Forecasting Fish Stock Recruitment and Planning Optimal Harvesting Strategies by Using Neural Network

作者：Lin Sun, Hongjun Xiao, Shouju Li, Dequan Yang · 发表于：Journal of Computers · 年份：2009 · DOI：10.4304/jcp.4.11.1075-1082 · 被引用次数：19 · 研究领域：Water Quality Monitoring Technologies、Marine and fisheries research、Machine Learning and ELM

Abstract—Recruitment prediction is a key element for management decisions in many fisheries. A new approach using neural network is developed as a tool to produce a formula for forecasting fish stock recruitment. In order to deal with the local minimum problem in training neural network with back-propagation algorithm and to enhance forecasting precision, neural network’s weights are adjusted by optimization algorithm. It is demonstrated that a well trained artificial neural network reveals an extremely fast convergence and a high degree of accuracy in the prediction of fish stock recruitment. Index Terms—neural network, prediction of fish stock recruitment, optimal harvesting strategy, management decision I.