IEEE transactions on neural networks最新文献_第8页

Minimum-volume-constrained nonnegative matrix factorization: enhanced ability of learning parts. 最小体积约束非负矩阵分解:增强学习部件的能力。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-30 DOI: 10.1109/TNN.2011.2164621

Guoxu Zhou, Shengli Xie, Zuyuan Yang, Jun-Mei Yang, Zhaoshui He

{"title":"Minimum-volume-constrained nonnegative matrix factorization: enhanced ability of learning parts.","authors":"Guoxu Zhou, Shengli Xie, Zuyuan Yang, Jun-Mei Yang, Zhaoshui He","doi":"10.1109/TNN.2011.2164621","DOIUrl":"https://doi.org/10.1109/TNN.2011.2164621","url":null,"abstract":"Nonnegative matrix factorization (NMF) with minimum-volume-constraint (MVC) is exploited in this paper. Our results show that MVC can actually improve the sparseness of the results of NMF. This sparseness is L(0)-norm oriented and can give desirable results even in very weak sparseness situations, thereby leading to the significantly enhanced ability of learning parts of NMF. The close relation between NMF, sparse NMF, and the MVC_NMF is discussed first. Then two algorithms are proposed to solve the MVC_NMF model. One is called quadratic programming_MVC_NMF (QP_MVC_NMF) which is based on quadratic programming and the other is called negative glow_MVC_NMF (NG_MVC_NMF) because it uses multiplicative updates incorporating natural gradient ingeniously. The QP_MVC_NMF algorithm is quite efficient for small-scale problems and the NG_MVC_NMF algorithm is more suitable for large-scale problems. Simulations show the efficiency and validity of the proposed methods in applications of blind source separation and human face images analysis.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 10","pages":"1626-37"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2164621","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30108304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 63

Embedding prior knowledge within compressed sensing by neural networks. 用神经网络在压缩感知中嵌入先验知识。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-09-06 DOI: 10.1109/TNN.2011.2164810

Dany Merhej, Chaouki Diab, Mohamad Khalil, Rémy Prost

{"title":"Embedding prior knowledge within compressed sensing by neural networks.","authors":"Dany Merhej, Chaouki Diab, Mohamad Khalil, Rémy Prost","doi":"10.1109/TNN.2011.2164810","DOIUrl":"https://doi.org/10.1109/TNN.2011.2164810","url":null,"abstract":"In the compressed sensing framework, different algorithms have been proposed for sparse signal recovery from an incomplete set of linear measurements. The most known can be classified into two categories: l(1) norm minimization-based algorithms and l(0) pseudo-norm minimization with greedy matching pursuit algorithms. In this paper, we propose a modified matching pursuit algorithm based on the orthogonal matching pursuit (OMP). The idea is to replace the correlation step of the OMP, with a neural network. Simulation results show that in the case of random sparse signal reconstruction, the proposed method performs as well as the OMP. Complexity overhead, for training and then integrating the network in the sparse signal recovery is thus not justified in this case. However, if the signal has an added structure, it is learned and incorporated in the proposed new OMP. We consider three structures: first, the sparse signal is positive, second the positions of the non zero coefficients of the sparse signal follow a certain spatial probability density function, the third case is a combination of both. Simulation results show that, for these signals of interest, the probability of exact recovery with our modified OMP increases significantly. Comparisons with l(1) based reconstructions are also performed. We thus present a framework to reconstruct sparse signals with added structure by embedding, through neural network training, additional knowledge to the decoding process in order to have better performance in the recovery of sparse signals of interest.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 10","pages":"1638-49"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2164810","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30127260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Incremental learning of concept drift in nonstationary environments. 非平稳环境下概念漂移的增量学习。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-04 DOI: 10.1109/TNN.2011.2160459

Ryan Elwell, Robi Polikar

{"title":"Incremental learning of concept drift in nonstationary environments.","authors":"Ryan Elwell, Robi Polikar","doi":"10.1109/TNN.2011.2160459","DOIUrl":"https://doi.org/10.1109/TNN.2011.2160459","url":null,"abstract":"We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn(++). NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn(++) family of algorithms, that is, without requiring access to previously seen data. Learn(++). NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn(++). NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 10","pages":"1517-31"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2160459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30063664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 765

Passivity analysis for discrete-time stochastic Markovian jump neural networks with mixed time delays. 混合时滞离散随机马尔可夫跳变神经网络的无源性分析。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-12 DOI: 10.1109/TNN.2011.2163203

Zheng-Guang Wu, Peng Shi, Hongye Su, Jian Chu

引用次数: 364

Analysis of fixed-point and coordinate descent algorithms for regularized kernel methods. 正则化核方法的不动点和坐标下降算法分析。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-18 DOI: 10.1109/TNN.2011.2164096

Francesco Dinuzzo

引用次数: 6

Stability and L2 performance analysis of stochastic delayed neural networks. 随机延迟神经网络的稳定性和L2性能分析。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-12 DOI: 10.1109/TNN.2011.2163319

Yun Chen, Wei Xing Zheng

引用次数: 28

Chaotic simulated annealing by a neural network with a variable delay: design and application. 变延迟神经网络的混沌模拟退火:设计与应用。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-12 DOI: 10.1109/TNN.2011.2163080

Shyan-Shiou Chen

{"title":"Chaotic simulated annealing by a neural network with a variable delay: design and application.","authors":"Shyan-Shiou Chen","doi":"10.1109/TNN.2011.2163080","DOIUrl":"https://doi.org/10.1109/TNN.2011.2163080","url":null,"abstract":"In this paper, we have three goals: the first is to delineate the advantages of a variably delayed system, the second is to find a more intuitive Lyapunov function for a delayed neural network, and the third is to design a delayed neural network for a quadratic cost function. For delayed neural networks, most researchers construct a Lyapunov function based on the linear matrix inequality (LMI) approach. However, that approach is not intuitive. We provide a alternative candidate Lyapunov function for a delayed neural network. On the other hand, if we are first given a quadratic cost function, we can construct a delayed neural network by suitably dividing the second-order term into two parts: a self-feedback connection weight and a delayed connection weight. To demonstrate the advantage of a variably delayed neural network, we propose a transiently chaotic neural network with variable delay and show numerically that the model should possess a better searching ability than Chen-Aihara's model, Wang's model, and Zhao's model. We discuss both the chaotic and the convergent phases. During the chaotic phase, we simply present bifurcation diagrams for a single neuron with a constant delay and with a variable delay. We show that the variably delayed model possesses the stochastic property and chaotic wandering. During the convergent phase, we not only provide a novel Lyapunov function for neural networks with a delay (the Lyapunov function is independent of the LMI approach) but also establish a correlation between the Lyapunov function for a delayed neural network and an objective function for the traveling salesman problem.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 10","pages":"1557-65"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2163080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29934357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction. 控制方向未知非线性时变时滞系统的神经网络自适应控制。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-30 DOI: 10.1109/TNN.2011.2165222

Yuntong Wen, Xuemei Ren

{"title":"Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction.","authors":"Yuntong Wen, Xuemei Ren","doi":"10.1109/TNN.2011.2165222","DOIUrl":"https://doi.org/10.1109/TNN.2011.2165222","url":null,"abstract":"This paper investigates a neural network (NN) state observer-based adaptive control for a class of time-varying delays nonlinear systems with unknown control direction. An adaptive neural memoryless observer, in which the knowledge of time-delay is not used, is designed to estimate the system states. Furthermore, by applying the property of the function tanh(2)(ϑ/ε)/ϑ (the function can be defined at ϑ = 0) and introducing a novel type appropriate Lyapunov-Krasovskii functional, an adaptive output feedback controller is constructed via backstepping method which can efficiently avoid the problem of controller singularity and compensate for the time-delay. It is highly proven that the closed-loop systems controller designed by the NN-basis function property, new kind parameter adaptive law and Nussbaum function in detecting the control direction is able to guarantee the semi-global uniform ultimate boundedness of all signals and the tracking error can converge to a small neighborhood of zero. The characteristic of the proposed approach is that it relaxes any restrictive assumptions of Lipschitz condition for the unknown nonlinear continuous functions. And the proposed scheme is suitable for the systems with mismatching conditions and unmeasurable states. Finally, two simulation examples are given to illustrate the effectiveness and applicability of the proposed approach.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 10","pages":"1599-612"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2165222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30110075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 86

Efficient revised simplex method for SVM training. 基于修正单纯形的SVM训练方法。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-09-06 DOI: 10.1109/TNN.2011.2165081

Christopher Sentelle, Georgios C Anagnostopoulos, Michael Georgiopoulos

{"title":"Efficient revised simplex method for SVM training.","authors":"Christopher Sentelle, Georgios C Anagnostopoulos, Michael Georgiopoulos","doi":"10.1109/TNN.2011.2165081","DOIUrl":"https://doi.org/10.1109/TNN.2011.2165081","url":null,"abstract":"Existing active set methods reported in the literature for support vector machine (SVM) training must contend with singularities when solving for the search direction. When a singularity is encountered, an infinite descent direction can be carefully chosen that avoids cycling and allows the algorithm to converge. However, the algorithm implementation is likely to be more complex and less computationally efficient than would otherwise be required for an algorithm that does not have to contend with the singularities. We show that the revised simplex method introduced by Rusin provides a guarantee of nonsingularity when solving for the search direction. This method provides for a simpler and more computationally efficient implementation, as it avoids the need to test for rank degeneracies and also the need to modify factorizations or solution methods based upon those rank degeneracies. In our approach, we take advantage of the guarantee of nonsingularity by implementing an efficient method for solving the search direction and show that our algorithm is competitive with SVM-QP and also that it is a particularly effective when the fraction of nonbound support vectors is large. In addition, we show competitive performance of the proposed algorithm against two popular SVM training algorithms, SVMLight and LIBSVM.","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 10","pages":"1650-61"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2165081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30125871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Deep learning regularized Fisher mappings. 深度学习正则化Fisher映射。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-12 DOI: 10.1109/TNN.2011.2162429

W K Wong, Mingming Sun

引用次数: 41