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Fuzzy logic, neural networks, and soft computing
Lotfi A. Zadeh
- pp 775-782
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TLDR
A simple case in point is the problem of parking a car as discussed by the authors, where the final position of the car is not specified exactly, and if it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position.Abstract:
The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.read more
Citations
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Journal ArticleDOI
Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic
TL;DR: M Modes of information granulation (IG) in which the granules are crisp (c-granular) play important roles in a wide variety of methods, approaches and techniques, but this does not reflect the fact that in almost all of human reasoning and concept formation thegranules are fuzzy (f- Granular).
Journal ArticleDOI
Nonlinear black-box modeling in system identification: a unified overview
Jonas Sjöberg,Qinghua Zhang,Lennart Ljung,Albert Benveniste,Bernard Delyon,Pierre-Yves Glorennec,Håkan Hjalmarsson,Anatoli Juditsky +7 more
TL;DR: What are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques are described, from a user's perspective.
Journal ArticleDOI
Gesture Recognition: A Survey
Sushmita Mitra,T. Acharya +1 more
TL;DR: A survey on gesture recognition with particular emphasis on hand gestures and facial expressions is provided, and applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail.
Book ChapterDOI
From computing with numbers to computing with words. From manipulation of measurements to manipulation of perceptions
TL;DR: The computational theory of perceptions (CTP) as mentioned in this paper is a methodology for reasoning and computing with perceptions rather than measurements, where words play the role of labels of perceptions and, more generally, perceptions are expressed as propositions in a natural language.
Book ChapterDOI
Computational Intelligence: An Introduction
TL;DR: The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.
References
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Journal ArticleDOI
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Journal ArticleDOI
The concept of a linguistic variable and its application to approximate reasoning—II☆
TL;DR: Much of what constitutes the core of scientific knowledge may be regarded as a reservoir of concepts and techniques which can be drawn upon to construct mathematical models of various types of systems and thereby yield quantitative information concerning their behavior.
Journal ArticleDOI
Outline of a New Approach to the Analysis of Complex Systems and Decision Processes
TL;DR: By relying on the use of linguistic variables and fuzzy algorithms, the approach provides an approximate and yet effective means of describing the behavior of systems which are too complex or too ill-defined to admit of precise mathematical analysis.
Book
Introduction To The Theory Of Neural Computation
TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.
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Decision-making in a fuzzy environment
Richard Bellman,Lotfi A. Zadeh +1 more
TL;DR: A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.