Learning Fuzzy Systems by a Co-Evolutionary Artificial-Immune-Based Algorithm

2009 ◽  
pp. 312-319 ◽  
Author(s):  
Luiz Lenarth G. Vermaas ◽  
Leonardo M. Honorio ◽  
Muriel Freire ◽  
Daniele Barbosa
Keyword(s):  
Fuzzy Systems ◽  
Artificial Immune

Applications of Computational Intelligence in Computing Security

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.

H∞ control for Takagi–Sugeno fuzzy systems

2001 ◽  
Vol 32 (7) ◽  
pp. 915-924 ◽  
Author(s):  
Jun Yoneyama ◽  
Masahiro Nishikawa ◽  
Hitoshi Katayama ◽  
Akira Ichikawa
Keyword(s):  
Fuzzy Systems ◽  
Takagi Sugeno

Improvement of AODV Routing on MANETs Using Fuzzy Systems

2011 ◽  
Vol 7 (2) ◽  
pp. 102-106 ◽  
Author(s):  
Taqwa Odey Fahad ◽  
Abduladhim A. Ali
Keyword(s):  
Fuzzy Systems

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

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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