Results 141 to 150 of about 20,311 (188)
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Integrated Evolutionary Neural Network Approach With Distributed Computing for Congestion Management
Applied Soft Computing Journal, 2010Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security ...
Laxmi Srivastava, Shashikala Tapaswi
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Soft computing - integrating evolutionary, neural, and fuzzy systems.
, 2001 Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit
Andrea G. B. Tettamanzi, Marco Tomassini
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Computational intelligence-fuzzy, neural, and evolutionary computing
Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 2002This paper introduces the recent topics of computational intelligence. First, elemental techniques of fuzzy, neural, and evolutionary computing are described, and next, their emerging synthetic methodology for robotics is introduced. Furthermore, this paper proposes the total architecture of the structured intelligence for robotic systems.
T. Fukuda, N. Kubota
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Evolutionary computation in the design of optimum neural controllers
1999 European Control Conference (ECC), 1999This paper presents a novel technique in which fuzzy and evolutionary techniques are fused for the design of a class of optimum neural controllers. In the proposed technique the attributes of the performance of the closed system, i.e. overshoot, rise time and settling time in response to a step demand are related to the suitability of the controller ...
V. Goggos, Anna Stathaki, Robert E. King
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, 2013 This paper gives a brief presentation of history of Soft Computing considered as a mix of three scientific disciplines that arose in the mid of the 20th century: Fuzzy Sets and Systems, Neural Networks, and Evolutionary Computation.
Marco Elio Tabacchi, Rudolf Seising
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Boosted Neural Networks in Evolutionary Computation
2009The paper deals with a neural-network-based version of surrogate modelling, a modern approach to the optimization of empirical objective functions. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, a property that is particularly attractive in evolutionary optimization. In the paper, an extension of
Martin Holena +2 more
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An Evolutionary Computing Approach for the Autonomous Tuning of Neural Networks
Communications in Computer and Information ScienceJulio Ramirez-Pacheco +2 more
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When Neural Network Computation Meets Evolutionary Computation: A Survey
2016Neural network (NN) and evolutionary computation (EC) are two of the most popular and important techniques in computational intelligence, which can be combined together to solve the complex real world problems. This paper represents a review of the researches that combined NN and EC. There are 3 main research focuses as follows.
Zong-Gan Chen +4 more
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Computation: evolutionary, neural, molecular
2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00EX448), 2002A confluence of factors emanating from computer science, biology, and technology have brought self-organizing approaches back to the fore. Neural networks in particular provide high evolvability platforms for variation-selection search strategies. The neuron doctrine and the fundamental nature of computing come into question. Is a neuron an atom of the
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Fuzzy neural tree in evolutionary computation for architectural design cognition
2015 IEEE Congress on Evolutionary Computation (CEC), 2015A novel fuzzy-neural tree (FNT) is presented. Each tree node uses a Gaussian as a fuzzy membership function, so that the approach uniquely is in align with both the probabilistic and possibilistic interpretations of fuzzy membership. It provides a type of logical operation by fuzzy logic (FL) in a neural structure in the form of rule-chaining, yielding
Özer Ciftcioglu, Michael S. Bittermann
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