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Design and Implementation of Fishery Forecasting System Based on Radial Basis Function Neural Network


Yuan, H and Wang, J and Chen, Y and Chen, X, Design and Implementation of Fishery Forecasting System Based on Radial Basis Function Neural Network, Proceedings of the 2011 Second International Conference on Digital Manufacturing & Automation, 5-7 August 2011, Zhangjiajie, Hunan, China, pp. 373-376. ISBN 978-0-7695-4455-7 (2011) [Refereed Conference Paper]

Copyright Statement

Copyright 2011 IEEE

DOI: doi:10.1109/ICDMA.2011.98


This 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.

Item Details

Item Type:Refereed Conference Paper
Keywords:Radial Basis Function; Fishery Forecasting; System Design
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Information and Communication Services
Objective Group:Information services
Objective Field:Electronic information storage and retrieval services
UTAS Author:Chen, Y (Ms Ying Chen)
ID Code:76844
Year Published:2011
Deposited By:Information and Communication Technology
Deposited On:2012-03-14
Last Modified:2014-12-09

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