1004- Parameter identification of chaotic dynamic systems throughan improved particle swarm optimiza

مقاله شبیه سازی شده

کد پروژه: 1004

 

موضوع:الگوریتم pso و سیستم آشوب

 

شامل:مقاله اصلی + فایل شبیه سازی با نرم افزار متلبMatlab +گزارش کامل از خلاصه ای از مقاله و نتایج شبیه سازی

 

عنوان مقاله:

Parameter identification of chaotic dynamic systems throughan improved particle swarm optimization

آدرس:sciencedirect

خلاصه:

Abstract

This paper is concerned with the parameter identification problem for chaotic dynamic systems. An improved particle swarm optimization (IPSO), which is a novel evolutionary computation technique, is proposed to solve this problem. The feasibility of this approach is demonstrated through identifying the parameters of Lorenz chaotic system. The performance of the proposed IPSO is compared with the genetic algorithm (GA) and standard particle swarm optimization (SPSO) in terms of parameter accuracy and computational time. It is illustrated in simulations that the proposed IPSO is more successful than the SPSO and GA. IPSO is also improved to detect and determine the variation of parameters. In this case, a sentry particle is introduced to detect any changes in system parameters and if any change is detected, IPSO runs to find new optimal parameters. Hence, the proposed algorithm is a promising particle swarm optimization algorithm for system identification.

Keywords

Collaborative filtering; Recommender systems; Similarity measures; Metrics; Genetic algorithms; Performance

 

سفارش پروژه

1001_ Application of an improved PSO algorithm to optimal tuning of PID gains for water turbinegover

مقاله شبیه سازی شده

کد پروژه: 1001

موضوع:الگوریتم PSO و کنترل بهینه

شامل:مقاله اصلی + فایل شبیه سازی با نرم افزار متلبMatlab +گزارش کامل از خلاصه ای از مقاله و نتایج شبیه سازی

عنوان مقاله:

Application of an improved PSO algorithm to optimal tuning of PID gains for water turbinegovernor

آدرس:sciencedirect

خلاصه:

Abstract

In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed. Besides the individual best position and the global best position, a nominal average position of the swarm is introduced in IPSO. The performance of IPSO is compared to different PSO variants with five well-known benchmark functions. The experimental results show that the proposed IPSO algorithm improves the searching performance on the benchmark functions. And then, IPSO, as well as other PSO variants, is applied to optimal tuning of Proportional–Integral–Derivative (PID) gains for a typical PID control system of water turbine governor. The computer simulation results of an actual hydro power plant in China show that IPSO algorithm has stable convergence characteristic and good computational ability, and it is an effective and easily implemented method for optimal tuning of PID gains of water turbine governor.

 

سفارش پروژه