By De-Shuang Huang, Vitoantonio Bevilacqua, Prashan Premaratne
This e-book – at the side of the volumes LNAI 8589 and LNBI 8590 – constitutes the refereed complaints of the tenth foreign 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 a number of submissions. The papers are geared up in topical sections corresponding to evolutionary computation and studying; swarm intelligence and optimization; computer studying; social and typical computing; neural networks; biometrics acceptance; snapshot processing; info safety; digital truth and human-computer interplay; wisdom discovery and knowledge mining; sign processing; trend reputation; biometric process and defense for clever computing.
Read or Download Intelligent Computing Theory: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings PDF
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Additional info for Intelligent Computing Theory: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings
8 Execution Steps Step 1. txt”, randomly initialize the population according to these constraints, select one arbitrary location in solution space to be the initial position of the particle and set its initial velocity. Step 2. Calculate the fitness value of all particles then update the global optimal value and the worst value in current population and individual extreme value according to the objective function (test function),. Step 3. Update ambient temperature and particle’s inertia weight in accordance with the simulated annealing table namely the formula (5); Meanwhile, utilize the sort-based approach which represented by formula (4) to update the particle radius, use the formula (9) and (10) to update move rate and wave rate; Finally update the velocity and location of the population according to equation (11) and (12).
Table 1. 2 Training Neural Network A sample training data set of size 2000 were used to train the neural network, while the rest were used to test the trained neural network. In our experiments, the neural network is a back propagation neural network as shown in Fig. 4. It has 3 input 6 C. Zheng, Z. Ding, and J. Hu neurons which are data buffer hit ratio and library cache hit ratio and in memory sort ratio, respectively; also has 3 output neurons which are db_cache_size, shared_pool _size and pga_aggregate_target while has 7 nodes in the hidden layer, respectively.
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. International Journal of Electrical Power and Energy Systems 31, 249–257 (2009) 9. : Self-adaptive Differential Evolution. -C. ) CIS 2005.