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

机译:基于径向基函数神经网络的渔业预报系统的设计与实现

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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.
机译:本文介绍了基于径向基函数(RBF)神经网络的渔业预报系统的设计和实现。该系统是使用CLient / Server体系结构(Windows 7平台上的Visual Studio 2008环境中的C#编程语言)开发的。它借鉴了RBF神经网络理论,远洋渔业的生产历史数据和海洋环境数据的知识。该系统使用面向对象的分析和设计方法。通过输入海洋环境数据信息和RBF神经网络模型,可以快速获得可供用户使用的预测结果。预测系统包括三个主要功能模块,即预处理渔业生产数据,匹配生产数据和环境数据,训练RBF神经网络并进行预测。实验表明,该预报系统可以产生准确有效的中上层渔业知识。

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