Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
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发表时间：
2000-09
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通讯作者：
F. Ham;I. Kostanic
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作者：
F. Ham;I. Kostanic
这本令人兴奋的新书涵盖了人工神经网络，但更具体地说，是神经计算。神经计算关注的是信息处理，它涉及人工神经网络架构中的学习过程。这种神经结构根据定义的学习规则响应输入，然后训练后的网络可以根据应用程序执行某些任务。神经计算可以在解决模式识别、优化、事件分类、非线性系统的控制和识别以及统计分析等问题中发挥重要作用。与其他神经网络教材不同，《科学与工程神经计算原理》是专门为想要应用神经网络解决复杂问题的科学家和工程师编写的。对于每个神经计算概念，都提供了坚实的数学基础以及伴随特定架构和相关训练算法的说明性示例。本书主要用于研究生水平的神经网络课程，但在某些情况下可能用于本科水平。这本书包括许多详细的例子和一套广泛的章末问题。
From the Publisher: This exciting new text covers artificial neural networks,but more specifically,neurocomputing. Neurocomputing is concerned with processing information,which involves a learning process within an artificial neural network architecture. This neural architecture responds to inputs according to a defined learning rule and then the trained network can be used to perform certain tasks depending on the application. Neurocomputing can play an important role in solving certain problems such as pattern recognition,optimization,event classification,control and identification of nonlinear systems,and statistical analysis. "Principles of Neurocomputing for Science and Engineering," unlike other neural networks texts,is written specifically for scientists and engineers who want to apply neural networks to solve complex problems. For each neurocomputing concept,a solid mathematical foundation is presented along with illustrative examples to accompany that particular architecture and associated training algorithm. The book is primarily intended for graduate-level neural networks courses,but in some instances may be used at the undergraduate level. The book includes many detailed examples and an extensive set of end-of-chapter problems.