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IEEE Transactions on Neural Networks and Learning Systems

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https://www.readbyqxmd.com/read/28922130/haze-removal-using-radial-basis-function-networks-for-visibility-restoration-applications
#1
Bo-Hao Chen, Shih-Chia Huang, Chian-Ying Li, Sy-Yen Kuo
Restoration of visibility in hazy images is the first relevant step of information analysis in many outdoor computer vision applications. To this aim, the restored image must feature clear visibility with sufficient brightness and visible edges, while avoiding the production of noticeable artifacts. In this paper, we propose a haze removal approach based on the radial basis function (RBF) through artificial neural networks dedicated to effectively removing haze formation while retaining not only the visible edges but also the brightness of restored images...
September 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28922128/deep-manifold-learning-combined-with-convolutional-neural-networks-for-action-recognition
#2
Xin Chen, Jian Weng, Wei Lu, Jiaming Xu, Jiasi Weng
Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and efficiency. In this paper, we propose to incorporate the manifold of training samples into deep learning, which is defined as deep manifold learning (DML). The proposed DML framework can be adapted to most existing deep networks to learn more discriminative features for action recognition...
September 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28922129/dissipativity-and-synchronization-of-generalized-bam-neural-networks-with-multivariate-discontinuous-activations
#3
Dongshu Wang, Lihong Huang, Longkun Tang
This paper is concerned with the dissipativity and synchronization problems of a class of delayed bidirectional associative memory (BAM) neural networks in which neuron activations are modeled by discontinuous bivariate functions. First, the concept of the Filippov solution is extended to functional differential equations with discontinuous right-hand sides and mixed delays via functional differential inclusions. The global dissipativity of the Filippov solution to the considered BAM neural networks is proven using generalized Halanay inequalities and matrix measure approaches...
September 14, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28922127/jointly-learning-structured-analysis-discriminative-dictionary-and-analysis-multiclass-classifier
#4
Zhao Zhang, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, Shuicheng Yan
In this paper, we propose an analysis mechanism-based structured analysis discriminative dictionary learning (ADDL) framework. The ADDL seamlessly integrates ADDL, analysis representation, and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learned dictionaries, representations, and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the subdictionaries associated with different classes to be independent...
September 14, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28920909/credit-card-fraud-detection-a-realistic-modeling-and-a-novel-learning-strategy
#5
Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, Cesare Alippi, Gianluca Bontempi
Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS)...
September 14, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28920911/efficient-online-learning-algorithms-based-on-lstm-neural-networks
#6
Tolga Ergen, Suleyman Serdar Kozat
We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions...
September 13, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28920910/online-density-estimation-of-nonstationary-sources-using-exponential-family-of-distributions
#7
Kaan Gokcesu, Suleyman S Kozat
We investigate online probability density estimation (or learning) of nonstationary (and memoryless) sources using exponential family of distributions. To this end, we introduce a truly sequential algorithm that achieves Hannan-consistent log-loss regret performance against true probability distribution without requiring any information about the observation sequence (e.g., the time horizon T and the drift of the underlying distribution C) to optimize its parameters. Our results are guaranteed to hold in an individual sequence manner...
September 13, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28910780/finite-time-synchronization-of-discontinuous-neural-networks-with-delays-and-mismatched-parameters
#8
Wanli Zhang, Xinsong Yang, Chen Xu, Jianwen Feng, Chuandong Li
This paper investigates the problem of finite-time drive-response synchronization for a class of neural networks with discontinuous activations, time-varying discrete and infinite-time distributed delays, and mismatched parameters. In order to cope with the difficulties induced by discontinuous activations, time delays, as well as mismatched parameters simultaneously, new 1-norm-based analytical techniques are developed. Both state feedback and adaptive controllers with and without the sign function are designed...
September 7, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28910779/partial-nodes-based-state-estimation-for-complex-networks-with-unbounded-distributed-delays
#9
Yurong Liu, Zidong Wang, Yuan Yuan, Fuad E Alsaadi
In this brief, the new problem of partial-nodes-based (PNB) state estimation problem is investigated for a class of complex network with unbounded distributed delays and energy-bounded measurement noises. The main novelty lies in that the states of the complex network are estimated through measurement outputs of a fraction of the network nodes. Such fraction of the nodes is determined by either the practical availability or the computational necessity. The PNB state estimator is designed such that the error dynamics of the network state estimation is exponentially ultimately bounded in the presence of measurement errors...
September 7, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28910778/self-weighted-supervised-discriminative-feature-selection
#10
Rui Zhang, Feiping Nie, Xuelong Li
In this brief, a novel self-weighted orthogonal linear discriminant analysis (SOLDA) problem is proposed, and a self-weighted supervised discriminative feature selection (SSD-FS) method is derived by introducing sparsity-inducing regularization to the proposed SOLDA problem. By using the row-sparse projection, the proposed SSD-FS method is superior to multiple sparse feature selection approaches, which can overly suppress the nonzero rows such that the associated features are insufficient for selection. More specifically, the orthogonal constraint ensures the minimal number of selectable features for the proposed SSD-FS method...
September 7, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28885162/adaptive-approximation-based-regulation-control-for-a-class-of-uncertain-nonlinear-systems-without-feedback-linearizability
#11
Ning Wang, Jing-Chao Sun, Min Han, Zhongjiu Zheng, Meng Joo Er
In this paper, for a general class of uncertain nonlinear (cascade) systems, including unknown dynamics, which are not feedback linearizable and cannot be solved by existing approaches, an innovative adaptive approximation-based regulation control (AARC) scheme is developed. Within the framework of adding a power integrator (API), by deriving adaptive laws for output weights and prediction error compensation pertaining to single-hidden-layer feedforward network (SLFN) from the Lyapunov synthesis, a series of SLFN-based approximators are explicitly constructed to exactly dominate completely unknown dynamics...
September 6, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28880195/lann-svd-a-non-iterative-svd-based-learning-algorithm-for-one-layer-neural-networks
#12
Oscar Fontenla-Romero, Beatriz Perez-Sanchez, Bertha Guijarro-Berdinas
In the scope of data analytics, the volume of a data set can be defined as a product of instance size and dimensionality of the data. In many real problems, data sets are mainly large only on one of these aspects. Machine learning methods proposed in the literature are able to efficiently learn in only one of these two situations, when the number of variables is much greater than instances or vice versa. However, there is no proposal allowing to efficiently handle either circumstances in a large-scale scenario...
September 1, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28880191/exploiting-spatio-temporal-structure-with-recurrent-winner-take-all-networks
#13
Eder Santana, Matthew S Emigh, Pablo Zegers, Jose C Principe
We propose a convolutional recurrent neural network (ConvRNNs), with winner-take-all (WTA) dropout for high-dimensional unsupervised feature learning in multidimensional time series. We apply the proposed method for object recognition using temporal context in videos and obtain better results than comparable methods in the literature, including the deep predictive coding networks (DPCNs) previously proposed by Chalasani and Principe. Our contributions can be summarized as a scalable reinterpretation of the DPCNs trained end-to-end with backpropagation through time, an extension of the previously proposed WTA autoencoders to sequences in time, and a new technique for initializing and regularizing ConvRNNs...
September 1, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28880189/event-triggered-h%C3%A2-state-estimation-for-delayed-stochastic-memristive-neural-networks-with-missing-measurements-the-discrete-time-case
#14
Hongjian Liu, Zidong Wang, Bo Shen, Xiaohui Liu
In this paper, the event-triggered H∞ state estimation problem is investigated for a class of discrete-time stochastic memristive neural networks (DSMNNs) with time-varying delays and missing measurements. The DSMNN is subject to both the additive deterministic disturbances and the multiplicative stochastic noises. The missing measurements are governed by a sequence of random variables obeying the Bernoulli distribution. For the purpose of energy saving, an event-triggered communication scheme is used for DSMNNs to determine whether the measurement output is transmitted to the estimator or not...
September 1, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28880194/stability-and-guaranteed-cost-analysis-of-time-triggered-boolean-networks
#15
Min Meng, James Lam, Jun-E Feng, Kie Chung Cheung
This paper investigates stability and guaranteed cost of time-triggered Boolean networks (BNs) based on the semitensor product of matrices. The time triggering is generated by mode-dependent average dwell-time switching signals in the BNs. With the help of the copositive Lyapunov function, a sufficient condition is derived to ensure that the considered network is globally stable under a designed average dwell-time switching signal. Subsequently, an infinite time cost function is further discussed and its bound is presented according to the obtained stability result...
August 31, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28880193/beyond-pairwise-matching-person-reidentification-via-high-order-relevance-learning
#16
Xibin Zhao, Nan Wang, Yubo Zhang, Shaoyi Du, Yue Gao, Jiaguang Sun
Person reidentification has attracted extensive research efforts in recent years. It is challenging due to the varied visual appearance from illumination, view angle, background, and possible occlusions, leading to the difficulties when measuring the relevance, i.e., similarities, between probe and gallery images. Existing methods mainly focus on pairwise distance metric learning for person reidentification. In practice, pairwise image matching may limit the data for comparison (just the probe and one gallery subject) and yet lead to suboptimal results...
August 31, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28880192/global-exponential-stability-of-impulsive-fuzzy-high-order-bam-neural-networks-with-continuously-distributed-delays
#17
Wengui Yang, Wenwu Yu, Jinde Cao
This paper investigates the stability of equilibrium point and periodic solution for impulsive fuzzy high-order bidirectional associative memory neural networks with continuously distributed delays. By applying the inequality analysis technique, $M$-matrix, and Banach contraction mapping principle and constructing some suitable Lyapunov functionals, some sufficient conditions for the uniqueness and global exponential stability of equilibrium point and global exponential stability of periodic solutions are established...
August 31, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28880190/learning-semantic-aligned-action-representation
#18
Bingbing Ni, Teng Li, Xiaokang Yang
A fundamental bottleneck for achieving highly discriminative action representation is that local motion/appearance features are usually not semantic aligned. Namely, a local feature, such as a motion vector or motion trajectory, does not possess any attribute that indicates which moving body part or operated object it is associated with. This mostly leads to global feature pooling/representation learning methods that are often too coarse. Inspired by the recent success of end-to-end (pixel-to-pixel) deep convolutional neural networks (DCNNs), in this paper, we first propose a DCNN architecture, which maps a human centric image region onto human body part response maps...
August 31, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28866603/boundary-control-of-2-d-burgers-pde-an-adaptive-dynamic-programming-approach
#19
Behzad Talaei, Sarangapani Jagannathan, John Singler
In this paper, an adaptive dynamic programming-based near optimal boundary controller is developed for partial differential equations (PDEs) modeled by the uncertain Burgers' equation under Neumann boundary condition in 2-D. Initially, Hamilton-Jacobi-Bellman equation is derived in infinite-dimensional space. Subsequently, a novel neural network (NN) identifier is introduced to approximate the nonlinear dynamics in the 2-D PDE. The optimal control input is derived by online estimation of the value function through an additional NN-based forward-in-time estimation and approximated dynamic model...
August 29, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28866602/deterministic-convergence-for-learning-control-systems-over-iteration-dependent-tracking-intervals
#20
Deyuan Meng, Jingyao Zhang
This brief addresses the iterative learning control (ILC) problems for discrete-time systems subject to iteration-dependent tracking time intervals. A modified class of P-type ILC algorithms is proposed by properly defining an available modified output, for which robust convergence analysis is performed with an inductive approach. It is shown that if a persistent full-learning property is ensured, then a necessary and sufficient convergence condition of ILC can be derived to reach the perfect output tracking objective though the tracking time interval is iteration-dependent...
August 29, 2017: IEEE Transactions on Neural Networks and Learning Systems
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