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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, X
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.

History

Publication title

Proceedings of the 2011 Second International Conference on Digital Manufacturing & Automation

Editors

Min Chen, Han QingJue & YuCai Zhou

Pagination

373-376

ISBN

978-0-7695-4455-7

Department/School

School of Information and Communication Technology

Publisher

IEEE Computer Society

Place of publication

Piscataway, NJ, USA

Event title

2011 Second International Conference on Digital Manufacturing & Automation

Event Venue

Zhangjiajie, Hunan, China

Date of Event (Start Date)

2011-08-05

Date of Event (End Date)

2011-08-07

Rights statement

Copyright 2011 IEEE

Repository Status

  • Restricted

Socio-economic Objectives

Electronic information storage and retrieval services

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