Fish Return Forecasting with Neural Networks

使用神经网络预测鱼类回归

• 批准号: 9461197
• 负责人: Patrick Simpson
• 金额: $ 7.49万
• 依托单位: Scientific Fishery Systems, Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-02-01 至 1996-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9461197&HistoricalAwards=false
• 关键词: Fish; Return; Forecasting; Neural; Networks

The use of neural networks for forecasting sockeye salmon Bristol Bay returns is proposed. In Phase I, this project will collect and characterize available data sources, train a neural network to forecast the sockeye salmon run, and compare the performance of the resulting system against existing techniques. Because neural networks are able to automatically learn nonlinear mappings from diverse data types, it is expected that neural networks will produce more accurate forecasts. In Phase II this project will expand its scope to include fish population modeling.

利用神经网络预测红鲑布里斯托湾回报率。在第一阶段，该项目将收集和表征可用的数据源，训练神经网络来预测红鲑鱼的运行，并将所产生的系统的性能与现有技术进行比较。由于神经网络能够从不同的数据类型中自动学习非线性映射，因此预计神经网络将产生更准确的预测。在第二阶段，该项目将扩大其范围，包括鱼类种群建模。