Download PDF by Tao Tao, Zhenming Peng, Chaonan Yang, Fang Wei (auth.), Min: Electrical Engineering and Control: Selected Papers from the

By Tao Tao, Zhenming Peng, Chaonan Yang, Fang Wei (auth.), Min Zhu (eds.)

ISBN-10: 3642217648

ISBN-13: 9783642217647

This quantity comprises prolonged and revised types of a suite of chosen papers from the foreign convention on electrical and Electronics (EEIC 2011) , hung on June 20-22 , 2011, that's together prepared by means of Nanchang college, Springer, and IEEE IAS Nanchang Chapter.

The aim of EEIC 2011 quantity 2 is to supply a big interdisciplinary discussion board for the presentation of recent ways from electric engineering and controls, to foster integration of the newest advancements in medical examine. 133 comparable subject papers have been chosen into this quantity. the entire papers have been reviewed through 2 application committee individuals and chosen by means of the quantity editor Prof. Min Zhu.

We wish each player may have an excellent chance to switch their examine principles and effects and to debate the cutting-edge within the parts of engineering and controls.

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Extra resources for Electrical Engineering and Control: Selected Papers from the 2011 International Conference on Electric and Electronics (EEIC 2011) in Nanchang, China on June 20-22, 2011, Volume 2

Example text

BFO eliminate animals with great foraging strategies to successfully gain the desired target. BFO mimics the biological motion of the E. coli bacteria, there are chemo taxis, swarming, tumbling, reproduction, elimination and dispersal actions. But the complexity of BFO forced researchers for its simplification and for faster convergence. Therefore, BFPSO learning algorithm is integrated the benefits of the BFO’s global search ability and the PSO’s fast convergence learning machine. In the article, we propose a bacterial foraging particle swarm optimization (BFPSO) algorithm to avoid the trial-and-error type trying way.

The PSO algorithm performs the heuristic exchange of their own and other particle’s experiences which have been better so far to discover superior offprint. In the learning cycle, each particle’s position value X and velocity value Y are regulated by two best values Pbest_x and Gbest_x. Pbest_x is denoted as the individual particle’s best solution (highest fitness) it has achieved so far. Gbest_x is obtained by choosing the overall best value from all particles in populations. In this basic PSO iteration learning step, the velocity of the particle is learned according to the relative Pbest_x and Gbest_x values.

Thus, different parameters set {ci1, ci2, … , cin, di1, di2, … din, yi, 1 ≤ i ≤ m } decide different fuzzy system with different performance setting. If there are m fuzzy rules to construct, the m*(2n+l) parameters in this parameter set {ai1, ai2, … , ain, bi1, bi2, … bin, yi, 1 ≤ i ≤ m } need to be determined for designing such fuzzy systems. This searching problem is that the m fuzzy rules with n inputs are proposed in an n dimensional space to choice the proper m hyper-ellipsoids functions. In this paper, the parameters selection in approaching the appropriate fuzzy system is formulated as a space search problem.

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Electrical Engineering and Control: Selected Papers from the 2011 International Conference on Electric and Electronics (EEIC 2011) in Nanchang, China on June 20-22, 2011, Volume 2 by Tao Tao, Zhenming Peng, Chaonan Yang, Fang Wei (auth.), Min Zhu (eds.)


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