By Topic

Neural Networks, IEEE Transactions on

Issue 12 • Date Dec. 2011

Filter Results

Displaying Results 1 - 25 of 30
  • Table of contents

    Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (114 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Neural Networks publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (39 KB)  
    Freely Available from IEEE
  • Editorial: The Blossoming of the IEEE Transactions on Neural Networks

    Page(s): 1850
    Save to Project icon | Request Permissions | PDF file iconPDF (76 KB)  
    Freely Available from IEEE
  • Optimal Tracking Control for a Class of Nonlinear Discrete-Time Systems With Time Delays Based on Heuristic Dynamic Programming

    Page(s): 1851 - 1862
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (618 KB) |  | HTML iconHTML  

    In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the “backward iteration” is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Hierarchical Approximate Policy Iteration With Binary-Tree State Space Decomposition

    Page(s): 1863 - 1877
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (690 KB) |  | HTML iconHTML  

    In recent years, approximate policy iteration (API) has attracted increasing attention in reinforcement learning (RL), e.g., least-squares policy iteration (LSPI) and its kernelized version, the kernel-based LSPI algorithm. However, it remains difficult for API algorithms to obtain near-optimal policies for Markov decision processes (MDPs) with large or continuous state spaces. To address this problem, this paper presents a hierarchical API (HAPI) method with binary-tree state space decomposition for RL in a class of absorbing MDPs, which can be formulated as time-optimal learning control tasks. In the proposed method, after collecting samples adaptively in the state space of the original MDP, a learning-based decomposition strategy of sample sets was designed to implement the binary-tree state space decomposition process. Then, API algorithms were used on the sample subsets to approximate local optimal policies of sub-MDPs. The original MDP was decomposed into a binary-tree structure of absorbing sub-MDPs, constructed during the learning process, thus, local near-optimal policies were approximated by API algorithms with reduced complexity and higher precision. Furthermore, because of the improved quality of local policies, the combined global policy performed better than the near-optimal policy obtained by a single API algorithm in the original MDP. Three learning control problems, including path-tracking control of a real mobile robot, were studied to evaluate the performance of the HAPI method. With the same setting for basis function selection and sample collection, the proposed HAPI obtained better near-optimal policies than previous API methods such as LSPI and KLSPI. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Unified Development of Multiplicative Algorithms for Linear and Quadratic Nonnegative Matrix Factorization

    Page(s): 1878 - 1891
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (368 KB) |  | HTML iconHTML  

    Multiplicative updates have been widely used in approximative nonnegative matrix factorization (NMF) optimization because they are convenient to deploy. Their convergence proof is usually based on the minimization of an auxiliary upper-bounding function, the construction of which however remains specific and only available for limited types of dissimilarity measures. Here we make significant progress in developing convergent multiplicative algorithms for NMF. First, we propose a general approach to derive the auxiliary function for a wide variety of NMF problems, as long as the approximation objective can be expressed as a finite sum of monomials with real exponents. Multiplicative algorithms with theoretical guarantee of monotonically decreasing objective function sequence can thus be obtained. The solutions of NMF based on most commonly used dissimilarity measures such as α - and β-divergence as well as many other more comprehensive divergences can be derived by the new unified principle. Second, our method is extended to a nonseparable case that includes e.g., γ-divergence and Rényi divergence. Third, we develop multiplicative algorithms for NMF using second-order approximative factorizations, in which each factorizing matrix may appear twice. Preliminary numerical experiments demonstrate that the multiplicative algorithms developed using the proposed procedure can achieve satisfactory Karush-Kuhn-Tucker optimality. We also demonstrate NMF problems where algorithms by the conventional method fail to guarantee descent at each iteration but those by our principle are immune to such violation. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Subject to Linear Equality Constraints

    Page(s): 1892 - 1900
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1283 KB) |  | HTML iconHTML  

    In this paper, a one-layer recurrent neural network is presented for solving pseudoconvex optimization problems subject to linear equality constraints. The global convergence of the neural network can be guaranteed even though the objective function is pseudoconvex. The finite-time state convergence to the feasible region defined by the equality constraints is also proved. In addition, global exponential convergence is proved when the objective function is strongly pseudoconvex on the feasible region. Simulation results on illustrative examples and application on chemical process data reconciliation are provided to demonstrate the effectiveness and characteristics of the neural network. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Incremental Learning From Stream Data

    Page(s): 1901 - 1914
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (694 KB) |  | HTML iconHTML  

    Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Silicon Modeling of the Mihalaş–Niebur Neuron

    Page(s): 1915 - 1927
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (999 KB) |  | HTML iconHTML  

    There are a number of spiking and bursting neuron models with varying levels of complexity, ranging from the simple integrate-and-fire model to the more complex Hodgkin-Huxley model. The simpler models tend to be easily implemented in silicon but yet not biologically plausible. Conversely, the more complex models tend to occupy a large area although they are more biologically plausible. In this paper, we present the 0.5 μm complementary metal-oxide-semiconductor (CMOS) implementation of the Mihalaş-Niebur neuron model-a generalized model of the leaky integrate-and-fire neuron with adaptive threshold-that is able to produce most of the known spiking and bursting patterns that have been observed in biology. Our implementation modifies the original proposed model, making it more amenable to CMOS implementation and more biologically plausible. All but one of the spiking properties-tonic spiking, class 1 spiking, phasic spiking, hyperpolarized spiking, rebound spiking, spike frequency adaptation, accommodation, threshold variability, integrator and input bistability-are demonstrated in this model. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • SaFIN: A Self-Adaptive Fuzzy Inference Network

    Page(s): 1928 - 1940
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (655 KB) |  | HTML iconHTML  

    There are generally two approaches to the design of a neural fuzzy system: (1) design by human experts, and (2) design through a self-organization of the numerical training data. While the former approach is highly subjective, the latter is commonly plagued by one or more of the following major problems: (1) an inconsistent rulebase; (2) the need for prior knowledge such as the number of clusters to be computed; (3) heuristically designed knowledge acquisition methodologies; and (4) the stability-plasticity tradeoff of the system. This paper presents a novel self-organizing neural fuzzy system, named Self-Adaptive Fuzzy Inference Network (SaFIN), to address the aforementioned deficiencies. The proposed SaFIN model employs a new clustering technique referred to as categorical learning-induced partitioning (CLIP), which draws inspiration from the behavioral category learning process demonstrated by humans. By employing the one-pass CLIP, SaFIN is able to incorporate new clusters in each input-output dimension when the existing clusters are not able to give a satisfactory representation of the incoming training data. This not only avoids the need for prior knowledge regarding the number of clusters needed for each input-output dimension, but also allows SaFIN the flexibility to incorporate new knowledge with old knowledge in the system. In addition, the self-automated rule formation mechanism proposed within SaFIN ensures that it obtains a consistent resultant rulebase. Subsequently, the proposed SaFIN model is employed in a series of benchmark simulations to demonstrate its efficiency as a self-organizing neural fuzzy system, and excellent performances have been achieved. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonlinear System Identification by Gustafson–Kessel Fuzzy Clustering and Supervised Local Model Network Learning for the Drug Absorption Spectra Process

    Page(s): 1941 - 1951
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (691 KB) |  | HTML iconHTML  

    This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.e., unsupervised learning, is applied. In combination with the LMN learning procedure, a new incremental method to define the number and the initial locations of the cluster centers for the GK clustering algorithm is proposed. Each data cluster corresponds to a local region of the process and is modeled with a local linear model. Since the validity functions are calculated from the fuzzy covariance matrices of the clusters, they are highly adaptable and thus the process can be described with a very sparse amount of local models, i.e., with a parsimonious LMN model. The proposed method for constructing the LMN is finally tested on a drug absorption spectral process and compared to two other methods, namely, Lolimot and Hilomot. The comparison between the experimental results when using each method shows the usefulness of the proposed identification algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Modeling Activity-Dependent Plasticity in BCM Spiking Neural Networks With Application to Human Behavior Recognition

    Page(s): 1952 - 1966
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (715 KB) |  | HTML iconHTML  

    Spiking neural networks (SNNs) are considered to be computationally more powerful than conventional NNs. However, the capability of SNNs in solving complex real-world problems remains to be demonstrated. In this paper, we propose a substantial extension of the Bienenstock, Cooper, and Munro (BCM) SNN model, in which the plasticity parameters are regulated by a gene regulatory network (GRN). Meanwhile, the dynamics of the GRN is dependent on the activation levels of the BCM neurons. We term the whole model “GRN-BCM.” To demonstrate its computational power, we first compare the GRN-BCM with a standard BCM, a hidden Markov model, and a reservoir computing model on a complex time series classification problem. Simulation results indicate that the GRN-BCM significantly outperforms the compared models. The GRN-BCM is then applied to two widely used datasets for human behavior recognition. Comparative results on the two datasets suggest that the GRN-BCM is very promising for human behavior recognition, although the current experiments are still limited to the scenarios in which only one object is moving in the considered video sequences. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Fast Independent Component Analysis Algorithm for Quaternion Valued Signals

    Page(s): 1967 - 1978
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1501 KB) |  | HTML iconHTML  

    An extension of the fast independent component analysis algorithm is proposed for the blind separation of both BBQ-proper and BBQ-improper quaternion-valued signals. This is achieved by maximizing a negentropy-based cost function, and is derived rigorously using the recently developed mbiBBHBBR calculus in order to implement Newton optimization in the augmented quaternion statistics framework. It is shown that the use of augmented statistics and the associated widely linear modeling provides theoretical and practical advantages when dealing with general quaternion signals with noncircular (rotation-dependent) distributions. Simulations using both benchmark and real-world quaternion-valued signals support the approach. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Synchronization of Continuous Dynamical Networks With Discrete-Time Communications

    Page(s): 1979 - 1986
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (389 KB) |  | HTML iconHTML  

    In this paper, synchronization of continuous dynamical networks with discrete-time communications is studied. Though the dynamical behavior of each node is continuous-time, the communications between every two different nodes are discrete-time, i.e., they are active only at some discrete time instants. Moreover, the communication intervals between every two communication instants can be uncertain and variable. By choosing a piecewise Lyapunov-Krasovskii functional to govern the characteristics of the discrete communication instants and by utilizing a convex combination technique, a synchronization criterion is derived in terms of linear matrix inequalities with an upper bound for the communication intervals obtained. The results extend and improve upon earlier work. Simulation results show the effectiveness of the proposed communication scheme. Some relationships between the allowable upper bound of communication intervals and the coupling strength of the network are illustrated through simulations on a fully connected network, a star-like network, and a nearest neighbor network. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Quantitative Analysis of Nonlinear Embedding

    Page(s): 1987 - 1998
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1604 KB) |  | HTML iconHTML  

    A lot of nonlinear embedding techniques have been developed to recover the intrinsic low-dimensional manifolds embedded in the high-dimensional space. However, the quantitative evaluation criteria are less studied in literature. The embedding quality is usually evaluated by visualization which is subjective and qualitative. The few existing evaluation methods to estimate the embedding quality, neighboring preservation rate for example, are not widely applicable. In this paper, we propose several novel criteria for quantitative evaluation, by considering the global smoothness and co-directional consistence of the nonlinear embedding algorithms. The proposed criteria are geometrically intuitive, simple, and easy to implement with a low computational cost. Experiments show that our criteria capture some new geometrical properties of the nonlinear embedding algorithms, and can be used as a guidance to deal with the embedding of the out-of-samples. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Exponential Synchronization of Complex Networks With Finite Distributed Delays Coupling

    Page(s): 1999 - 2010
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1138 KB) |  | HTML iconHTML  

    In this paper, the exponential synchronization for a class of complex networks with finite distributed delays coupling is studied via periodically intermittent control. Some novel and useful criteria are derived by utilizing a different technique compared with some correspondingly previous results. As a special case, some sufficient conditions ensuring the exponential synchronization for a class of coupled neural networks with distributed delays are obtained. Furthermore, a feasible region of the control parameters is derived for the realization of exponential synchronization. It is worth noting that the synchronized state in this paper is not an isolated node but a non-decoupled state, in which the inner coupling matrix and the degree of the nodes play a central role. Additionally, the traditional assumptions on control width, non-control width, and discrete delays are removed in our results. Finally, some numerical simulations are given to demonstrate the effectiveness of the proposed control method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Bayesian Multitask Classification With Gaussian Process Priors

    Page(s): 2011 - 2021
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (396 KB) |  | HTML iconHTML  

    We present a novel approach to multitask learning in classification problems based on Gaussian process (GP) classification. The method extends previous work on multitask GP regression, constraining the overall covariance (across tasks and data points) to factorize as a Kronecker product. Fully Bayesian inference is possible but time consuming using sampling techniques. We propose approximations based on the popular variational Bayes and expectation propagation frameworks, showing that they both achieve excellent accuracy when compared to Gibbs sampling, in a fraction of time. We present results on a toy dataset and two real datasets, showing improved performance against the baseline results obtained by learning each task independently. We also compare with a recently proposed state-of-the-art approach based on support vector machines, obtaining comparable or better results. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Data Mining Based Full Ceramic Bearing Fault Diagnostic System Using AE Sensors

    Page(s): 2022 - 2031
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (738 KB) |  | HTML iconHTML  

    Full ceramic bearings are considered the first step toward full ceramic, oil-free engines in the future. No research on full ceramic bearing fault diagnostics using acoustic emission (AE) sensors has been reported. Unlike their steel counterparts, signal processing methods to extract effective AE fault characteristic features and fault diagnostic systems for full ceramic bearings have not been developed. In this paper, a data mining based full ceramic bearing diagnostic system using AE based condition indicators (CIs) is presented. The system utilizes a new signal processing method based on Hilbert Huang transform to extract AE fault features for the computation of CIs. These CIs are used to build a data mining based fault classifier using a k-nearest neighbor algorithm. Seeded fault tests on full ceramic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and AE burst data are collected. The effectiveness of the developed fault diagnostic system is validated using real full ceramic bearing seeded fault test data. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Mobility Timing for Agent Communities, a Cue for Advanced Connectionist Systems

    Page(s): 2032 - 2049
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (908 KB) |  | HTML iconHTML  

    We introduce a wait-and-chase scheme that models the contact times between moving agents within a connectionist construct. The idea that elementary processors move within a network to get a proper position is borne out both by biological neurons in the brain morphogenesis and by agents within social networks. From the former, we take inspiration to devise a medium-term project for new artificial neural network training procedures where mobile neurons exchange data only when they are close to one another in a proper space (are in contact). From the latter, we accumulate mobility tracks experience. We focus on the preliminary step of characterizing the elapsed time between neuron contacts, which results from a spatial process fitting in the family of random processes with memory, where chasing neurons are stochastically driven by the goal of hitting target neurons. Thus, we add an unprecedented mobility model to the literature in the field, introducing a distribution law of the intercontact times that merges features of both negative exponential and Pareto distribution laws. We give a constructive description and implementation of our model, as well as a short analytical form whose parameters are suitably estimated in terms of confidence intervals from experimental data. Numerical experiments show the model and related inference tools to be sufficiently robust to cope with two main requisites for its exploitation in a neural network: the nonindependence of the observed intercontact times and the feasibility of the model inversion problem to infer suitable mobility parameters. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Classifiability-Based Discriminatory Projection Pursuit

    Page(s): 2050 - 2061
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1035 KB) |  | HTML iconHTML  

    Fisher's linear discriminant (FLD) is one of the most widely used linear feature extraction method, especially in many visual computation tasks. Based on the analysis on several limitations of the traditional FLD, this paper attempts to propose a new computational paradigm for discriminative linear feature extraction, named “classifiability-based discriminatory projection pursuit” (CDPP), which is different from the traditional FLD and its variants. There are two steps in the proposed CDPP: one is the construction of a candidate projection set (CPS), and the other is the pursuit of discriminatory projections. Specifically, in the former step, candidate projections are generated by using the nearest between-class boundary samples, while the latter is efficiently achieved by classifiability-based AdaBoost learning from the CPS. We show that the new “projection pursuit” paradigm not only does not suffer from the limitations of the traditional FLD but also inherits good generalizability from the boundary attribute of candidate projections. Extensive experiments on both synthetic and real datasets validate the effectiveness of CDPP for discriminative linear feature extraction. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Bioinspired Neural Network for Real-Time Cooperative Hunting by Multirobots in Unknown Environments

    Page(s): 2062 - 2077
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1075 KB) |  | HTML iconHTML  

    Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path planning, and collision avoidance), but also need to cooperate in order to pursue and catch the evaders efficiently. In this paper, a novel approach based on a bioinspired neural network is proposed for the real-time cooperative hunting by multirobots, where the locations of evaders and the environment are unknown and changing. The bioinspired neural network is used for cooperative pursuing by the multirobot team. Some other algorithms are used to enable the robots to catch the evaders efficiently, such as the dynamic alliance and formation construction algorithm. In the proposed approach, the pursuing alliances can dynamically change and the robot motion can be adjusted in real-time to pursue the evader cooperatively, to guarantee that all the evaders can be caught efficiently. The proposed approach can deal with various situations such as when some robots break down, the environment has different boundary shapes, or the obstacles are linked with different shapes. The simulation results show that the proposed approach is capable of guiding the robots to achieve the hunting of multiple evaders in real-time efficiently. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Auto-Regressive Processes Explained by Self-Organized Maps. Application to the Detection of Abnormal Behavior in Industrial Processes

    Page(s): 2078 - 2090
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (708 KB) |  | HTML iconHTML  

    This paper analyzes the expected time evolution of an auto-regressive (AR) process using self-organized maps (SOM). It investigates how a SOM captures the time information given by the AR input process and how the transitions from one neuron to another one can be understood under a probabilistic perspective. In particular, regions of the map into which the AR process is expected to move are identified. This characterization allows detecting anomalous changes in the AR process structure or parameters. On the basis of the theoretical results, an anomaly detection method is proposed and applied to a real industrial process. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Parallel Programmable Asynchronous Neighborhood Mechanism for Kohonen SOM Implemented in CMOS Technology

    Page(s): 2091 - 2104
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1374 KB) |  | HTML iconHTML  

    We present a new programmable neighborhood mechanism for hardware implemented Kohonen self-organizing maps (SOMs) with three different map topologies realized on a single chip. The proposed circuit comes as a fully parallel and asynchronous architecture. The mechanism is very fast. In a medium sized map with several hundreds neurons implemented in the complementary metal-oxide semiconductor 0.18 μm technology, all neurons start adapting the weights after no more than 11 ns. The adaptation is then carried out in parallel. This is an evident advantage in comparison with the commonly used software-realized SOMs. The circuit is robust against the process, supply voltage and environment temperature variations. Due to a simple structure, it features low energy consumption of a few pJ per neuron per a single learning pattern. In this paper, we discuss different aspects of hardware realization, such as a suitable selection of the map topology and the initial neighborhood range, as the optimization of these parameters is essential when looking from the circuit complexity point of view. For the optimal values of these parameters, the chip area and the power dissipation can be reduced even by 60% and 80%, respectively, without affecting the quality of learning. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Passivity and Stability Analysis of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions

    Page(s): 2105 - 2116
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB) |  | HTML iconHTML  

    This paper is concerned with the passivity and stability problems of reaction-diffusion neural networks (RDNNs) in which the input and output variables are varied with the time and space variables. By utilizing the Lyapunov functional method combined with the inequality techniques, some sufficient conditions ensuring the passivity and global exponential stability are derived. Furthermore, when the parameter uncertainties appear in RDNNs, several criteria for robust passivity and robust global exponential stability are also presented. Finally, a numerical example is provided to illustrate the effectiveness of the proposed criteria. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering

    Page(s): 2117 - 2131
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1201 KB) |  | HTML iconHTML  

    Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.

 

This Transactions ceased production in 2011. The current retitled publication is IEEE Transactions on Neural Networks and Learning Systems.

Full Aims & Scope