Particle Swarm Optimization and Intelligence: Advances and Applications. Konstantinos E. Parsopoulos, Michael N. Vrahatis
ISBN: 1615206663,9781615206667 | 329 pages | 9 Mb
Particle Swarm Optimization and Intelligence: Advances and Applications Konstantinos E. Parsopoulos, Michael N. Vrahatis
Publisher: Information Science Publishing
In many filtering applications −3 dB frequency has become a recognizable parameter for defining the cut-off frequency (the frequency at which the magnitude attains an absolute value of 0.5). And reasoning; data mining and knowledge discovery; machine learning; statistical learning; evolutionary computation; particle swarm optimization; intelligent agent; search and planning; natural language processing; and AI applications. RGA, PSO, and DE have also been adopted for the sake of comparison. In order to solve that the clustering algorithm based on PSO can not determine the number of clusters k, by the k-means algorithm, achieve the best number of cluster k and the structuring of the cluster validity function. Research and Application of DNA Clustering Algorithm Based on Intelligent Algorithm. Comments Off However, in most cases, the number of clusters k can not be determined in advance, so the best number of clusters k needs to be optimized. Evolutionary algorithms stand upon some common characteristics like stochastic, adaptive, and learning in order to produce intelligent optimization schemes. The International Conference on Swarm Intelligence (ICSI 2011) will take place in Cergy (Paris area), France, in June 14-15, 2011. Artificial Intelligence LISP Prolog Language: English | 137 books | PDF | 853 MB Bayesian networks : Dynamic Bayesian Networks Representation, Inference And Learning - Kevin Patrick Murphy Learning Ba. Particle Swarm Optimization Methods for Pattern Recognition and Image Processing - Mahamed G. It has recently Theoretical advances of particle swarm optimization, ant colony optimization.
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