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第2章

于 2018-06-28 发布文件大小:100KB

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《精通MATLABSimulink系统仿真》主要分为三大部分，共15章。第1部分主要介绍MATLAB基础知识、Simulink仿真入门、 Simulink模型的建立与仿真、Simulink常用命令库分析等；第二部分主要为基于Simulink的S-Function 建模、控制系统Simulink仿真、基于PID的控制系统仿真等；第三部分则涉及Simulink高级应用，包括模糊逻辑控制仿真、电力系统仿真、通信系统仿真、神经网络控制仿真、滑模控制、车辆系统仿真、群智能算法控制系统仿真等。(The MATLABSimulink system simulation is mainly divided into three parts, with 15 chapters. The first part mainly introduces the basic knowledge of MATLAB, the introduction of Simulink simulation, the establishment and Simulation of the Simulink model, the analysis of the common command base of Simulink, and so on. The second part mainly consists of Simulink based S-Function modeling, the control system Simulink simulation, and the PID based control system simulation, and the third part deals with the Simulink senior should. It includes fuzzy logic control simulation, power system simulation, communication system simulation, neural network control simulation, sliding mode control, vehicle system simulation, swarm intelligence algorithm control system simulation and so on.)

文件列表：

第2章\ysw2_1.slx, 13274 , 2014-06-05
第2章\ysw2_2.m, 583 , 2014-06-05
第2章\ysw2_3.slx, 14382 , 2014-06-08
第2章\ysw2_4.slx, 15623 , 2014-06-08
第2章\ysw2_5.slx, 16690 , 2014-06-08
第2章\ysw2_6.slx, 16899 , 2014-06-08
第2章\ysw2_7.slx, 14254 , 2014-06-08
第2章\ysw2_8.m, 1059 , 2014-06-10
第2章\ysw2_9.slx, 12928 , 2014-06-10

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The optimization behavior of the self-adaptation (SA) evolution strategy (ES) with intermediate multirecombination (the (=I )-SA-ES) using isotropic mutations is investigated on the general elliptic objective function. An asymptotically exact quadratic progress rate formula is derived. This is used to model the dynamical ES system by a set of difference equations. The solutions of this system are used to analytically calculate the optimal learning parameter . The theoretical results are compared and validated by comparison with real (=I )-SA-ES runs on typical elliptic test model cases. The theoretical results clearly indicate that using a model-independent learning parameter leads to suboptimal performance of the (=I )-SA-ES on objective functions with changing local condition numbers as often encountered in practical problems with complex fitness landscapes.

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