An Improved Particle Swarm Algorithm and Its Application in Soft Sensor Modeling
An Improved Particle Swarm Algorithm and Its Application in Soft Sensor Modeling
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发表时间：
2007
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通讯作者：
Q. Feng
中科院分区：
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作者：
Q. Feng
提出了一种改进的PSO(粒子群优化)算法，很好地解决了基本PSO算法收敛速度慢、计算精度低的问题。通过对多个标准测试函数的结果进行比较分析，证明了PSO的优良性能。然后，将改进的PSO应用于NN(神经网络)结构和参数的优化。通过将NN应用于4-CBA测量的软测量建模，证明了该算法优化神经网络的有效性。
An improved PSO(particle swarm optimization) algorithm is presented which well addresses slow convergence speed and low calculation precision in the basic PSO algorithm.By comparing and analyzing the results of several standard test functions,the excellent performance of PSO is proved.Then,the improved PSO is applied to optimization of the structure and parameters in NN(neural network).The availability of algorithm in optimizing neural network is proved by applying NN in soft sensor modeling of 4-CBA measurement.