Springer, 2002. — 367.Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real-world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc.
In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain expert) and an implicit, hidden knowledge "buried" in past-experience numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge "encoded" in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed. Since traditional, symbolic AI systems are not able to make effective use of this kind of data, new methods and algorithms for the extraction of knowledge from data, knowledge representation and reasoning have been emerging in the last several years. They can be treated either as complementary techniques with regard to traditional AI systems or as a kind of modern extension and generalization of them. Computational Intelligence (CI) systems - based on various synergistic links between artificial neural networks, methods of granular information processing (in particular, fuzzy sets and fuzzy logic), and methods of evolutionary computations (in particular, genetic algorithms) - are the most representative class of these methodologies.
During the last couple of decades there has been growing interest in algorithms, which rely on analogies to natural processes and "humanlike" problem-solving. All three main constituents of CI systems belong to this group. The theory of fuzzy sets and fuzzy logic was developed as a means for representing, manipulating, and utilizing uncertain information and to provide a framework for handling uncertainties and imprecision in realworld applications. This theory provides inference mechanisms that enable approximate reasoning and model human reasoning capabilities to be applied to knowledge-based intelligent systems. Artificial neural networks are biologically-inspired, massively-parallel, distributed information processing systems. They are characterized by a computational power, fault tolerance, as well as learning and generalizing capabilities. Genetic algorithms are a global-search paradigm based on principles imitating mechanisms of genetics, natural selection, evolution and heredity, including the evolutionary principle of survival of the fittest and extinction of the worst adapted individuals.
Synergistic combination of all three methodologies has a very sound rational basis because they all approach the problem of designing intelligent systems from quite different but complementary angles. Thus, their combination within one system significantly reduces their shortcomings and amplifies their merits. An integrated CI system has the advantages of neural systems (learning, generalization and adaptation abilities, processing huge amounts of numerical data from databases, and a connectionist structure with high fault tolerance and distributed representation properties), fuzzy systems (structural framework with easily -interpretable rule-based knowledge and high-level fuzzy reasoning) and genetic algorithms (parameter and structure optimization of the system).
This research monograph presents new concepts and implementations of CI systems and a broad comparative analysis with several of the existing, best-known neuro-fuzzy systems as well as with systems representing other knowledge-discovery techniques such as rough sets, decision trees, regression trees, probabilistic rule induction, etc. This presentation is preceded by a discussion of the main directions of synthesizing fuzzy sets, artificial neural networks and genetic algorithms in the framework of designing CI systems. In order to keep the book self-contained, introductions to the basic concepts of fuzzy systems, artificial neural networks and genetic algorithms are given. This book is intended for researchers and practitioners in AI/CI fields and for students of computer science or neighbouring areas.Introduction
Elements of the theory of fuzzy sets
Essentials of artificial neural networks
Brief introduction to genetic algorithms
Main directions of combining artificial neural networks, fuzzy sets and evolutionary computations in designing computational intelligence systems
Neuro-fuzzy(-genetic) system for synthesizing rule-based knowledge from data
Rule-based neuro-fuzzy modelling of dynamic systems and designing of controllers
Neuro-fuzzy(-genetic) rule-based classifier designed from data for intelligent decision support
Fuzzy neural network for system modelling and control

Fuzzy neural classifier

Springer, 2002. - 367.传统的人工智能（AI）系统采用符号处理作为其主要范式。符号人工智能系统在处理以精确和完整的知识表示为特征的问题时被证明是有效的。不幸的是，这些系统在处理不精确、不确定和不完整的数据和信息方面能力很弱，而这些数据和信息对许多现实世界的问题，包括物理系统和过程以及决策机制的描述都有很大的帮助。此外，在许多情况下，由于现有知识的不完整性、人类专家的不同偏见造成的问题、形成规则的困难等等，专家领域知识（许多符号人工智能系统的基础）不足以设计智能系统。

一般来说，解决一个特定问题的问题知识可以包括一个显性知识（例如，由领域专家提供的启发式规则）和一个 "埋藏 "在过去经验的数字数据中的隐性、隐藏的知识。研究大量的这些数据（收集在数据库中）并综合其中 "编码 "的知识（也被称为数据中的知识发现或数据挖掘），可以显著提高所设计的智能系统的性能。由于传统的、符号化的人工智能系统无法有效地利用这类数据，在过去几年中，从数据中提取知识、知识表示和推理的新方法和算法不断出现。它们既可以被看作是对传统人工智能系统的补充技术，也可以被看作是对它们的一种现代扩展和概括。计算智能（CI）系统--基于人工神经网络、颗粒信息处理方法（特别是模糊集和模糊逻辑）和进化计算方法（特别是遗传算法）之间的各种协同联系--是这些方法学中最具代表性的一类。

在过去的几十年里，人们对算法的兴趣越来越大，它依赖于对自然过程和 "人类 "问题解决的类似性。CI系统的三个主要构成部分都属于这一类。模糊集和模糊逻辑的理论是作为一种表示、操作和利用不确定信息的手段而发展起来的，并为处理现实世界应用中的不确定性和不精确性提供一个框架。这一理论提供了推理机制，使近似推理和人类推理能力的模型被应用于基于知识的智能系统。人工神经网络是受生物启发的、大规模并行的、分布式信息处理系统。它们的特点是计算能力、容错能力以及学习和概括能力。遗传算法是一种基于模仿遗传学、自然选择、进化和遗传机制的全局搜索范式，包括适者生存和最差个体灭绝的进化原则。

所有这三种方法的协同组合有一个非常合理的基础，因为它们都是从相当不同但互补的角度来设计智能系统的问题。因此，它们在一个系统中的结合大大减少了它们的缺点并放大了它们的优点。一个综合的CI系统具有神经系统（学习、概括和适应能力，处理来自数据库的大量数字数据，以及具有高容错性和分布式表示特性的连接主义结构）、模糊系统（具有易于解释的基于规则的知识和高级模糊推理的结构框架）和遗传算法（系统的参数和结构优化）的优势。

本研究专著介绍了CI系统的新概念和实现，并与现有的几个最著名的神经模糊系统以及代表其他知识发现技术的系统进行了广泛的比较分析，如粗糙集、决策树、回归树、概率规则归纳等。在这个介绍之前，我们讨论了在设计CI系统的框架内综合使用模糊集、人工神经网络和遗传算法的主要方向。为了使本书自成体系，对模糊系统、人工神经网络和遗传算法的基本概念作了介绍。本书适用于人工智能/CI领域的研究人员和从业人员以及计算机科学或邻近领域的学生。

模糊集理论的要素

人工神经网络的要点

遗传算法的简要介绍

结合人工神经网络、模糊集和进化计算来设计计算智能系统的主要方向

从数据中合成基于规则的知识的神经-模糊（-遗传）系统

基于规则的动态系统的神经模糊建模和控制器的设计

从数据中设计基于规则的神经模糊（遗传）分类器，用于智能决策支持

用于系统建模和控制的模糊神经网络

模糊神经分类器