Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
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发表时间：
2001-08
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通讯作者：
D. Mandic;J. Chambers
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作者：
D. Mandic;J. Chambers
来自出版商：从移动的通信到机器人技术，再到空间技术和医疗仪器，新技术对数字信号处理（DSP）方法的要求越来越高。本书向研究人员展示了如何实现递归神经网络，以扩展传统信号处理技术的范围。本书以神经网络稳定性的原创性研究为特色，结合了严格的数学分析和应用实例。实验证据以及现有的方法的概述也包括在内。市场：工程师在信号处理，神经网络，通信，非线性控制和时间序列分析工作。
From the Publisher: From mobile communications to robotics to space technology to medical instrumentation, new technologies are demanding increasingly complex methods of digital signal processing (DSP). This book shows researchers how recurrent neural networks can be implemented to expand the range of traditional signal processing techniques. Featuring original research on stability in neural networks, the book combines rigorous mathematical analysis with application examples. Experimental evidence as well as an overview of existing approaches are also included. Market: Engineers working in signal processing, neural networks, communications, nonlinear control, and time series analysis.