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微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。,Particle Swarm Opt
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介绍模糊控制，智能控制，神经控制和遗传控制系统的书籍-Introduction of fuzzy control, intelligent control, neural control and the genetic control system for books
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MATLAB在遗传模糊控制系统仿真中的应用-MATLAB in the genetic fuzzy control system simulation
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< 控制系统计算机辅助设计--MATLAB语言与应用>>系统地介绍了国际控制界最流行的控制系统计算机辅助设计语言MATLAB，侧重于介绍MATLAB语言编程基础与技巧、数学问题的MATLAB求解、线性系统计算机辅助分析、控制系统与其他复杂系统的Simulink建模，控制系统的计算机辅助设计方法，包括串联控制器、状态反馈控制器、多变量系统频域设计、PID控制器设计、最优控制器设计、LQG/LTR控制器设计、H2=H1 最优控制、分数阶控制、自适应控制、模糊控制、神经网络控制、遗传
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传统PID控制器通常难以满足多变量、非线性、强耦合的步进电机动态响应和精
确调速要求,结合传统PID控制和模糊控制及遗传算法(GA)整定PID参数的优点,设计基于
模糊遗传算法的实时自适应步进电动机PID控制器,充分发挥传统和智能控制策略各自的优
势。仿真结果表明,该实时自适应步进电动机PID控制器,具有很好的自适应能力和抗负载扰
动能力。在稳定性、动态速度响应诸方面均优于传统的PID控制器和模糊控制器,系统达到了
较高调速性能和控制精度。
-Traditional PI
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同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域，基于Java语言实现。-Compared with the genetic algorithm, PSO has the advantage is simple and easy and there is no need to adjust many parameters. Has been widely applied to function o
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基于遗传算法与模糊系统的应用研究概述,自己查阅资料总结的，感觉很不错！-Based on Genetic Algorithms and Fuzzy System Applied Research, summarized their data access, feels really good!
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This simulation demonstrates the use of the PMSM in a closed-loop speed and current control on a 1.1 kW 3000 rpm industrial motor. A three-phase motor rated 1.1 KW, 220 V, 3000 rpm is fed by a PWM inverter. In this simulation, the whole system is bui
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a design methodology is introduced that
blends the classical PID and the fuzzy controllers in an
intelligent way and thus a new intelligent hybrid
controller has been achieved. Basically, in this design
methodology, the classical PID and fuzz
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PSO算法简单、易实现且参数较少，现已被应用于函数优化、神经网络训练、模糊系统控制以及其它遗传算法的应用领域-PSO algorithm is simple, easy to implement and less parameters, have been applied to function optimization, neural network training, fuzzy system control and other applications of genetic algorit
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粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究。
PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。
同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练
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In this paper a Conventional PD Controller and Fuzzy Neural Controller for trajectory tracking of mobile robot
platform is presented. Using computed torque method is given input- torque on mobile robot wheels. The
locomotion control structure is
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Design a feedforward network is u
dehaze algorithms review
path planning using GA and ACO...
The neural network adaboost stron
TS neural network m files, fast c
bp neural network, written with C
Bayes net and memory based learni
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Design a feedforward network is u
dehaze algorithms review
path planning using GA and ACO...
The neural network adaboost stron
TS neural network m files, fast c
bp neural network, written with C
Bayes net and memory based learni
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A GENETIC-BASED FUZZY LOGIC POWER SYSTEM STABILIZER FOR
MULTIMACHINE POWER SYSTEMS
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Design of Genetic Algorithm Based Fuzzy
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In this paper,we address the effect of illumination on the face detection and the face recognition problem by introducing a novel illumination invariant method, called OptiFuzz. It is an optimized fuzzy-based illumination invariant method to solve th
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粒子群优化算法的基本思想是通过群体中个体之间的协作和信息共享来寻找最优解．
PSO的优势在于简单容易实现并且没有许多参数的调节。目前已被广泛应用于函数优化、神经网络训练、模糊系统控制以及其他遗传算法的应用领域。-The basic idea of Particle Swarm Optimization (PSO) is to find the optimal solution by cooperating and sharing information among individuals.
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A Genetic Fuzzy System (GFS) is basically a fuzzy system augmented by a learning process based on a Q3
genetic algorithm (GA). Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Curren
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Genetic Fuzzy System (GFS) based Wavelet
Co-occurrence Feature selection in Mammogram
Classification for Breast Cancer Diagnosis
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