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IEEE Transactions on Neural Networks and Learning Systems

Yifeng Li, Youlian Pan, Ziying Liu
In this big data era, interpretable machine learning models are strongly demanded for the comprehensive analytics of large-scale multiclass data. Characterizing all features from such data is a key but challenging step to understand the complexity. However, existing feature selection methods do not meet this need. In this paper, to address this problem, we propose a Bayesian multiclass nonnegative matrix factorization (MC-NMF) model with structured sparsity that is able to discover ubiquitous and class-specific features...
July 16, 2018: IEEE Transactions on Neural Networks and Learning Systems
Shangce Gao, MengChu Zhou, Yirui Wang, Jiujun Cheng, Hanaki Yachi, Jiahai Wang
An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems...
July 10, 2018: IEEE Transactions on Neural Networks and Learning Systems
Haoran Chen, Yanfeng Sun, Junbin Gao, Yongli Hu, Baocai Yin
Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two data sets. However, all existing approaches often optimize a PLSR model in Euclidean space and take a successive strategy to calculate all the factors one by one for keeping the mutually orthogonal PLSR factors. Thus, a suboptimal solution is often generated. To overcome the shortcoming, this paper takes statistically inspired modification of PLSR (SIMPLSR) as a representative of PLSR, proposes a novel approach to transform SIMPLSR into optimization problems on Riemannian manifolds, and develops corresponding optimization algorithms...
July 9, 2018: IEEE Transactions on Neural Networks and Learning Systems
James S Smith, Bo Wu, Bogdan M Wilamowski
Difficult experiments in training neural networks often fail to converge due to what is known as the flat-spot problem, where the gradient of hidden neurons in the network diminishes in value, rending the weight update process ineffective. Whereas a first-order algorithm can address this issue by learning parameters to normalize neuron activations, the second-order algorithms cannot afford additional parameters given that they include a large Jacobian matrix calculation. This paper proposes Levenberg-Marquardt with weight compression (LM-WC), which combats the flat-spot problem by compressing neuron weights to push neuron activation out of the saturated region and close to the linear region...
July 6, 2018: IEEE Transactions on Neural Networks and Learning Systems
Fanghai Zhang, Zhigang Zeng
In this paper, multiple ψ-type stability of Cohen-Grossberg neural networks (CGNNs) with both time-varying discrete delays and distributed delays is investigated. By utilizing ψ-type functions combined with a new ψ-type integral inequality for treating distributed delay terms, some sufficient conditions are obtained to ensure that multiple equilibrium points are ψ-type stable for CGNNs with discrete and distributed delays, where the distributed delays include bounded and unbounded delays. These conditions of CGNNs with different output functions are less restrictive...
July 6, 2018: IEEE Transactions on Neural Networks and Learning Systems
Hongfeng Li, Hongkai Zhao, Hong Li
This paper proposes a novel and simple multilayer feature learning method for image classification by employing the extreme learning machine (ELM). The proposed algorithm is composed of two stages: the multilayer ELM (ML-ELM) feature mapping stage and the ELM learning stage. The ML-ELM feature mapping stage is recursively built by alternating between feature map construction and maximum pooling operation. In particular, the input weights for constructing feature maps are randomly generated and hence need not be trained or tuned, which makes the algorithm highly efficient...
July 3, 2018: IEEE Transactions on Neural Networks and Learning Systems
Jiewan Zheng, Xianbin Cao, Baochang Zhang, Xiantong Zhen, Xiangbo Su
Video classification has been extensively researched in computer vision due to its wide spread applications. However, it remains an outstanding task because of the great challenges in effective spatial-temporal feature extraction and efficient classification with high-dimensional video representations. To address these challenges, in this paper, we propose an end-to-end learning framework called deep ensemble machine (DEM) for video classification. Specifically, to establish effective spatio-temporal features, we propose using two deep convolutional neural networks (CNNs), i...
July 3, 2018: IEEE Transactions on Neural Networks and Learning Systems
Mohammad Reza Bonyadi, Quang M Tieng, David C Reutens
In this paper, we introduce a new classification algorithm called the optimization of distribution differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as possible to one another, whereas the gravity centers of these classes are as far as possible from one another. This aim is formulated as a multiobjective optimization problem that is solved by a hybrid of an evolutionary strategy and the quasi-Newton method...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Jiangcheng Zhu, Jun Zhu, Zhepei Wang, Shan Guo, Chao Xu
This paper proposes a hierarchical decision-making and control algorithm for the shepherd game, the seventh mission in the International Aerial Robotics Competition (IARC). In this game, the agent (a multirotor aerial robot) is required to contact targets (ground vehicles) sequentially and drive them to a certain boundary to earn score. During the game of 10 min, the agent should be fully autonomous without any human interference. Regarding the lower-level controller and dynamics of the agent, each action takes a duration of time to accomplish...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Zhanyu Ma, Yuping Lai, W Bastiaan Kleijn, Yi-Zhe Song, Liang Wang, Jun Guo
In this paper, we develop a novel variational Bayesian learning method for the Dirichlet process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very flexible for modeling vectors with positive elements. The recently proposed extended variational inference (EVI) framework is adopted to derive an analytically tractable solution. The convergency of the proposed algorithm is theoretically guaranteed by introducing single lower bound approximation to the original objective function in the EVI framework...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Jicheng Shan, Hang Zhang, Weike Liu, Qingbao Liu
In practical applications, data stream classification faces significant challenges, such as high cost of labeling instances and potential concept drifting. We present a new online active learning ensemble framework for drifting data streams based on a hybrid labeling strategy that includes the following: 1) an ensemble classifier, which consists of a long-term stable classifier and multiple dynamic classifiers (a multilevel sliding window model is used to create and update the dynamic classifiers to effectively process both the gradual drift type and sudden drift type data stream) and 2) active learning, which takes a nonfixed labeling budget, supports on-demand request labeling, and adopts an uncertainty strategy and random strategy to label instances...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Prayag Gowgi, Shayan Srinivasa Garani
Self-organizing maps (SOMs) find numerous applications in learning, clustering, and recalling spatial input patterns. The traditional approach in learning spatiotemporal patterns is to incorporate time on the output space of a SOM along with heuristic update rules that work well in practice. Inspired by the pioneering work of Alan Turing, who used reaction-diffusion equations to explain spatial pattern formation, we develop an analogous theoretical model for a spatiotemporal memory to learn and recall temporal patterns...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Xiangyu Chang, Yan Zhong, Yao Wang, Shaobo Lin
Low-rank matrix estimation arises in a number of statistical and machine learning tasks. In particular, the coefficient matrix is considered to have a low-rank structure in multivariate linear regression and multivariate quantile regression. In this paper, we propose a method called penalized matrix least squares approximation (PMLSA) toward a unified yet simple low-rank matrix estimate. Specifically, PMLSA can transform many different types of low-rank matrix estimation problems into their asymptotically equivalent least-squares forms, which can be efficiently solved by a popular matrix fast iterative shrinkage-thresholding algorithm...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Yu Kang, Shaofeng Chen, Xuefeng Wang, Yang Cao
Helicopters are complex high-order and time-varying nonlinear systems, strongly coupling with aerodynamic forces, engine dynamics, and other phenomena. Therefore, it is a great challenge to investigate system identification for dynamic modeling and adaptive control for helicopters. In this paper, we address the system identification problem as dynamic regression and propose to represent the uncertainties and the hidden states in the system dynamic model with a deep convolutional neural network. Particularly, the parameters of the network are directly learned from the real flight data of aerobatic helicopter...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Stylianos I Venieris, Christos-Savvas Bouganis
Since neural networks renaissance, convolutional neural networks (ConvNets) have demonstrated a state-of-the-art performance in several emerging artificial intelligence tasks. The deployment of ConvNets in real-life applications requires power-efficient designs that meet the application-level performance needs. In this context, field-programmable gate arrays (FPGAs) can provide a potential platform that can be tailored to application-specific requirements. However, with the complexity of ConvNet models increasing rapidly, the ConvNet-to-FPGA design space becomes prohibitively large...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Ruimei Zhang, Deqiang Zeng, Ju H Park, Yajuan Liu, Shouming Zhong
This paper investigates the stability problem of Markovian neural networks (MNNs) with time delay. First, to reflect more realistic behaviors, more generalized transition rates are considered for MNNs, where all transition rates of some jumping modes are completely unknown. Second, a new approach, namely time-delay-dependent-matrix (TDDM) approach, is proposed for the first time. The TDDM approach is associated with both time delay and its time derivative. Thus, the TDDM approach can fully capture the information of time delay and would play a key role in deriving less conservative results...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Xiongbo Wan, Zidong Wang, Min Wu, Xiaohui Liu
This paper investigates the H∞ state estimation problem for a class of discrete-time nonlinear singularly perturbed complex networks (SPCNs) under the Round-Robin (RR) protocol. A discrete-time nonlinear SPCN model is first devised on two time scales with their discrepancies reflected by a singular perturbation parameter (SPP). The network measurement outputs are transmitted via a communication network where the data transmissions are scheduled by the RR protocol with hope to avoid the undesired data collision...
July 2, 2018: IEEE Transactions on Neural Networks and Learning Systems
Yan-Jun Liu, Shu Li, Shaocheng Tong, C L Philip Chen
In this paper, an optimal control algorithm is designed for uncertain nonlinear systems in discrete-time, which are in nonaffine form and with unknown dead-zone. The main contributions of this paper are that an optimal control algorithm is for the first time framed in this paper for nonlinear systems with nonaffine dead-zone, and the adaptive parameter law for dead-zone is calculated by using the gradient rules. The mean value theory is employed to deal with the nonaffine dead-zone input and the implicit function theory based on reinforcement learning is appropriately introduced to find an unknown ideal controller which is approximated by using the action network...
June 28, 2018: IEEE Transactions on Neural Networks and Learning Systems
Hanwen Ning, Guangyan Qing, Tianhai Tian, Xingjian Jing
In this paper, we propose a novel kernel method for the online identification of stochastic nonlinear spatiotemporal dynamical systems using the robust control approach. By the difference method, the stochastic spatiotemporal (SST) systems driven by multiplicative noise are first transformed into a class of multi-input-multi-output-partially linear kernel models (PLKMs) with heterogeneous random terms. With the help of techniques established for reproducing kernel Hilbert space, the online learning problem is reasonably considered as an output feedback control problem for a group of time varying linear dynamical systems...
June 28, 2018: IEEE Transactions on Neural Networks and Learning Systems
Rui Zhang, Feiping Nie, Xuelong Li
As for semisupervised learning, both label information and side information serve as pivotal indicators for the classification. Nonetheless, most of related research works utilize either label information or side information instead of exploiting both of them simultaneously. To address the referred defect, we propose a graph-based semisupervised learning (GSL) problem according to both given label information and side information. To solve the GSL problem efficiently, two novel self-weighted strategies are proposed based on solving associated equivalent counterparts of a GSL problem, which can be widely applied to a spectrum of biobjective optimizations...
June 28, 2018: IEEE Transactions on Neural Networks and Learning Systems
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