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人工免疫系统:理论与应用
引用本文:丁永生,任立红.人工免疫系统:理论与应用[J].模式识别与人工智能,2000,13(1).
作者姓名:丁永生  任立红
作者单位:东华大学信息科学与技术学院自动化系上海 200051
基金项目:国家自然科学基金,上海市教委"曙光计划"
摘    要:由生物引发的信息处理系统可分为:人工神经网络、进化计算和人工免疫系统(AIS).其中,神经网络和进化计算已被广泛地应用于各领域,而AIS由于其复杂性,应用相对较少.AIS实现一种由生物免疫系统启发的通过学习外界物质的自然防御机理的学习技术,提供了噪声忍耐、无教师学习、自组织、不需要反面例子、能明晰地表达学习的知识、具有内容可访记忆和能遗忘很少使用的信息等进化学习机理,结合了分类器、神经网络和机器推理等系统的一些优点,因此具有提供新颖的解决问题方法的潜力.为促使AIS更好地应用于科学和工程领域,本文系统地综述了AIS的最新研究成果,最后指出了其进一步研究的方向.

关 键 词:生物免疫系统  人工免疫系统  人工免疫网络  免疫学习算法  科学应用  工程应用

ARTIFICIAL IMMUNE SYSTEMS: THEORY AND APPLICATIONS
Abstract:Bilogically-motivated information processing systems can be classified into: artificial neural networks, evo lutionary computation and artificial immune systems (AISs). Among these, artificial neural networks and evolu tionary computation have been widely applied to various fields. But there have been a relatively few applications of AISs because of their complexity. AIS implements a learning technique inspired by the biological immune system which is a remarkable natural defense mechanism that learns about foreign substances. Also, AIS offers noise tolerance, unsupervised learning, and self-organizing. It doesn't need negative examples and can explicitly represent what it has learned. Moreover, AIS has an evolutionary learning mechanism which possesses a content addressable memory and the ability to forget little-used information, Such a system combines the advantages of learning classifier systems with some of the advantages of neural networks and machine induction. Hence, AIS has the potential to provide some novel methods to solve problems. In order that AIS could be better applied to science and engineering fields, the recent research results of AIS are systematically overviewed in this paper. Finally, the directions for further study are also provided.
Keywords:Biological Immune Systems  Artificial Immune Systems  Artificial Immune Networks  Immune Learning  ALgorithms  Science Applications  Engineering Applications
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