Improved Particle Swarm Optimization and Applications to Hidden Markov Model and Ackley Function

被引：0

作者：

Motiian, Saeed ^{[1
]}

Soltanian-Zadeh, Hamid ^{[1
]}

机构：

[1] Univ Tehran, Sch Elect & Comp Engn, Control & Intelligent Proc Ctr Excellence, Tehran 14395515, Iran

来源：

2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MEASUREMENT SYSTEMS AND APPLICATIONS (CIMSA) | 2011年

关键词：

Particle Swarm Optimization (PSO); Hidden Markov Model; Optimization; Ackley function;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Particle Swarm Optimization (PSO) is an algorithm based on social intelligence, utilized in many fields of optimization. In applications like speech recognition, due to existence of high dimensional matrices, the speed of standard PSO is very low. In addition, PSO may be trapped in a local optimum. In this paper, we introduce a novel algorithm that is faster and generates superior results than the standard PSO. Also, the probability of being trapped in a local optimum is decreased. To illustrate advantages of the proposed algorithm, we use it to train a Hidden Markov Model (HMM) and find the minimum of the Ackley function.

引用

页码：146 / 149

页数：4

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