Open AccessBook
Fuzzy and neural approaches in engineering
Lefteri H. Tsoukalas,Robert E. Uhrig +1 more
- 01 Jan 1997
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TL;DR: Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy Systems.
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Abstract: From the Publisher:
Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy systems. Exploring the value of relating genetic algorithms and expert systems to fuzzy and neural technologies, this forward-thinking text highlights an entire range of dynamic possibilities within soft computing. With examples of specifically designed to illuminate key concepts and overcome the obstacles of notation and overly mathematical presentations often encountered in other sources, plus tables, figures, and an up-to-date bibliography, this unique work is both an important reference and a practical guide to neural networks and fuzzy systems.
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Citations
Creep rupture forecasting for high performance energy systems
Stylianos Chatzidakis,Miltiadis Alamaniotis,Lefteri H. Tsoukalas +2 more
- 07 Jul 2014
TL;DR: The results obtained demonstrate the capability of the proposed methodology to apply artificial neural networks to forecast the time to rupture and improve safety and efficiency of high performance systems.
3
•Proceedings Article
Fuzzy logic based motion control of mobile robot in a rough terrain
D. Elayaraja,S. Ramabalan +1 more
- 30 Mar 2012
TL;DR: This paper describes the fuzzy logic control of motion control of mobile robot on rough terrain using a two output and single output system.
3
Real-Time Identification and Forecasting of Chaotic Time Series Using Hybrid Systems of Computational Intelligence
Yevgeniy Bodyanskiy,Vitaliy Kolodyazhniy +1 more
- 01 Jan 2006
TL;DR: The problems of identification, modeling, and forecasting of chaotic signals are discussed, and novel hybrid structures based on the Kolmogorov’s superposition theorem and using the neo-fuzzy neurons as elementary processing units are solved.
3
•Book Chapter
Modelling ethical decisions
Reggie Davidrajuh
- 01 Jan 2010
TL;DR: In this article, the authors discuss the difficulty of making ethical decisions in business decisions and present a solution to the ethical dilemma of business decision makers without any computing aid, which is not easy for decision makers to make an "optimal" solution.
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