design method. It can quickly obtain the forecast results available to users through inputting marine environment data information and the RBF neural network model. The forecasting system includes three major functional modules, namely preprocessing fishery production data, matching production data and environmental data, training RBF neural network and making predictions. Experiments have shown that this forecasting system can generate accurate and effective pelagic fishery knowledge.
History
Publication title
Proceedings of the 2011 Second International Conference on Digital Manufacturing & AutomationEditors
Min Chen, Han QingJue & YuCai ZhouPagination
373-376ISBN
978-0-7695-4455-7Department/School
School of Information and Communication TechnologyPublisher
IEEE Computer SocietyPlace of publication
Piscataway, NJ, USAEvent title
2011 Second International Conference on Digital Manufacturing & AutomationEvent Venue
Zhangjiajie, Hunan, ChinaDate of Event (Start Date)
2011-08-05Date of Event (End Date)
2011-08-07Rights statement
Copyright 2011 IEEERepository Status
- Restricted