Diagnosis Model Based on Least Squares Support Vector Machine Optimized by Multi-swarm Cooperative Chaos Particle Swarm Optimization and Its Application

Guojun Ding1; Lide Wang1; Peng Yang1; Ping Shen1; Shuping Dang2

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Diagnosis Model Based on Least Squares Support Vector Machine Optimized by Multi-swarm Cooperative Chaos Particle Swarm Optimization and Its Application

机译：基于最小二乘支持向量机的诊断模型通过多群合作混沌粒子群优化优化及其应用

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—The classification accuracy of the least squares support vector machine (LSSVM) models strongly depends on proper setting of its parameters. An optimal selection approach of LSSVM parameters is put forward based on multi-swarm cooperative chaos particle swarm optimization (MCCPSO) algorithm. Chaos particle swarm optimization (CPSO) can improve the ability of local search optimization with good robust and adaptable. Multi-swarm cooperative particle swarm optimization (MCPSO) algorithm is masterslave heuristic method with a good global search. Then the MCCPSO-LSSVM diagnosis model is used to diagnosing analog circuit fault. Simulation results show that MCCPSO algorithm can jump out of local optimums with fast convergence and good stability. Results for analog circuit fault diagnosis show that the proposed method has strong robustness, and high accuracy.

机译：- 最小二乘支持向量机（LSSVM）模型的分类准确性强烈取决于其参数的正确设置。基于多群协作混沌粒子群综合优化（MCCPSO）算法，提出了LSSVM参数的最佳选择方法。混沌粒子群优化（CPSO）可以通过良好的鲁棒和适应性提高本地搜索优化的能力。多群协作粒子群优化（MCPSO）算法是具有良好全球搜索的MastersLave启发式方法。然后MCCPSO-LSSVM诊断模型用于诊断模拟电路故障。仿真结果表明，MCCPSO算法可以用快速收敛和良好的稳定性跳出局部最优。模拟电路故障诊断结果表明，该方法具有强大的鲁棒性和高精度。

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《Journal of Computers》 |2013年第4期|共8页
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Guojun Ding1; Lide Wang1; Peng Yang1; Ping Shen1; Shuping Dang2;
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Diagnosis Model Based on Least Squares Support Vector Machine Optimized by Multi-swarm Cooperative Chaos Particle Swarm Optimization and Its Application

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