Connectionist Learning Algorithms for Temporal Processing and Multi-Scale Search

用于时间处理和多尺度搜索的联结学习算法

• 批准号: 8921275
• 负责人: Ronald Williams
• 金额: $ 19.77万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-08-01 至 1994-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8921275&HistoricalAwards=false
• 关键词: Connectionist; Learning; Algorithms; Temporal; Processing

This grant will support Dr. Williams' theoretical and experimental work in two important areas of artificial neural computation: recursive networks for processing time-varying signals, and multiscale networks for exploiting information at multiple resolutions. Recursive networks will be important for control and signal-processing problems with feedback delays of unknown duration. Multiscale architectures and learning strategies are needed for large-scale problems of practical importance, including those with complex spatial and temporal components. Proposed target applications are in speech recognition and adaptive sensorimotor control.

This grant will support Dr. Williams' theoretical and experimental work in two important areas of artificial neural computation: recursive networks for processing time-varying signals, and multiscale networks for exploiting information at multiple resolutions. Recursive networks will be important for control and signal-processing problems with feedback delays of unknown duration. Multiscale architectures and learning strategies are needed for large-scale problems of practical importance, including those with complex spatial and temporal components. 提出的目标应用是语音识别和自适应感觉运动控制。