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Articles by Li-Quan Sun
Total Records ( 2 ) for Li-Quan Sun
  Xue-yao Gao , Li-quan Sun and Da-song Sun
  Particle Swarm Optimization (PSO) algorithm is often used for finding optimal solution, but it easily entraps into the local extremum in later evolution period. Based on improved chaos searching strategy, an enhanced particle swarm optimization algorithm is proposed in this study. When particles get into the local extremum, they are activated by chaos search strategy, where the chaos search area is controlled in the neighborhood of current optimal solution by reducing search area of variables. The new algorithm not only gets rid of the local extremum effectively but also enhances the precision of convergence significantly. Experiment results show that the proposed algorithm is better than standard PSO algorithm in both precision and stability.
  Li Feng and Li-Quan Sun
  In the algorithm of Kohonen’s Self-Organizing Maps (SOM) at the beginning of cluster, the number of input vectors in training set has to be settled down, which leads to the bad flexibility and is against the unsupervised principle. Also the fixed output network structure will lead to over-use or lack-of-use to the neuron node. To improve the exist defect of SOM and at the same time keep its advantages, an enhanced dynamic self organizing maps algorithm is proposed. This new method based on the idea of classical Growing Hierarchical Self-organizing Map (GHSOM), take advantage of GHSOM’s feature of self determine the structure reflects the variability of data. By put forward a new cycle network structure EDSOM overcome the limitation of neutron under-utilize and over-utilize caused by the boundary effect. The experiment of intrusion detection proved the efficiency of the algorithm.
 
 
 
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