Read by QxMD icon Read

IEEE Transactions on Neural Networks and Learning Systems

Jinglan Li, Qinmin Yang, Bo Fan, Youxian Sun
The coaxial-rotor micro-aerial vehicles (CRMAVs) have been proven to be a powerful tool in forming small and agile manned-unmanned hybrid applications. However, the operation of them is usually subject to unpredictable time-varying aerodynamic disturbances and model uncertainties. In this paper, an adaptive robust controller based on a neural network (NN) approach is proposed to reject such perturbations and track both the desired position and orientation trajectories. A complete dynamic model of a CRMAV is first constructed...
May 15, 2019: IEEE Transactions on Neural Networks and Learning Systems
Giuseppe C Calafiore, Stephane Gaubert, Corrado Possieri
In this paper, we show that a one-layer feedforward neural network with exponential activation functions in the inner layer and logarithmic activation in the output neuron is a universal approximator of convex functions. Such a network represents a family of scaled log-sum exponential functions, here named log-sum-exp (LSET). Under a suitable exponential transformation, the class of LSET functions maps to a family of generalized posynomials GPOST, which we similarly show to be universal approximators for log-log-convex functions...
May 15, 2019: IEEE Transactions on Neural Networks and Learning Systems
Yan Luo, Yongkang Wong, Mohan Kankanhalli, Qi Zhao
Intraclass compactness and interclass separability are crucial indicators to measure the effectiveness of a model to produce discriminative features, where intraclass compactness indicates how close the features with the same label are to each other and interclass separability indicates how far away the features with different labels are. In this paper, we investigate intraclass compactness and interclass separability of features learned by convolutional networks and propose a Gaussian-based softmax (G-softmax) function that can effectively improve intraclass compactness and interclass separability...
May 10, 2019: IEEE Transactions on Neural Networks and Learning Systems
Xiaojun Chen, Renjie Chen, Qingyao Wu, Yixiang Fang, Feiping Nie, Joshua Zhexue Huang
Although many spectral clustering algorithms have been proposed during the past decades, they are not scalable to large-scale data due to their high computational complexities. In this paper, we propose a novel spectral clustering method for large-scale data, namely, large-scale balanced min cut (LABIN). A new model is proposed to extend the self-balanced min-cut (SBMC) model with the anchor-based strategy and a fast spectral rotation with linear time complexity is proposed to solve the new model. Extensive experimental results show the superior performance of our proposed method in comparison with the state-of-the-art methods including SBMC...
May 10, 2019: IEEE Transactions on Neural Networks and Learning Systems
Chun-Na Li, Yuan-Hai Shao, Wotao Yin, Ming-Zeng Liu
In this paper, we propose a robust linear discriminant analysis (RLDA) through Bhattacharyya error bound optimization. RLDA considers a nonconvex problem with the L₁-norm operation that makes it less sensitive to outliers and noise than the L₂-norm linear discriminant analysis (LDA). In addition, we extend our RLDA to a sparse model (RSLDA). Both RLDA and RSLDA can extract unbounded numbers of features and avoid the small sample size (SSS) problem, and an alternating direction method of multipliers (ADMM) is used to cope with the nonconvexity in the proposed formulations...
May 9, 2019: IEEE Transactions on Neural Networks and Learning Systems
Joey Tianyi Zhou, Hao Zhang, Di Jin, Xi Peng, Yang Xiao, Zhiguo Cao
In this paper, we mainly investigate two issues for sequence labeling, namely, label imbalance and noisy data that are commonly seen in the scenario of named entity recognition (NER) and are largely ignored in the existing works. To address these two issues, a new method termed robust sequence labeling (RoSeq) is proposed. Specifically, to handle the label imbalance issue, we first incorporate label statistics in a novel conditional random field (CRF) loss. In addition, we design an additional loss to reduce the weights of overwhelming easy tokens for augmenting the CRF loss...
May 8, 2019: IEEE Transactions on Neural Networks and Learning Systems
Alessandro Betti, Marco Gori, Stefano Melacci
This paper proposes a theory for understanding perceptual learning processes within the general framework of laws of nature. Artificial neural networks are regarded as systems whose connections are Lagrangian variables, namely, functions depending on time. They are used to minimize the cognitive action, an appropriate functional index that measures the agent interactions with the environment. The cognitive action contains a potential and a kinetic term that nicely resemble the classic formulation of regularization in machine learning...
May 8, 2019: IEEE Transactions on Neural Networks and Learning Systems
Qibin Zheng, Xingchun Diao, Jianjun Cao, Yi Liu, Hongmei Li, Junnan Yao, Chen Chang, Guojun Lv
Dissimilarity measures play a crucial role in clustering and, are directly related to the performance of clustering algorithms. However, effectively measuring the dissimilarity is not easy, especially for categorical data. The main difficulty of the dissimilarity measurement for categorical data is that its representation lacks a clear space structure. Therefore, the space structure-based representation has been proposed to provide the categorical data with a clear linear representation space. This representation improves the clustering performance obviously but only applies to small data sets because its dimensionality increases rapidly with the size of the data set...
May 8, 2019: IEEE Transactions on Neural Networks and Learning Systems
Xinxing Wu, Junping Zhang, Fei-Yue Wang
As one of the efficient approaches to deal with big data, divide-and-conquer distributed algorithms, such as the distributed kernel regression, bootstrap, structured perception training algorithms, and so on, are proposed and broadly used in learning systems. Some learning theories have been built to analyze the feasibility, approximation, and convergence bounds of these distributed learning algorithms. However, less work has been studied on the stability of these distributed learning algorithms. In this paper, we discuss the generalization bounds of distributed learning algorithms from the view of algorithmic stability...
May 8, 2019: IEEE Transactions on Neural Networks and Learning Systems
Tong Yang, Ning Sun, He Chen, Yongchun Fang
As a type of indispensable oceanic transportation tools, ship-mounted crane systems are widely employed to transport cargoes and containers on vessels due to their extraordinary flexibility. However, various working requirements and the oceanic environment may cause some uncertain and unfavorable factors for ship-mounted crane control. In particular, to accomplish different control tasks, some plant parameters (e.g., boom lengths, payload masses, and so on) frequently change; hence, most existing model-based controllers cannot ensure satisfactory control performance any longer...
May 6, 2019: IEEE Transactions on Neural Networks and Learning Systems
Xin Wang, Ju H Park, Shouming Zhong, Huilan Yang
This paper investigates the issue of sampled-data stabilization for Takagi-Sugeno fuzzy memristive neural networks (FMNNs) with time-varying delay. First, the concerned FMNNs are transformed into the tractable fuzzy NNs based on the excitatory and inhibitory of memristive synaptic weights using a new convex combination technique. Meanwhile, a switched fuzzy sampled-data controller is employed for the first time to tackle stability problems related to FMNNs. Then, the novel stabilization criteria of the FMNNs are established using the fuzzy membership functions (FMFs)-dependent Lyapunov-Krasovskii functional...
May 6, 2019: IEEE Transactions on Neural Networks and Learning Systems
Gonzalo Napoles, Frank Vanhoenshoven, Rafael Falcon, Koen Vanhoof
We introduce a neural cognitive mapping technique named long-term cognitive network (LTCN) that is able to memorize long-term dependencies between a sequence of input and output vectors, especially in those scenarios that require predicting the values of multiple dependent variables at the same time. The proposed technique is an extension of a recently proposed method named short-term cognitive network that aims at preserving the expert knowledge encoded in the weight matrix while optimizing the nonlinear mappings provided by the transfer function of each neuron...
May 6, 2019: IEEE Transactions on Neural Networks and Learning Systems
Zhibin Quan, Weili Zeng, Xuelian Li, Yandong Liu, Yunxiu Yu, Wankou Yang
Learning long-term dependences (LTDs) with recurrent neural networks (RNNs) is challenging due to their limited internal memories. In this paper, we propose a new external memory architecture for RNNs called an external addressable long-term and working memory (EALWM)-augmented RNN. This architecture has two distinct advantages over existing neural external memory architectures, namely the division of the external memory into two parts--long-term memory and working memory--with both addressable and the capability to learn LTDs without suffering from vanishing gradients with necessary assumptions...
May 3, 2019: IEEE Transactions on Neural Networks and Learning Systems
Hongxin Wang, Jigen Peng, Xuqiang Zheng, Shigang Yue
Monitoring small objects against cluttered moving backgrounds is a huge challenge to future robotic vision systems. As a source of inspiration, insects are quite apt at searching for mates and tracking prey, which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Although a few STMD-based models have been proposed, these existing models only use motion information for small target detection and cannot discriminate small targets from small-target-like background features (named fake features)...
May 1, 2019: IEEE Transactions on Neural Networks and Learning Systems
Bowen Li, Yang Liu, Jungang Lou, Jianquan Lu, Jinde Cao
In this brief, we investigate the robustness of outputs with respect to disturbances for Boolean control networks (BCNs) by semi-tensor product (STP) of matrices. First, BCNs are converted into the corresponding algebraic forms by STP, then two sufficient conditions for the robustness are derived. Moreover, the corresponding permutation system and permutation graph are constructed. It is proven that if there exist controllers such that the outputs of permutation systems are robust with respect to disturbances, then there must also exist controllers such that the outputs of the corresponding original systems achieve robustness with respect to disturbances...
May 1, 2019: IEEE Transactions on Neural Networks and Learning Systems
Guodong Zhang, Zhigang Zeng
In this brief, we investigate a class of second-order memristive neural networks (SMNNs) with mixed time-varying delays. Based on nonsmooth analysis, the Lyapunov stability theory, and adaptive control theory, several new results ensuring global stabilization of the SMNNs are obtained. In addition, compared with the reduced-order method used in the existing research studies, we consider the global stabilization directly from the SMNNs themselves without the reduced-order method. Finally, we give some numerical simulations to show the effectiveness of the results...
May 1, 2019: IEEE Transactions on Neural Networks and Learning Systems
Carlos J Vega, Oscar J Suarez, Edgar N Sanchez, Guanrong Chen, Santiago Elvira-Ceja, David I Rodriguez
A new approach for trajectory tracking on uncertain complex networks is proposed. To achieve this goal, a neural controller is applied to a small fraction of nodes (pinned ones). Such controller is composed of an on-line identifier based on a recurrent high-order neural network, and an inverse optimal controller to track the desired trajectory; a complete stability analysis is also included. In order to verify the applicability and good performance of the proposed control scheme, a representative example is simulated, which consists of a complex network with each node described by a chaotic Lorenz oscillator...
April 30, 2019: IEEE Transactions on Neural Networks and Learning Systems
Zhengming Li, Zheng Zhang, Jie Qin, Zhao Zhang, Ling Shao
Both interclass variances and intraclass similarities are crucial for improving the classification performance of discriminative dictionary learning (DDL) algorithms. However, existing DDL methods often ignore the combination between the interclass and intraclass properties of dictionary atoms and coding coefficients. To address this problem, in this paper, we propose a discriminative Fisher embedding dictionary learning (DFEDL) algorithm that simultaneously establishes Fisher embedding models on learned atoms and coefficients...
April 30, 2019: IEEE Transactions on Neural Networks and Learning Systems
Hao Shen, Shicheng Huo, Huaicheng Yan, Ju H Park, Victor Sreeram
The distributed dissipative state estimation issue of Markov jump genetic regulatory networks subject to round-robin scheduling is investigated in this paper. The system parameters randomly change in the light of a Markov chain. Each node in sensor networks communicates with its neighboring nodes in view of the prescribed network topology graph. The round-robin scheduling is employed to arrange the transmission order to lessen the likelihood of the occurrence of data collisions. The main goal of the work is to design a compatible distributed estimator to assure that the distributed error system is strictly ( Λ₁,Λ₂,Λ₃) -ɣ -stochastically dissipative...
April 30, 2019: IEEE Transactions on Neural Networks and Learning Systems
Tianyu Hu, Qinglai Guo, Zhengshuo Li, Xinwei Shen, Hongbin Sun
Probability density forecast offers the whole distributions of forecasting targets, which brings greater flexibility and practicability than the other probabilistic forecast models such as prediction interval (PI) and quantile forecast. However, existing density forecast models have introduced various constraints on forecasted distributions, which has limited their ability to approximate real distributions and may result in suboptimality. In this paper, a distribution-free density forecast model based on deep learning is proposed, in which the real cumulative density functions (CDFs) of forecasting target are approximated by a large-capacity positive-weighted deep neural network (NN)...
April 30, 2019: IEEE Transactions on Neural Networks and Learning Systems
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"