Description: ：介绍遣传算法的基本原理和Matlab的遗传算法优化工具箱(GAOT)，分析了优化工具函数。探讨Matlab遗传算法工具箱在
参数优化和非线性规划中的应用。通过优化实例，说明遗传算法是一种具有良好的全局寻优性能的优化方法。用Maflab语
言及Maflab语言编制的优化工具箱进行优化设计具有语言简单、函数丰富、用法比较灵活、编程效率高等特点。-: Removal algorithm introduce the basic principles and Matlab Genetic Algorithm Optimization Toolbox (GAOT), an analysis of function optimization tool. Explore the Matlab Genetic Algorithm Toolbox in the parameter optimization and nonlinear programming applications. Through optimization examples to illustrate the genetic algorithm is a good global optimization method to optimize performance. Maflab language and using language Maflab Optimization Toolbox to optimize the design of the language is simple, function-rich, and using more flexible programming and high efficiency. Platform: |
Size: 210944 |
Author:icyrock |
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Description: 简要阐述了遗传算法的基本原理，并对O0PQ0R 遗传算法工具箱（"0SP）的参数进行
了详细的介绍。探讨了O0PQ0R 遗传算法工具箱在参数优化和非线性规划中的应用，实例证明了遗
传算法在参数优化和非线性规划中的可行性。-Briefly described the basic principles of genetic algorithms and genetic algorithms O0PQ0R Toolbox ( 0SP) the parameters in detail. O0PQ0R explored the genetic algorithm toolbox in the parameter optimization and nonlinear programming applications, examples of proven genetic algorithm in parameter optimization and nonlinear programming feasibility. Platform: |
Size: 641024 |
Author:dh |
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Description: 在实时平台上,高斯混合模型(GMM)具有计算有效性和易于实现的优点。最大似然规则中,模型参数不
断更新,但由于爬山特征,任意的原始模型参数估计通常将导致局部最优 遗传算法(GA)适于求解复杂组合优化问
题及非线性函数优化。提出了基于说话人识别的可以解决GMM局部最优问题的GMM/GA新算法,实验结果表明,
提出的GMM/GA新算法比纯粹的GMM算法能获得更优的效果。
- In real-time platform, the Gaussian mixture model (GMM) with the calculation of the effectiveness and easy to realize benefits. Maximum likelihood rule, the model parameters are not
Broken updates, but due to climbing features, any of the original model parameter estimation will usually result in local optimum genetic algorithm (GA) is suitable for solving complex combinatorial optimization question
Title and non-linear function optimization. Proposed speaker recognition based on GMM can solve the problem of local optimal GMM/GA new algorithm, experimental results show that the
Proposed GMM/GA new algorithm than purely GMM algorithm can get better results. Platform: |
Size: 4448256 |
Author:于高 |
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Description: 人工神经网络(Artificial Neural Network)是从生理角度对智能的模拟，具有极
高的学习能力和自适应能力，能够以任意精度逼近任意函数，完成对系统的仿真；
而遗传算法是对自然界生物进化过程的模拟，具有极强的全局寻优能力，这两种
算法都是当下研究较多的智能方法。将这两种方法与常规的 PID 控制相结合，
构成智能 PID 控制器，使其具有参数自整定、自适应的能力，以适应复杂环境
下的控制要求，这一思路对提高控制效果具有很好的现实意义。
-Artificial Neural Network (ANN) is an imitation of the intelligence by the point of physiological. It has a high capacity of learning and adaptive, can approximate any function to arbitrary accuracy, and complete the simulation of the system. The Genetic
algorithm is a simulation of natural biological evolution, which has a strong ability of
global optimization. These two algorithms are more intelligent method of current
research. The idea of combining these two methods with the conventional PID
controller to be a intelligent controller with the abilities of parameter auto-tuning and
adaptive for the requirements of the complex environment, has a high practical
significance of improving the control effect.
Platform: |
Size: 661504 |
Author:baijiaxuan |
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Description: 遗传算法是1962年由美国Michigan大学的Holland教授提出的模拟自然界遗传机制和生物进化论而形成的一种并行随机搜索最优化方法[1-3]，
在自动控制领域中得到了越来越广泛的应用。该文引入了“稳定区域算法”求取闭环系统稳定的PID控制器参数区间，并以此算法的计算结果限定进
化算法的参数寻优区间，通过仿真试验取得了令人满意的控制效果。-Genetic Algorithms is proposed by the University of Michigan’s Professor Holland in the United
States in 1962 which is Analog nature of genetic mechanisms and biological theory of evolution and the
formation of a parallel random search optimization method [1-3], in the field of automatic control has been
more and more wide range of applications. In this paper, the introduction of the "stable region algorithm" to
strike a closed-loop system stability of interval parameter PID controller, and the results of this algorithm
for calculating the parameters of limited evolutionary algorithm optimization interval, through the simulation
achieved satisfactory control effect. Platform: |
Size: 888832 |
Author:baijiaxuan |
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Description: matlab编写的遗传算法案例，减振器速度特性的参数识别和优化-Matlab prepared by the genetic algorithm case, parameter identification and optimization of the velocity characteristics of shock absorbers Platform: |
Size: 1024 |
Author:妙风 |
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Description: The paper presents an in situ parameter estimation method to determine the equivalent circuit parameters of the three-winding
transformer (TWT). The suggested method also estimates geometrically a complex parameter that is mutual leakage between
secondary and tertiary windings, which would be useful in transient studies. Beside the saturation effect of the transformer is taken
into account by estimating a highly nonlinear parameter that is magnetizing circuit reactance. Different search based optimization
tools are applied for parameter estimation among which the results obtained using genetic algorithm is found to be encouraging.
Load test data at one particular operating point namely supply voltage, load currents, input power, and load impedance is suffi cient
to estimate the parameters. Platform: |
Size: 129024 |
Author:fouzirock |
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Description: a b s t r a c t
A new metaheuristic method, the Cuckoo Search (CS) algorithm, based on the life of a bird family is proposed in this paper for optimal design of static synchronous compensator (STATCOM) in a multimachine environment. PV curves are illustrated to determine the best location of STATCOM. The STATCOM parameter tuning problem is converted to an optimization problem which is solved by CS Algorithm. The
performance of the proposed CS based STATCOM (CSSTATCOM) is compared with Genetic Algorithm (GA) based STATCOM (GASTATCOM) and open loop STATCOM under various operating conditions and disturbances.The superiority of the proposed technique in damping oscillations and enhancing voltage profiles is confirmed through igenvalues and time domain simulation results over the GA and open loop one Platform: |
Size: 911360 |
Author:elijj56 |
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Description: 将改进的粒子群算法和GA与SVM相结合，通过参数寻优构建新模型完成对空气质量指数的预测(The improved particle swarm optimization and genetic algorithm are combined with SVM. The prediction of air quality index (AQI) is completed by constructing a new model by parameter optimization.) Platform: |
Size: 19456 |
Author:心静2279 |
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Search - parameter optimization by genetic algorithm