Connectionist Learning Algorithms for Temporal Processing and Multi-Scale Search

这笔赠款将支持威廉姆斯博士的理论和 人工神经网络的两个重要领域的实验工作 计算：用于处理时变的递归网络 信号和利用信息的多尺度网络. 多个决议。 递归网络将是重要的， 反馈延迟的控制和信号处理问题 未知的持续时间 多尺度架构和学习 战略是需要大规模的实际问题， 重要性，包括那些具有复杂的空间和时间 件. 建议的目标应用是语音 识别和自适应感觉运动控制。

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.