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[source in ebook] 网络游戏Use Case框图的规划设计 DL : 0: <VC++网络游戏建摸与实现>源代码-lt; VC network games modeling and implementation gt; Source code
Update : 2025-02-19 Size : 25kb Publisher : 韦功成
[source in ebook] BPNN DL : 0: 由于BP网络的权值优化是一个无约束优化问题，而且权值要采用实数编码，所以直接利用Matlab遗传算法工具箱。以下贴出的代码是为一个19输入变量，1个输出变量情况下的非线性回归而设计的，如果要应用于其它情况，只需改动编解码函数即可。 -As a result of BP network weights optimization is a constrained optimization problems, and weights to be used real-coded, so the direct use of Matlab genetic algorithm toolbox. Posted the following code is for a 19 input variables, an output variable in case of non-linear regression designed, if applied to other situations, simply change your codec function.
Update : 2025-02-19 Size : 2kb Publisher : 王刚
[AI-NN-PR] GA-BP-neural DL : 1: 由于BP网络的权值优化是一个无约束优化问题，而且权值要采用实数编码，所以直接利用Matlab遗传算法工具箱。以下贴出的代码是为一个19输入变量，1个输出变量情况下的非线性回归而设计的，如果要应用于其它情况，只需改动编解码函数即可。程序需要调用gaot工具箱.-As a result of BP network weights optimization is a constrained optimization problems, and weights to be used real-coded, so the direct use of Matlab genetic algorithm toolbox. Posted the following code is for a 19 input variables, an output variable in case of non-linear regression designed, if applied to other situations, simply change your codec function. Procedures need to call gaot toolbox.
Update : 2025-02-19 Size : 4kb Publisher :
[matlab] GA-based_Inversion_Example DL : 1: 面波反演的一个例子。。。。里面有文件说明相信大家会好好利用的-Surface wave inversion of a case in point. . . . There are documents that I believe we will make good use of the
Update : 2025-02-19 Size : 64kb Publisher : yanel
[Software Engineering] TSPwith DL : 0: this the TSP problem with input GA algoritm that get input from matrix,i put test case too, you can use them-this is the TSP problem with input GA algoritm that get input from matrix,i put test case too, you can use them...
Update : 2025-02-19 Size : 294kb Publisher : Navid Yamini
[AI-NN-PR] 189-199 DL : 0: As the compact design of a muffler system within a constrained environment of a existing machine room becomes obligatory, it also becomes essential to maximize the acoustic performance of mufflers under space constraints. In this paper, the shape optimization of a double-chamber muffler with an extended tube is presented. The main characteristic of the solution methodology is the use of genetic algorithm (GA) as the optimizer. In the paper, the acoustic perfor- mance of sound transmission loss (STL) derived by transfer matrix is conjugated with the techniques of GA searching. A numerical case-As the compact design of a muffler system within a constrained environment of a existing machine room becomes obligatory, it also becomes essential to maximize the acoustic performance of mufflers under space constraints. In this paper, the shape optimization of a double-chamber muffler with an extended tube is presented. The main characteristic of the solution methodology is the use of genetic algorithm (GA) as the optimizer. In the paper, the acoustic perfor- mance of sound transmission loss (STL) derived by transfer matrix is conjugated with the techniques of GA searching. A numerical case
Update : 2025-02-19 Size : 443kb Publisher : payal
[Other] 30bus DL : 0: You cab use this source code to optimal capacitor locating with the aid of GA. Case system is Ieee-30bus.
Update : 2025-02-19 Size : 3kb Publisher : mehdi
[matlab] ga-wind-prediction DL : 0: 运用GA遗传算法优化BP网络，对风电功率进行预测，含实际数据和案例。-GA genetic algorithm to optimize the use of BP network prediction for wind power, with actual data and case studies.
Update : 2025-02-19 Size : 14kb Publisher : Victoria
[matlab] GA-and-PSO DL : 0: GA遗传算法与PSO离子群组合算法matlab程序，以下是使用本程序的简单介绍与使用步骤 1）修改设计变量个数 NPar变量定义的是设计变量个数，本例为8，使用时需根据自己实际情况进行修改。 2）修改设计变量的上下限 VarLow 与VarHign定义的是设计变量的上下限，使用时需根据自己实际情况进行修改。记住，变量的维数，要与1）变量个数一致哦。 3）修改FunName 变量FunName定义的是优化目标函数值的计算函数，根据自己实际情况改成自己的函数名。 4）修改最大迭代次数MaxIterations 要根据自己的问题实际，通过试算找出合适的MaxIterations数。如果，你对GA与PSO比较精通，还可以通过修改KeepPercent、CrossPercent来提高算法的效率，但是对于初学者来说，上述的步骤与操作已经足够，所以其它不再赘述。祝你好运！ -GA genetic algorithm and PSO Ion combination algorithm matlab program, the following is a brief description of the use of this procedure with the use of step a) to modify the design variables are defined by the number of NPar variable number of design variables, in this case eight, when used according to need their actual situation changes. 2) modify the design variables on the lower limit is defined VarLow and VarHign upper and lower limits of design variables need to be modified when used according to their actual situation. Remember, dimensions variable, the number to be with a) variable consistency oh. 3) Modify FunName variable FunName definition is to optimize the objective function value calculation function, according to their actual situation into their own function name. 4) Modify the maximum number of iterations MaxIterations issue according to their actual, through spreadsheets to find the appropriate number MaxIterations. If you are more proficient GA and PSO, but also can
Update : 2025-02-19 Size : 121kb Publisher : 天天
[matlab] ANN-GA DL : 0: 基于matlab的神经网络和遗传算法的案例和代码源，供大家参考和套用使用。-The case of neural network and genetic algorithm based on matlab and the source code, for your reference and to use. Please enter the text to be translated
Update : 2025-02-19 Size : 21.56mb Publisher : 赵天牧
[matlab] the-typical-case-of-GA DL : 0: 遗传算法的典型案例，适合MATLAB初学者使用。-A typical case of genetic algorithm, suitable for beginners to use MATLAB.
Update : 2025-02-19 Size : 5kb Publisher : zhou