Издательство Academic Press, 1998, -407 pp.Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple processing units, i.e., neurons, via mutual interconnection. It learns to solve problems by adequately adjusting the strength of the interconnections according to input data. Moreover, the neural network adapts easily to new environments by learning, and can deal with information that is noisy, inconsistent, vague, or probabilistic. These features have motivated extensive research and developments in artificial neural networks. This volume is probably the first rather comprehensive treatment devoted to the broad areas of algorithms and architectures for the realization of neural network systems. Techniques and diverse methods in numerous areas of this broad subject are presented. In addition, various major neural network structures for achieving effective systems are presented and illustrated by examples in all cases. Numerous other techniques and subjects related to this broadly significant area are treated.
The remarkable breadth and depth of the advances in neural network systems with their many substantive applications, both realized and yet to be realized, make it quite evident that adequate treatment of this broad area requires a number of distinctly titled but well-integrated volumes. This is the fifth of seven volumes on the subject of neural network systems and it is entitled Image Processing and Pattern Recognition. The entire set of seven volumes contains
Algorithms and Architectures ( /file/1517411/ or /file/261251/ )
Optimization Techniques ( /file/664172/ )
Implementation Techniques ( /file/664174/ )
Industrial and Manufacturing Systems (absent)
Image Processing and Pattern Recognition ( /file/664149/ )
Fuzzy Logic and Expert Systems Applications ( /file/664164/ )
Control and Dynamic Systems ( /file/664176/ )Pattern Recognition
Comparison of Statistical and Neural Classifiers and Their Applications to Optical Character Recognition and Speech Classification
Medical Imaging
Paper Currency Recognition
Neural Network Classification Reliability: Problems and Applications
Parallel Analog Image Processing: Solving Regularization Problems with Architecture Inspired by the Vertebrate Retinal Circuit
Algorithmic Techniques and Their Applications
Learning Algorithms and Applications of Principal Component Analysis

Learning Evaluation and Pruning Techniques

Издательство Academic Press, 1998, -407 pp.受人脑结构的启发，人工神经网络已被广泛应用于模式识别、优化、编码、控制等领域，因为它们能够通过直接从数据中学习来解决繁琐或棘手的问题。人工神经网络通常由大量的简单处理单元，即神经元，通过相互连接组成。它通过根据输入数据充分调整相互连接的强度来学习解决问题。此外，神经网络通过学习很容易适应新的环境，并能处理有噪声、不一致、模糊或概率性的信息。这些特点促使人们对人工神经网络进行广泛的研究和开发。本卷可能是第一个相当全面的处理方法，专门用于实现神经网络系统的算法和架构的广泛领域。本书介绍了这一广泛主题的众多领域的技术和各种方法。此外，还介绍了用于实现有效系统的各种主要神经网络结构，并在所有情况下通过实例加以说明。与这个广泛的重要领域有关的许多其他技术和主题也得到了处理。

神经网络系统进展的显著广度和深度及其许多实质性的应用，包括已实现的和尚未实现的，使之相当明显，对这一广泛领域的充分处理需要一些有明显标题但很好地整合的卷。本书是关于神经网络系统主题的七卷中的第五卷，题为《图像处理和模式识别》。整套七卷本包括

算法和架构 ( /file/1517411/ 或 /file/261251/ )

优化技术 ( /file/664172/ )

实施技术 ( /file/664174/ )

工业和制造系统 (缺席)

图像处理和模式识别 ( /file/664149/ )

模糊逻辑和专家系统应用 ( /file/664164/ )

控制和动态系统 ( /file/664176/ ) 模式识别

统计和神经分类器的比较及其在光学字符识别和语音分类中的应用

医学成像

纸币识别

神经网络分类的可靠性。问题与应用

平行模拟图像处理。用受脊椎动物视网膜电路启发的结构解决规则化问题

算法技术及其应用

主成分分析的学习算法和应用

学习评估和修剪技术