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Wrapper Approach for Feature Selections in RBF Network Classifier  [cached]
Novakovic Jasmina
Theory and Applications of Mathematics & Computer Science , 2011,
Abstract: In this paper we investigate the impact of wrapper approach on classification accuracy and performance of RBF network. Wrapper approach used six rule induction algorithms for evaluators on supervised learning algorithms RBF network and tested using eight real and three artificial benchmark data sets. Classification accuracy and performance of RBF network depends on evaluators. Our experimental results indicate that every rule induction algorithms in wrapper approach maintains or improves the accuracy of RBF network for more than half data sets. Evaluation of selecting features with wrappers approach is not so fast compare with filters approach.
A Hybrid Classifier Based on the Rough Sets and RBF Neural Networks
基于粗糙集和RBF神经网络的文本自动分类方法

Bai Rujiang,
白如江

现代图书情报技术 , 2006,
Abstract: This paper presentes a new hybrid classifier based on the combination of rough set theory and RBF neural network. Experimental results show that the algorithm Rough - RBF is effective for the texts classification, and has the better performance in classification precision, stability and fault - tolerance comparing with the traditional classification methods, Bayesian classifiers SVM and kNN, especially for the complex classification problems with many feature vectors.
RBF neural net based classifier for the AIRIX accelerator fault diagnosis  [PDF]
J. C. Ribes,G. Delaunay,J. Delvaux,E. Merle,M. Mouillet
Physics , 2000,
Abstract: The AIRIX facility is a high current linear accelerator (2-3.5kA) used for flash-radiography at the CEA of Moronvilliers France. The general background of this study is the diagnosis and the predictive maintenance of AIRIX. We will present a tool for fault diagnosis and monitoring based on pattern recognition using artificial neural network. Parameters extracted from the signals recorded on each shot are used to define a vector to be classified. The principal component analysis permits us to select the most pertinent information and reduce the redundancy. A three layer Radial Basis Function (RBF) neural network is used to classify the states of the accelerator. We initialize the network by applying an unsupervised fuzzy technique to the training base. This allows us to determine the number of clusters and real classes, which define the number of cells on the hidden and output layers of the network. The weights between the hidden and the output layers, realising the non-convex union of the clusters, are determined by a least square method. Membership and ambiguity rejection enable the network to learn unknown failures, and to monitor accelerator operations to predict future failures. We will present the first results obtained on the injector.
Topological invariance of the sign of the Lyapunov exponents in one-dimensional maps  [PDF]
Henk Bruin,Stefano Luzzatto
Mathematics , 2004,
Abstract: We explore some properties of Lyapunov exponents of measures preserved by smooth maps of the interval, and study the behaviour of the Lyapunov exponents under topological conjugacy.
Sign-changing Lyapunov functions in linear extensions of dynamical systems  [cached]
Victor Kulik,Ewa Tkocz-Piszczek
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica , 2008,
Abstract: In this note we consider sets of linear extensions of dynamical systems on a torus. We examine regularity of the systems by means of a given sign-changing Lyapunov function. The main result of the paper is to give conditions of regularity for the set of differential equations with degenerated matrix of coefficients.
Design and realization of new C-RBF neural networks classifier
新的CRBF神经网络分类器的设计与实现

HUANG Guo-hong,LIU Gang,
黄国宏
,刘刚

计算机应用研究 , 2008,
Abstract: 从RBF神经元的几何意义出发,提出了一种新的用于模式识别的C-RBF神经网络分类器.与传统RBF网络相比,该算法能够自动地优化RBF网络中核函数的个数、中心和宽度,且由于竞争神经元的引入,省去了传统RBF神经网络输出层线性连接权的计算,从而简化了网络的学习过程,大大缩短了训练时间.
Sign-changing Lyapunov functions in regularity of linear extensions of dynamical systems on a torus  [PDF]
Ewa Tkocz-Piszczek
Opuscula Mathematica , 2008,
Abstract: In this paper we consider some sign-changing Lyapunov function in research on regularity of sets of linear extensions of dynamical systems on a torus.
基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法
Traffic sign recognition method based on HOG-Gabor feature fusion and Softmax classifier
 [PDF]

梁敏健,崔啸宇,宋青松,赵祥模
- , 2017,
Abstract: 为了提高交通标志识别的正确率和实时性,提出了一种基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法。采用 Gamma矫正方法提取HOG特征,采用对比度受限的自适应直方图均衡化方法提取Gabor特征,基于线性特征融合原理,将提取的HOG和 Gabor特征向量直接串联,得到刻画交通标志的融合特征向量,采用Softmax分类器对融合特征向量进行分类,采用德国交通标志识别 基准(GTSRB)数据库测试了所提方法的有效性,比较了基于单特征与融合特征的交通标志识别效果。试验结果表明:在图像增强过程 中,针对HOG特征,采用Gamma矫正方法的分类正确率最大,为97.11%,针对Gabor特征,采用限制对比度的直方图均衡化方法的分类正确 率最大,为97.54%; 采用Softmax分类器的最小分类正确率为97.11%,耗时小于2 s; 针对HOG-Gabor融合特征,采Softmax分类器的识 别率高达97.68%,因此, 基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法的识别率高,实时性强。
In order to improve the accuracy and real-time performance of traffic sign recognition, a traffic sign recognition method was proposed based on HOG-Gabor feature fusion and Softmax classifier. HOG(histogram of oriented gradient)feature was extracted by using the Gamma correction method, and Gabor feature was extracted by using the contrast limited adaptive histogram equalization method. According to the linear feature fusion principle, HOG and Gabor feature vectors were connected to constitute the fusional feature vector for depicting the traffic signs. The effectiveness of the proposed method was verified based on the GTSRB(German Traffic Sign Recognition Benchmark)data set. The recognition effects of traffic sign based on single feature and fusional feature were compared. Experimental result shows that in image enhancement, the classification accuracy based on HOG feature is 97.11% and is largest by the Gamma correction method, and the classification accuracy based on Gabor feature is 97.54% and is largest by the contrast limited adaptive histogram equalization method. The minimum classification accuracy is 97.11% by using Softmax classifier, and classification time is only 2 s. The correct recognition rate of traffic sign reaches 97.68% by using the proposed method based on HOG-Gabor fusional features, so the traffic sign recognition method based on HOG-Gabor fusional features and Softmax classifier has high recognition rate and real-time performance. 4 tabs, 10 figs, 26 refs
?基于rbf网络的混沌动力系统辨识  [PDF]
?李冬梅,王正欧
天津大学学报(自然科学与工程技术版) , 2002,
Abstract: 提出用rbf神经网络对混沌动力系统进行辨识,设计了一个三层rbf网络结构,仿真实验说明了rbf网络用于学习混沌动力系统时的基本性质。用辨识模型重建吸引子方法定性地评价辨识模型,通过计算辨识模型的lyapunov指数定量地评价辨识模型的性能,同时推导了rbf网络模型lyapunov指数的计算公式。仿真结果表明,该辨识模型能很好地逼近原混沌动力系统,准确地体现原混沌系统的动力学特性。
Classification of mammographic features using RBF-SA  [cached]
Rafael do Espírito Santo,Roseli de Deus Lopes,Rangaraj M. Rangayyan
Exacta , 2006,
Abstract: We present in this work a new type of classes discriminator based upon nonlinear and combinational optimization techniques: radial basis functions-simulated annealing (RBF-SA). The combinational optimization method is used here as a preestimation of some parameters of the network classifier. We compare the classifier performance with and without pre-estimation. For training the classifiers, adopting the leave-one-out procedure, we have used case examples such as mammographic masses (malignant and benign). The classifier is trained with shape factors and edge-sharpness measures extracted from 57 regions of interest (ROI) (37 malignant and 20 benign), manually delineated, that describe mammographic masses and tumor features in terms of polygonal models for shape factors (compactness [CC], Fourier description [FF], fractional concavity [FCC] and speculated index [SI]) and edge sharpness-acutance (A) . The classifier performance is compared in terms of the area under the receive operating characteristic (ROC) curve (A). Higher values of A correspond to a better performance of classifier. Experiments with mammographic tumor and masses show that the best result of 0.9776 is obtained with RBF-SA when RBF parameters such as centers and spread matrix are pre-estimated, which is significantly better than the results obtained with no pre-estimation or only pre-estimation of the RBF centers, which are, 0.7071 and 0.9552 respectively.
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