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Description: 用RBF实现的目标图象识别程序,C实现,可以应用于DSP系统.-RBF realize the goal of using the image recognition program, C realize, can be used in DSP systems.
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Size: 352256 |
Author: 安平 |
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Description: PSO的RBFNN优化程序
算法步骤
1.样本数据归一化处理，即将输入输出归一化到[-1,1]区间；
2.确定RBF网络的中心和宽度；
3.以拟合误差的均方根作为性能指标，使用PSO算法优化RBF网络输出层到隐层的连接权值矩阵-PSO-RBFNN algorithm optimization procedures Step 1. Sample data normalization treatment, about input and output normalized to [-1,1] interval 2. To determine the center and width of the RBF network 3. To the fitting error of the mean square roots as a performance index, using the PSO algorithm to optimize RBF network output layer to hidden layer connection weight matrices
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Size: 4096 |
Author: aKON |
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Description: -s svm类型：SVM设置类型(默认0)
0 -- C-SVC
1 --v-SVC
2 – 一类SVM
3 -- e -SVR
4 -- v-SVR
-t 核函数类型：核函数设置类型(默认2)
0 – 线性：u v
1 – 多项式：(r*u v + coef0)^degree
2 – RBF函数：exp(-r|u-v|^2)
3 –sigmoid：tanh(r*u v + coef0)
-d degree：核函数中的degree设置(针对多项式核函数)(默认3)
-g r(gama)：核函数中的gamma函数设置(针对多项式/rbf/sigmoid核函数)(默认1/ k)
-r coef0：核函数中的coef0设置(针对多项式/sigmoid核函数)((默认0)
-c cost：设置C-SVC，e -SVR和v-SVR的参数(损失函数)(默认1)
-n nu：设置v-SVC，一类SVM和v- SVR的参数(默认0.5)
-p p：设置e -SVR 中损失函数p的值(默认0.1)
-m cachesize：设置cache内存大小，以MB为单位(默认40)
-e eps：设置允许的终止判据(默认0.001)
-h shrinking：是否使用启发式，0或1(默认1)
-wi weight：设置第几类的参数C为weight*C(C-SVC中的C)(默认1)
-v n: n-fold交互检验模式，n为fold的个数，必须大于等于2--s svm_type : set type of SVM (default 0)
0-- C-SVC
1-- nu-SVC
2-- one-class SVM
3-- epsilon-SVR
4-- nu-SVR
-t kernel_type : set type of kernel function (default 2)
0-- linear: u *v
1-- polynomial: (gamma*u *v+ coef0)^degree
2-- radial basis function: exp(-gamma*|u-v|^2)
3-- sigmoid: tanh(gamma*u *v+ coef0)
4-- precomputed kernel (kernel values in training_instance_matrix)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/k)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)
-b
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Size: 17408 |
Author: little863 |
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Description: 图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。
其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？
用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。
在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。-Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set.
In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem?
After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified.
Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram
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Size: 3585024 |
Author: lipiji |
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Description: 包含RBF神经网络、粒子群算法和L-D（杜宾）算法的源程序-Including RBF neural network, particle swarm algorithm and L-D (Doberman) algorithm source
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Size: 6144 |
Author: 陈梦 |
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Description: 概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出，是径向基网络的一个分支，属于前馈网络的一种。它具有如下优点：学习过程简单、训练速度快；分类更准确，容错性好等。从本质上说，它属于一种有监督的网络分类器，基于贝叶斯最小风险准则。(Probabilistic neural network was first proposed by Dr. D.F.Speeht in 1989. It is a branch of radial basis networks and belongs to a feedforward network. It has the following advantages: the learning process is simple, the training speed is fast, the classification is more accurate, and the fault tolerance is good. Essentially, it belongs to a supervised network classifier based on the Bayes minimum risk criterion.)
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Size: 5120 |
Author: gahuan
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Description: 概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出，是径向基网络的一个分支，属于前馈网络的一种。它具有如下优点：学习过程简单、训练速度快；分类更准确，容错性好等。从本质上说，它属于一种有监督的网络分类器，基于贝叶斯最小风险准则。(The rate neural network, first proposed in 1989, is a branch of the RBF network and is one of the feedforward networks. It has the following advantages: the learning process is simple, the training speed is fast, the classification is more accurate, the fault tolerance is good, and so on. In essence, it belongs to a supervised network classifier based on Bayesian minimum risk criteria.)
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Size: 46080 |
Author: 哼哼1214
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Description: pso rbf k-means simulik with matlab(programme d'un pso en hybride avec un rbf)
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Size: 5120 |
Author: hichemhamdi |
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Description: 采用D-H法建立了多自由度机械臂末端执行器相对于基础坐标系的位置与姿态，即多自由度机械臂的正运动学模型。其次对多自由度机械臂的逆运动学模型进行了分析。最后通过采用Langrange法对多自由度机械臂的动力学进行了建模，并对多自由度机械臂的动力学模型及其特点进行了分析。 针对多自由度机械臂数学模型的不确定性问题，提出一种基于RBF神经网络的机械臂自适应控制方法。(The position and attitude of the end effector of the multi DOF Manipulator relative to the basic coordinate system is established by using the D-H method, that is, the forward kinematics model of the multi DOF Manipulator. Secondly, the inverse kinematics model of multi DOF Manipulator is analyzed. In the end, the dynamics model of the multi degree of freedom manipulator is established by using the Langrange method, and the dynamics model and characteristics of the multi degree of freedom manipulator are analyzed. Aiming at the uncertainty of the mathematical model of the multi degree of freedom manipulator, an adaptive control method based on RBF neural network is proposed.)
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Size: 31744 |
Author: 又又ya |
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