Journal ArticleDOI
Hybrid soft computing systems: industrial and commercial applications
Piero P. Bonissone,Yu-To Chen,Kai Goebel,Pratap Shankar Khedkar +3 more
- Vol. 87, Iss: 9, pp 1641-1667
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TLDR
In this article, a collection of methods and tools that can be used to perform diagnostics, estimation, and control of industrial equipment, freight train control, and residential property valuation are presented.Abstract:
Soft computing (SC) is an association of computing methodologies that includes as its principal members fuzzy logic, neurocomputing, evolutionary computing and probabilistic computing. We present a collection of methods and tools that can be used to perform diagnostics, estimation, and control. These tools are a great match for real-world applications that are characterized by imprecise, uncertain data and incomplete domain knowledge. We outline the advantages of applying SC techniques and in particular the synergy derived from the use of hybrid SC systems. We illustrate some combinations of hybrid SC systems, such as fuzzy logic controllers (FLCs) tuned by neural networks (NNs) and evolutionary computing (EC), NNs tuned by EC or FLCs, and EC controlled by FLCs. We discuss three successful real-world examples of SC applications to industrial equipment diagnostics, freight train control, and residential property valuation.read more
Citations
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Journal ArticleDOI
Ten years of genetic fuzzy systems: current framework and new trends
TL;DR: The objective of this paper is to provide an account of genetic fuzzy systems, with special attention to genetic fuzzy rule-based systems.
An essay towards solving a problem in the doctrine of chances. [Facsimil]
TL;DR: The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.
Proceedings ArticleDOI
Ten years of genetic fuzzy systems: current framework and new trends
TL;DR: The article focuses on genetic fuzzy systems, paying special attention to genetic fuzzy rule based systems, giving a brief overview of the field.
Journal ArticleDOI
Evolutionary algorithms + domain knowledge = real-world evolutionary computation
TL;DR: The implicit and explicit knowledge representation mechanisms for evolutionary algorithms (EAs) are discussed and offline and online metaheuristics as examples of explicit methods to leverage this knowledge are described.
Journal ArticleDOI
The merging of neural networks, fuzzy logic, and genetic algorithms
TL;DR: An overview of the merging of NNs, FL and GAs is presented, which includes the advantages and disadvantages of each technology, the potential merging options, and the explicit nature of the merge.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Classification and Regression Trees.
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
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.