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BP神经网络预测
BP神经网络预测-BP neural network prediction
- 2022-01-25 21:19:01下载
- 积分：1
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最邻近的RBF算法,MATLAB实现,较为有价值的资料.
最邻近的RBF算法,MATLAB实现,较为有价值的资料.-The nearest RBF algorithm, MATLAB realize, the more valuable information.
- 2022-02-05 04:23:48下载
- 积分：1
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关于求最值的delphi遗传算法源码，绝对可以用，调试通过，界面美观，由王小平书上的c代码改编所成，创建了一个ga类，向构造函数输入合适的参数就可以运算！...
关于求最值的delphi遗传算法源码，绝对可以用，调试通过，界面美观，由王小平书上的c代码改编所成，创建了一个ga类，向构造函数输入合适的参数就可以运算！-seek the most value on the delphi genetic algorithm source code can definitely use, debug, beautiful interface, Wang Xiaoping book adaptation of the c code by 10%, creating a ga category Constructors input to the appropriate Operational parameters can!
- 2022-02-14 09:53:05下载
- 积分：1
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支持向量机的最新的工作工具，它将是非常方便的。
最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it.
- 2023-08-17 13:35:03下载
- 积分：1
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遗传算法求解优化问题，简明教程加示例
遗传算法求解优化问题，简明教程加示例- The heredity algorithm solution optimization question, the
concise course adds the demonstration
- 2022-05-25 15:35:31下载
- 积分：1
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基于canny的灰色预测和模式识别,标定的一种做法是：在一块平板上画若干个点， 同时用这两部相机照相，分别得到这些点在它们像平面上的像点，利用这两组像点的几何关...
基于canny的灰色预测和模式识别,标定的一种做法是：在一块平板上画若干个点， 同时用这两部相机照相，分别得到这些点在它们像平面上的像点，利用这两组像点的几何关系就可以得到这两部相机的相对位置。然而，无论在物平面或像平面上我们都无法直接得到没有几何尺寸的“点”。实际的做法是在物平面上画若干个圆（称为靶标），它们的圆心就是几何的点了。而它们的像一般会变形，如图1所示，所以必须从靶标上的这些圆的像中把圆心的像精确地找到，标定就可实现。-Based on the gray canny prediction and pattern recognition, calibration approach is to: a plate painted in a number of points, at the same time with these two cameras camera, respectively, as these points in their plane as points, the use of these two groups geometric relationship between the image point can be that the relative positions of two cameras. However, in terms of objects such as plane or plane, we can not directly be no geometry point. Actual practice is complex plane painted in a number of round (known as targets), their geometric center of a circle is the point. And they generally like deformation, as shown in Figure 1, it must target these round, like in the center of a circle of like to find precise, calibration can achieve.
- 2022-11-16 16:00:04下载
- 积分：1
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svm Main_SVR_Epsilon 用于svm回归的 在matlab中运用
svm Main_SVR_Epsilon 用于svm回归的 在matlab中运用 -svm regression svm Main_SVR_Epsilon for use in matlab
- 2022-06-29 03:53:35下载
- 积分：1
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这个软件支持Bayesian衰退和分类模形，它基于神经系统网络和Gaussian作用。它也包括一些根本的程序实现有限和无限混合的模型。...
这个软件支持Bayesian衰退和分类模形，它基于神经系统网络和Gaussian作用。它也包括一些根本的程序实现有限和无限混合的模型。-software support this recession and Bayesian classification pattern, It is based on neural network and Gaussian role. It also includes some of the procedures to achieve limited and unlimited mixed model.
- 2022-10-07 11:20:03下载
- 积分：1
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Neural network and C++ Source code (BP), very good number of routines, for infor...
神经网络和C++源代码(BP)，非常好的一些例程，仅供参考。-Neural network and C++ Source code (BP), very good number of routines, for informational purposes only.
- 2022-01-28 10:58:44下载
- 积分：1
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由于K－均值聚类算法局部最优的特点，而模拟退火算法理论上具有全局最优的特点。因此，用模拟退火算法对聚类进行了改进。20组聚类仿真表明，平均每次对K结果值改进8次...
由于K－均值聚类算法局部最优的特点，而模拟退火算法理论上具有全局最优的特点。因此，用模拟退火算法对聚类进行了改进。20组聚类仿真表明，平均每次对K结果值改进8次左右，效果显著。下一步工作：实际上在高温区随机生成邻域是个组合爆炸问题（见本人上载软件‘k－均值聚类算法’所述），高温跳出局部解的概率几乎为0，因此正考虑采用凸包约束进行模拟聚类，相关工作正在进行。很快将奉献给各位朋友。-as K-means clustering algorithm for optimal local characteristics, and simulated annealing algorithm theory with the characteristics of the global optimum. Thus, simulated annealing algorithm for clustering improvements. Cluster Group of 20 simulations show that the average value of K results improved about eight times, the results are obvious. The next step : In fact, in high temperature generated random neighborhood is a combination of explosives (see my software on the "k-means clustering algorithm" mentioned above), high-temperature solution of partial out almost zero probability, it is considering the use of convex hull bound for simulation cluster, the work under way . Soon dedication to the ladies.
- 2023-05-08 08:05:03下载
- 积分：1
