Evolving Intelligent Systems: Methods, Learning, & Applications

N. Kasabov, Dimitar Filev
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引用次数: 61

Abstract

The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of Evolving Intelligent Systems are summarized in this short communication. Intelligent systems can be defined as systems that incorporate some form of reasoning that is typical for humans. Fuzzy Systems are well known for being able to formalize human knowledge that still separates humans from machines. Artificial Neural Networks have proven to be a useful form of parallel processing of information that employs principles from the organization of the brain. Finally, the evolution is a phenomenon that was initially used to solve optimization problems inspired by the progress in Genetic Algorithms, Evolutionary Computing, and Genetic Programming. These types of evolutionary algorithms are mimicking the natural selection that takes place in populations of living creatures over generations. More recently, the evolution of individual systems within their life-span (self-organization, learning through experience, and self-developing) has attracted attention. These systems called `evolving' came as a result of the research on practical intelligent systems and on-line learning algorithms that are capable of extracting knowledge from data and performing a higher level adaptation of model structure as well as model parameters. Evolving systems can also be considered an extension of the multi-model concept known from the control theory, and of the on-line identification of fuzzy rule-based models. They can also be regarded as an extension of the methods for on-line learning neural networks with flexible structure that can grow and shrink.
进化的智能系统:方法、学习和应用
本文概述了进化智能系统的基本概念、构成、背景,并对近年来新兴领域的研究成果和有待解决的问题进行了综述。智能系统可以被定义为包含人类典型的某种推理形式的系统。模糊系统以能够形式化人类知识而闻名,这些知识仍然将人类与机器区分开来。人工神经网络已被证明是一种有用的信息并行处理形式,它采用了大脑组织的原理。最后,进化是一种现象,最初用于解决遗传算法、进化计算和遗传规划的进步所激发的优化问题。这些类型的进化算法是在模仿生物种群代代相传的自然选择。最近,个体系统在其生命周期内的进化(自组织、通过经验学习和自我发展)引起了人们的注意。这些被称为“进化”的系统是对实用智能系统和在线学习算法的研究的结果,这些算法能够从数据中提取知识,并对模型结构和模型参数进行更高层次的适应。进化系统也可以被认为是控制理论中已知的多模型概念的扩展,以及基于模糊规则的模型的在线识别。它们也可以被视为具有可生长和收缩的柔性结构的在线学习神经网络方法的扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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