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[AI-NN-PR] NEA DL : 0: 针对现有遗传算法在多维非线性优选方面的不足，本文提出了一种基于小生境进化算法（NEA）的非线性优选模型，探讨了NEA算法的参数选择原则。通过大量仿真和比较，表明算法在复杂非线性优选中具有快速、高效、鲁棒性强的特点，并能在全局范围内有效搜索所有最优解。 -against existing genetic algorithms in three-dimensional nonlinear optimization for the shortage, the paper presents a niche evolutionary algorithm (NEA) nonlinear optimization model, the NEA on the parameters chosen algorithm principle. Through simulation and large, the algorithm shown in a complex nonlinear optimization is fast, efficient, robust features of the strong, and the global scope effective search all the optimal solution.
Date : 2025-12-29 Size : 37kb User : 黄善理
[AI-NN-PR] MATLABGAOT DL : 0: 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.
Date : 2025-12-29 Size : 626kb User : dh
[AI-NN-PR] intelligent_control_modle DL : 0: 本人调试的大作业（源程序模型+论文），主要包括PID控制、模糊控制、神经网络控制、遗传算法优化神经网络控制（使用了遗传工具箱GAOT）对同一系统所作的仿真比较，并加入饱和、死区、时滞等非线性后的响应，具体的分析比较过程论文中写的很详细。-I debug a big operation (source model+ Thesis), including PID control, fuzzy control, neural network control, genetic algorithm optimization of neural network control (using a genetic toolbox GAOT) on the same system simulation by the comparison, and to join saturation, dead zones, such as nonlinear time-delay after the response, the specific analysis of the comparison process paper written in great detail.
Date : 2025-12-29 Size : 277kb User : hcnden
[AI-NN-PR] MATLAB_genetic DL : 0: 遗传算法工具箱在线性优化和非线性规划中的应用-Genetic algorithm optimization toolbox of linear and nonlinear Planning
Date : 2025-12-29 Size : 205kb User : 刘刘
[AI-NN-PR] jizhizuiyou DL : 0: 已调试好的神经网络遗传算法函数极值寻优-非线性函数极值的算法程序-Has debugging function neural network genetic algorithm optimization extreme- extreme nonlinear function of the algorithm
Date : 2025-12-29 Size : 100kb User : 胡盛亮
[AI-NN-PR] matlab1 DL : 0: 神经网络遗传算法函数极值寻优--非线性函数极值代码，内附有详细代码，详细请参考matlab在数学建模中的应用。-Neural network genetic algorithm function optimization extreme- extreme nonlinear function code, containing a detailed code, matlab details please refer to the application of mathematical modeling.
Date : 2025-12-29 Size : 100kb User : 陈虹志
[AI-NN-PR] genetic-algorithm-and-nonlinea DL : 0: 基于遗传算法和非线性规划的函数寻优算法在一组等式或者不等式的约束下求极值。基于遗传算法和非线性规划的函数寻优算法在一组等式或者不等式的约束下求极值。-Based on genetic algorithm and nonlinear programming function optimization algorithm in a set of equations or inequality constraints for extreme value. Based on genetic algorithm and nonlinear programming function optimization algorithm In a set of equations or inequality constraints for extreme value.
Date : 2025-12-29 Size : 5kb User : 王进
[AI-NN-PR] bpycsf DL : 0: 遗传算法优化BP神经网络-非线性函数拟合-Genetic algorithm optimization BP neural network nonlinear function approximation to ensure availability
Date : 2025-12-29 Size : 52kb User : 李宝强
[AI-NN-PR] bpycl1 DL : 0: 神经网络遗传算法函数极值寻优-非线性函数极值-Neural network genetic algorithm function optimization nonlinear function extreme value
Date : 2025-12-29 Size : 99kb User : 李宝强
[AI-NN-PR] Genetic_BP DL : 0: 根据神经网络和遗传算法原理，在MATLAB中编程实现神经网络遗传算法非线性函数寻优-Nonlinear function of the neural network, genetic algorithm optimization based on neural network and genetic algorithm theory, programming in MATLAB
Date : 2025-12-29 Size : 99kb User : 阿俊
[AI-NN-PR] GeneticNonlinearMatlab DL : 0: 遗传算法虽然全局搜索能力较强，但是局部搜索能力较弱，一般只能搜索到函数优化问题的次优解，而不是最优解，特别是函数具有多个峰值时，遗传算法易陷入局部极小，不能找到真正的全局最优解。非线性规划因多采用梯度下降方法求解，而具有极强的局部搜索能力。因此，本源代码结合两种算法的优点，一方面采用遗传算法进行全局搜索，另一方面采用非线性规划进行局部搜索，以得到函数优化问题的全局最优解。实验证明，这种方法不仅能解决多峰函数寻优易陷入局部极小的问题，而且具有很高的迭代寻优效率，取得了满意的结果。-Global search ability of genetic algorithms, local search capability is weak, generally only be able to search a sub-optimal solution to the function optimization problem, rather than the optimal solution, especially the function has multiple peaks, genetic algorithm is easy to fall into the local polar can not find the true global optimal solution. Solving nonlinear programming due to the use of the gradient descent method, and has strong local search ability. Source code algorithm combines two advantages, on the one hand, the use of genetic algorithms for global search, on the other hand by nonlinear programming local search to obtain the global optimum function optimization problems. Experiments show that this method can not only solve the multimodal function optimization easy to fall into local minima problems, and has a highly iterative optimization efficiency, and achieved satisfactory results.
Date : 2025-12-29 Size : 43kb User : 乐乐
[AI-NN-PR] Extreme-nonlinear-function DL : 0: 神经网络训练拟合根据寻优函数的特点构建合适的BP神经网络,用非线性函数的输入输出数据训练BP神经网络,训练后的BP神经网络就可以预测函数输出。遗传算法极值寻优把训练后的BP神经网络预测结果作为个体适应度值,通过选择、交叉和变异操作寻找函数的全局最优值及对应输入值。 -Neural network training function fitting based optimization features built right on BP neural network, using non-linear function of the input output data trained BP neural network, the trained BP neural network can predict the function output. The genetic algorithm optimization extreme training BP neural network prediction results as the individual fitness value through selection, crossover and mutation find the global optimal value function and the corresponding input value.
Date : 2025-12-29 Size : 3kb User : 吴军
[AI-NN-PR] Nonlinear-system-modeling DL : 0: 本课题首先根据寻优函数的特点构建合适的BP神经网络,用非线性函数的输入输出数据训练BP神经网络,训练后的BP神经网络就可以预测函数输出。遗传算法极值寻优把训练后的BP神经网络预测结果作为个体适应度值,通过选择、交叉和变异操作寻找函数的全局最优值及对应输入值。 -Neural network training function fitting based optimization features built right on BP neural network, using non-linear function of the input output data trained BP neural network, the trained BP neural network can predict the function output. The genetic algorithm optimization extreme training BP neural network prediction results as the individual fitness value through selection, crossover and mutation find the global optimal value function and the corresponding input value.
Date : 2025-12-29 Size : 4kb User : 吴军
[AI-NN-PR] Genetic-Algorithm DL : 0: 本例应用遗传算法实现了对于一些非线性、多模型、多目标的函数优化问题，用其它优化方法较难求解，而遗传算法可以方便的得到较好的结果。-In this case the genetic algorithm is applied to implement for some nonlinear, model, multi-objective function optimization problem, with other optimization methods are difficult to solve, and the genetic algorithm can easily get good results.
Date : 2025-12-29 Size : 3kb User : 周建
[AI-NN-PR] Optimal-Algorithm DL : 0: 非线性最小二乘优化算法，遗传优化算法，粒子群优化算法，经测试收敛较快，效果不错，需要非线性最优化的可以借鉴-Nonlinear least square optimization algorithm, genetic optimization algorithm, particle swarm optimization algorithm, tested converges faster, effect is good, need to nonlinear optimization can draw lessons from
Date : 2025-12-29 Size : 17kb User : wyg
[AI-NN-PR] Genetic-algorithm-optimization-BP DL : 0: 运用遗传算法优化BP神经网络-来求取非线性函数拟合用图-Using genetic algorithm optimization BP neural network- to strike a nonlinear function fitting Fig
Date : 2025-12-29 Size : 52kb User : weishixiong
[AI-NN-PR] genetic-algorithm DL : 0: 根据遗传算法和BP神经网络理论，在matlab软件中编程实现基于遗传算法优化的BP神经网络非线性系统拟合算法。-According to the theory of genetic algorithm and BP neural network, in the matlab software programming to realize BP neural network nonlinear systems based on genetic algorithm optimization fitting method.
Date : 2025-12-29 Size : 54kb User : 汪杰
[AI-NN-PR] neural-network-and-genetic-algorithm DL : 0: 对于未知的非线性函数，仅通过函数的输入输出数据难以准确寻找函数极值，这类问题可以通过神经网络结合遗传算法求解，利用神经网络的非线性拟合能力和遗传算法的非线性寻优能力寻找函数极值。-For unknown nonlinear function, only through the function of input and output data is difficult to accurately find the function extreme value, this kind of problem can be through the neural network combined with genetic algorithm, using the nonlinear fitting ability of the neural network and nonlinear optimization ability of genetic algorithm to find the function extreme value.
Date : 2025-12-29 Size : 296kb User : 汪杰
[AI-NN-PR] genetic DL : 0: 对于未知非线性函数，利用非线性拟合能力和遗传算法的非线性寻优能力寻找函数极值。-For unknown nonlinear function, non-linear optimization capability by nonlinear fitting ability and genetic algorithm to find the extreme value functions.
Date : 2025-12-29 Size : 100kb User : 王雨薇
[AI-NN-PR] Function optimization algorithm DL : 0: 遗传算法提供了求解非线性规划的通用框架，它不依赖于问题的具体领域。遗传算法的优点是将问题参数编码成染色体后进行优化，而不针对参数本身，从而不受函数约束条件的限制；搜索过程从问题解的一个集合开始，而不是单个个体，具有隐含并行搜索特性，可大大减少陷入局部最小的可能性。而且优化计算时算法不依赖于梯度信息，且不要求目标函数连续及可导，使其适于求解传统搜索方法难以解决的大规模、非线性组合优化问题。(Genetic algorithm provides a general framework for solving nonlinear programming, which does not depend on the specific problem domain. The advantage of genetic algorithm is that the problem parameters are encoded into chromosomes for optimization, rather than the parameters themselves. The search process starts from a set of problem solutions, rather than a single individual, and has the implicit parallel search feature, which can greatly reduce the possibility of falling into the local minimum. Moreover, the algorithm does not rely on gradient information and does not require the objective function to be continuous and differentiable, which makes it suitable for solving large-scale and nonlinear combinatorial optimization problems that are difficult to be solved by traditional search methods.)
Date : 2025-12-29 Size : 33kb User : FZenjoys

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