Neuro Fuzzy Techniques For Intelligent Information Systems

The book provides an introduction to basic concepts as well as some recent advancements in fuzzy set theory, approximate reasoning, artificial neural networks and clustering methods. These methodologies create together the so-called soft computing, which is part of a computational approach to system intelligence. The book deals with an overview of fuzzy set theory, foundations for approximate reasoning principles, specific equivalence of inference results using logical conjunctive interpretations of if-then rules, supervised and unsupervised artificial neural networks, a new generalized conditional fuzzy clustering method, artificial neural networks-based fuzzy inference system with parameterized consequences in if-then rules, MATLAB(R) m-files implementation of neuro-fuzzy systems, detailed study of neuro-fuzzy systems applications.

This volume comprises selected chapters that cover contemporary issues of the development and the application of neuro-fuzzy techniques. Developing and using neural networks, fuzzy logic systems, genetic algorithms and statistical methods as separate techniques, or in their combination, have been research topics in several areas such as mathematics, engineering, computer science, physics, economics and finance. Here the latest results in the fields are presented from both theoretical and practical point of view. The volume has four main parts. Part one presents generic techniques and theoretical issues while part two, three and four deal with practically oriented models, systems and implementations.

FUZZY INTELLIGENT SYSTEMS A comprehensive guide to Expert Systems and Fuzzy Logic that is the backbone of artificial intelligence. The objective in writing the book is to foster advancements in the field and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and those in education and research covering a broad cross section of technical disciplines. Fuzzy Intelligent Systems: Methodologies, Techniques, and Applications comprises state-of-the-art chapters detailing how expert systems are built and how the fuzzy logic resembling human reasoning, powers them. Engineers, both current and future, need systematic training in the analytic theory and rigorous design of fuzzy control systems to keep up with and advance the rapidly evolving field of applied control technologies. As a consequence, expert systems with fuzzy logic capabilities make for a more versatile and innovative handling of problems. This book showcases the combination of fuzzy logic and neural networks known as a neuro-fuzzy system, which results in a hybrid intelligent system by combining a human-like reasoning style of neural networks. Audience Researchers and students in computer science, Internet of Things, artificial intelligence, machine learning, big data analytics and information and communication technology-related fields. Students will gain a thorough understanding of fuzzy control systems theory by mastering its contents.

Hybrid intelligent systems are becoming a very important problem-solving methodology affecting researchers and practitioners in areas ranging from science and technology to business and commerce. This volume focuses on the hybridization of different soft computing technologies and their interactions with hard computing techniques, other intelligent computing frameworks, and agents. Topics covered include: genetic-neurocomputing, neuro-fuzzy systems, genetic-fuzzy systems, genetic-fuzzy neurocomputing, hybrid optimization techniques, interaction with intelligent agents, fusion of soft computing and hard computing techniques, other intelligent systems and hybrid systems applications. The different contributions were presented at the first international workshop on hybrid intelligent systems (HIS1) in Adelaide, Australia.

Hybrid intelligent systems are becoming a very important problem-solving methodology affecting researchers and practitioners in areas ranging from science and technology to business and commerce. This volume focuses on the hybridization of different soft computing technologies and their interactions with hard computing techniques, other intelligent computing frameworks, and agents. Topics covered include: genetic-neurocomputing, neuro-fuzzy systems, genetic-fuzzy systems, genetic-fuzzy neurocomputing, hybrid optimization techniques, interaction with intelligent agents, fusion of soft computing and hard computing techniques, other intelligent systems and hybrid systems applications. The different contributions were presented at the first international workshop on hybrid intelligent systems (HIS1) in Adelaide, Australia.

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 an implicit, hidden knowledge "buried" in past-experience expert) and 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.

Intelligence systems. We perfonn routine tasks on a daily basis, as for example: • recognition of faces of persons (also faces not seen for many years), • identification of dangerous situations during car driving, • deciding to buy or sell stock, • reading hand-written symbols, • discriminating between vines made from Sauvignon Blanc, Syrah or Merlot grapes, and others. Human experts carry out the following: • diagnosing diseases, • localizing faults in electronic circuits, • optimal moves in chess games. It is possible to design artificial systems to replace or "duplicate" the human expert. There are many possible definitions of intelligence systems. One of them is that: an intelligence system is a system able to make decisions that would be regarded as intelligent ifthey were observed in humans. Intelligence systems adapt themselves using some example situations (inputs of a system) and their correct decisions (system's output). The system after this learning phase can make decisions automatically for future situations. This system can also perfonn tasks difficult or impossible to do for humans, as for example: compression of signals and digital channel equalization.

One of the attractions of fuzzy logic is its utility in solving many real engineering problems. As many have realised, the major obstacles in building a real intelligent machine involve dealing with random disturbances, processing large amounts of imprecise data, interacting with a dynamically changing environment, and coping with uncertainty. Neural-fuzzy techniques help one to solve many of these problems. Fuzzy Logic and Intelligent Systems reflects the most recent developments in neural networks and fuzzy logic, and their application in intelligent systems. In addition, the balance between theoretical work and applications makes the book suitable for both researchers and engineers, as well as for graduate students.