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

1003- Improving collaborative filtering recommender system results and performance using genetic al

مقاله شبیه سازی شده

کد پروژه: 1003

 

موضوع:الگوریتم ژنتیک 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:

Improving collaborative filtering recommender system results and performance

using genetic algorithms 

آدرس:sciencedirect

خلاصه:

Abstract

This paper presents a metric to measure similarity between users, which is applicable in collaborative filtering processes carried out in recommender systems. The proposed metric is formulated via a simple linear combination of values and weights. Values are calculated for each pair of users between which the similarity is obtained, whilst weights are only calculated once, making use of a prior stage in which a genetic algorithm extracts weightings from the recommender system which depend on the specific nature of the data from each recommender system. The results obtained present significant improvements in prediction quality, recommendation quality and performance.

 

Highlights

 

►Metric formulated via a simple linear combination of values and weights. ► Model-based approach using genetic algorithms to improve results. ► Collaborative filtering predictions accuracy and performance improvements.

 

Keywords

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

سفارش پروژه

1002- Optimal brushless DC motor design using genetic algorithms

مقاله شبیه سازی شده

کد پروژه: 1002

 

موضوع:الگوریتم ژنتیک 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:

Optimal brushless DC motor design using genetic algorithms

 

آدرس:sciencedirect

خلاصه:

Abstract

This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.

Keywords

Optimization;  Brushless DC;  Motor;  Slotless;  Radial flux;  Genetic algorithm;  Surface mounted; Magnet

سفارش پروژه