James Keller, Derong Liu, and David Fogel: Fundamentals of computational intelligence: neural networks, fuzzy systems, and evolutionary computation

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.

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Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Applied Computational Intelligence and Soft Computing ◽

10.1155/2011/938240 ◽

2011 ◽

Vol 2011 ◽

pp. 1-20 ◽

Cited By ~ 14

Author(s):

Biaobiao Zhang ◽

Yue Wu ◽

Jiabin Lu ◽

K.-L. Du

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Structural Optimization ◽

Evolutionary Computation ◽

Fuzzy Systems ◽

Parametric Optimization ◽

Optimization Techniques ◽

System Modelling ◽

Discrete Optimization Problem ◽

Conventional Optimization

Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

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Computational Intelligence

Manufacturing Intelligence for Industrial Engineering - Advances in Civil and Industrial Engineering ◽

10.4018/978-1-60566-864-2.ch005 ◽

2010 ◽

pp. 111-136 ◽

Cited By ~ 1

Author(s):

Zude Zhou ◽

Huaiqing Wang ◽

Ping Lou

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Computational Intelligence ◽

Analytical Methods ◽

Fuzzy Systems ◽

Academic Community ◽

Neural Computing ◽

New Paradigm ◽

Combined System ◽

Robust System

In the 1990s, a new paradigm of science characterized by uncertainty, nonlinearity, and irreversibility and tackling complex problems was generally recognized by the academic community. In this new paradigm, traditional analytical methods are ineffectual, and there is recognition of the need to explore new methods to solve the more flexible, more robust system problems. In 1994 the first Computational Intelligence Conference in Orlando, Florida, US, first combined three different areas, smart neural networks, fuzzy systems and genetic algorithms, not only because the three have many similarities, but also because a properly combined system of the three is more effective than a system generated by one single technical field. Various theories and approaches of computational intelligence including neural computing, fuzzy computing and evolutional computing are comprehensively introduced in this chapter.

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Computational Intelligence as a Tool for Small Modular Reactors

ASME 2011 Small Modular Reactors Symposium ◽

10.1115/smr2011-6544 ◽

2011 ◽

Cited By ~ 1

Author(s):

Milos Manic ◽

Piyush Sabharwall

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Computational Intelligence ◽

Fuzzy Systems ◽

Nuclear Engineering ◽

Time Analysis ◽

Real Time Analysis ◽

Small Modular Reactors ◽

Engineering Problems ◽

And Performance

Computational intelligence techniques (CITs) traditionally consist of artificial neural networks (ANNs), fuzzy systems and genetic algorithms. This article overviews diverse implementations of ANNs, which are the most prominent in nuclear engineering problems, especially for small modular reactors (SMRs). Advanced computational intelligence-based tools will allow data to be transformation into knowledge, thus improving understanding, predictability (can be seen from the two case studies for thermal-hydraulic prediction), sustainability, and performance of SMRs with real time analysis and monitoring.

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Applications of Computational Intelligence in Computing Security

Implementing Computational Intelligence Techniques for Security Systems Design - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-2418-3.ch001 ◽

2020 ◽

pp. 1-22

Author(s):

Yousif Abdullatif Albastaki

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Swarm Intelligence ◽

Computational Intelligence ◽

Fuzzy Systems ◽

Artificial Immune Systems ◽

Artificial Immune ◽

Immune Systems

This chapter is an introductory chapter that attempts to highlight the concept of computational intelligence and its application in the field of computing security; it starts with a brief description of the underlying principles of artificial intelligence and discusses the role of computational intelligence in overcoming conventional artificial intelligence limitations. The chapter then briefly introduces various tools or components of computational intelligence such as neural networks, evolutionary computing, swarm intelligence, artificial immune systems, and fuzzy systems. The application of each component in the field of computing security is highlighted.

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Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

Archives of Metallurgy and Materials ◽

10.2478/amm-2013-0090 ◽

2013 ◽

Vol 58 (3) ◽

pp. 871-875

Author(s):

A. Herberg

Keyword(s):

Neural Networks ◽

Control System ◽

Network Structure ◽

Fuzzy Systems ◽

Fuzzy Neural Networks ◽

Cnc Machining ◽

Machining Process ◽

Modeling Process ◽

Fuzzy Neural ◽

Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

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