Unconstrained Minimization Methods for Solving Training Problems Arising in Neural Net Applications

解决神经网络应用中出现的训练问题的无约束最小化方法

• 批准号: 9000560
• 负责人: James Falk
• 金额: $ 3万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-01-15 至 1991-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9000560&HistoricalAwards=false
• 关键词: Unconstrained; Minimization; Methods; Solving; Training

The P.I. will attempt to apply current methodology from nonlinear programming to the solution of unconstrained optimization problems arising in applications of neural nets. The four main areas of investigation are: solving large problems using recent developments in conjugate gradient and Newton methods; development of methods for obtaining global as opposed to strictly local solutions; application of sensitivity theory to provide post-optimality analysis; use of factorable function theory to develop varieties of functional forms describing the input-output relations.

P.I.将尝试将当前的非线性规划方法应用于解决神经网络应用中出现的无约束优化问题。研究的四个主要领域是：利用共轭梯度和牛顿方法的最新发展来解决大问题；发展获得全局解而不是严格局部解的方法；运用敏感性理论提供后最优性分析；利用可因式函数理论发展各种描述投入产出关系的函数形式。