Computer aided investigations of artificial neural systems

D. Wang, B. Schurmann
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引用次数: 1

Abstract

An attempt is made to demonstrate how symbolic computation can be applied to aid in the analysis and derivation of neural systems. The authors review the general method and techniques of the Lyapunov method for the stability analysis of artificial neural systems. They present some strategies for using computer algebra systems and their extensions to analyze the stability of known neural systems and to derive novel stable ones. A brief description of a toolkit developed in MACSYMA is also provided. An illustration is given to sketch the derivation of neural learning dynamics by the toolkit. A discussion of future developments is included.<>
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人工神经系统的计算机辅助研究
本文试图演示符号计算如何应用于神经系统的分析和推导。综述了用于人工神经系统稳定性分析的李雅普诺夫方法的一般方法和技术。他们提出了一些利用计算机代数系统及其扩展来分析已知神经系统的稳定性并推导新的稳定系统的策略。还提供了在MACSYMA中开发的工具包的简要描述。用实例说明了该工具箱对神经学习动力学的推导过程。包括对未来发展的讨论。
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Control of a robotic manipulating arm by a neural network simulation of the human cerebral and cerebellar cortical processes Neural network training using homotopy continuation methods A learning scheme of neural networks which improves accuracy and speed of convergence using redundant and diversified network structures The abilities of neural networks to abstract and to use abstractions Backpropagation based on the logarithmic error function and elimination of local minima
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