Download Intelligent Computing Theory: 10th International Conference, by De-Shuang Huang, Vitoantonio Bevilacqua, Prashan Premaratne PDF

By De-Shuang Huang, Vitoantonio Bevilacqua, Prashan Premaratne

This e-book – along with the volumes LNAI 8589 and LNBI 8590 – constitutes the refereed court cases of the tenth overseas convention on clever Computing, ICIC 2014, held in Taiyuan, China, in August 2014. The ninety two papers of this quantity have been conscientiously reviewed and chosen from various submissions. The papers are equipped in topical sections corresponding to evolutionary computation and studying; swarm intelligence and optimization; computer studying; social and ordinary computing; neural networks; biometrics attractiveness; snapshot processing; info defense; digital fact and human-computer interplay; wisdom discovery and knowledge mining; sign processing; trend attractiveness; biometric process and defense for clever computing.

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Additional resources for Intelligent Computing Theory: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings

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After adaption and alteration IO_EAMn is obtained. 12. After every iteration of the GA and EAM in parallel, IO_GAn and IO_EAMn are obtained. 13. PGEn = {IO_GAn U IO_EAMn} 14. 14. n = n + 1 15. If n == MAXGEN 16. Set F*= PGEMAXGEN Then go to 16 else go to 4 17. Return F* 18. 2 Result Analysis Both uni-modal and multi-modal functions [18, 19] have been taken in this paper to check the performance of the proposed algorithm with PSO-TVAC, SADE and EAM. For each benchmark function population size (P = 40 and 100), generation (G = 1000) and dimensions (D = 7, 10, 20, 30) have been taken for experimental analysis.

5. 1  X = ( xih ) and Y = ( yho ) refer to the weight matrices of the neural network, where xih is the synaptic weight from the input neuron i to the hidden neuron h , and yho is the synaptic weight from the hidden neuron h to the output neuron o . Thereby, we can adjust the output value to control the input values. Self-tuning Performance of Database Systems with Neural Network 4 7 Performance Tuning Algorithm We know that one performance indicator of the database can be affected by a number of parameters, although the degree is different.

The American Naturalist 30(355), 536–553 (1896) 5. : Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. U. Michigan Press (1975) 6. : An introduction to differential evolution. In: New Ideas in Optimization, pp. 79–108. , UK (1999) 7. : Assessing the impacts of phenotypic plasticity on evolution. Integrative and Comparative Biology 52(1), 5–15 (2012) 8. : Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch.

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