Artificial neural networks: A tutorial
Artificial neural networks: A tutorial
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DOI：
10.1109/2.485891
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发表时间：
1996-03-01
期刊：
影响因子：
2.2
通讯作者：
Mohiuddin, KM
中科院分区：
文献类型：
--
作者：
Jain, AK;Mao, JC;Mohiuddin, KM
在开发智能程序方面已经取得了许多进展，其中一些是受到生物神经网络的启发。人工神经网络（ANN）是一种用于解决模式识别、预测、优化、联想记忆和控制等问题的新型网络。虽然在某些严格约束的环境中可以找到成功的传统应用，但没有一种能够灵活地在其领域之外表现良好。人工神经网络提供了令人兴奋的替代方案，许多应用程序都可以从使用它们中受益。本文适合那些对人工神经网络知之甚少或一无所知的读者，以帮助他们理解本期《计算机》中的其他文章。它讨论了人工神经网络发展背后的动机;描述了基本的生物神经元和人工计算模型;概述了网络架构和学习过程;并介绍了多层前馈网络，Kohonen的自组织映射，Carpenter和Grossberg的自适应共振理论模型以及Hopfield网络。它的结论与字符识别，一个成功的人工神经网络的应用。
Numerous advances have been made in developing intelligent programs, some inspired by biological neural networks. Researchers from many scientific disciplines are designing artificial neural networks (ANNs) to solve a variety of problems in pattern recognition, prediction, optimization, associative memory; and control.Although successful conventional applications can be found in certain well-constrained environments, none is flexible enough to perform well outside its domain. ANNs provide exciting alternatives, and many applications could benefit from using them.This article is for those readers with little or no knowledge of ANNs to help them understand the other articles in this issue of Computer. It discusses the motivation behind the development of ANNs; describes the basic biological neuron and the artificial computation model; outlines network architectures and learning processes; and presents multilayer feed-forward networks, Kohonen's self-organizing maps, Carpenter and Grossberg's Adaptive Resonance Theory models, and the Hopfield network. It concludes with character recognition, a successful ANN application.