By Topic

Neural Networks, IEEE Transactions on

Issue 9 • Date Sept. 2009

Filter Results

Displaying Results 1 - 16 of 16
  • Table of contents

    Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (37 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Neural Networks publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (38 KB)  
    Freely Available from IEEE
  • Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems

    Page(s): 1377 - 1384
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1112 KB) |  | HTML iconHTML  

    This paper presents a self-organizing control system based on cerebellar model articulation controller (CMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding-mode control (SMC), so the input space dimension of CMAC can be simplified. The structure of CMAC will be self-organized; that is, the layers of CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The control system consists of a self-organizing CMAC (SOCM) and a robust controller. SOCM containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient-descent method is used to online tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. A simulation study of inverted double pendulums system and an experimental result of linear ultrasonic motor motion control show that favorable tracking performance can be achieved by using the proposed control system. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multilayer SOM With Tree-Structured Data for Efficient Document Retrieval and Plagiarism Detection

    Page(s): 1385 - 1402
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1881 KB) |  | HTML iconHTML  

    This paper proposes a new document retrieval (DR) and plagiarism detection (PD) system using multilayer self-organizing map (MLSOM). A document is modeled by a rich tree-structured representation, and a SOM-based system is used as a computationally effective solution. Instead of relying on keywords/lines, the proposed scheme compares a full document as a query for performing retrieval and PD. The tree-structured representation hierarchically includes document features as document, pages, and paragraphs. Thus, it can reflect underlying context that is difficult to acquire from the currently used word-frequency information. We show that the tree-structured data is effective for DR and PD. To handle tree-structured representation in an efficient way, we use an MLSOM algorithm, which was previously developed by the authors for the application of image retrieval. In this study, it serves as an effective clustering algorithm. Using the MLSOM, local matching techniques are developed for comparing text documents. Two novel MLSOM-based PD methods are proposed. Detailed simulations are conducted and the experimental results corroborate that the proposed approach is computationally efficient and accurate for DR and PD. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination

    Page(s): 1403 - 1416
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2993 KB) |  | HTML iconHTML  

    Time-to-event analysis is important in a wide range of applications from clinical prognosis to risk modeling for credit scoring and insurance. In risk modeling, it is sometimes required to make a simultaneous assessment of the hazard arising from two or more mutually exclusive factors. This paper applies to an existing neural network model for competing risks (PLANNCR), a Bayesian regularization with the standard approximation of the evidence to implement automatic relevance determination (PLANNCR-ARD). The theoretical framework for the model is described and its application is illustrated with reference to local and distal recurrence of breast cancer, using the data set of Veronesi (1995). View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • CAVIAR: A 45k Neuron, 5M Synapse, 12G Connects/s AER Hardware Sensory–Processing– Learning–Actuating System for High-Speed Visual Object Recognition and Tracking

    Page(s): 1417 - 1438
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3434 KB) |  | HTML iconHTML  

    This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asynchronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45 k neurons (spiking cells), up to 5 M synapses, performs 12 G synaptic operations per second, and achieves millisecond object recognition and tracking latencies. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Wavelet Differential Neural Network Observer

    Page(s): 1439 - 1449
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (327 KB) |  | HTML iconHTML  

    State estimation for uncertain systems affected by external noises is an important problem in control theory. This paper deals with a state observation problem when the dynamic model of a plant contains uncertainties or it is completely unknown. Differential neural network (NN) approach is applied in this uninformative situation but with activation functions described by wavelets. A new learning law, containing an adaptive adjustment rate, is suggested to imply the stability condition for the free parameters of the observer. Nominal weights are adjusted during the preliminary training process using the least mean square (LMS) method. Lyapunov theory is used to obtain the upper bounds for the weights dynamics as well as for the mean squared estimation error. Two numeric examples illustrate this approach: first, a nonlinear electric system, governed by the Chua's equation and second the Lorentz oscillator. Both systems are assumed to be affected by external perturbations and their parameters are unknown. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Processing Short-Term and Long-Term Information With a Combination of Polynomial Approximation Techniques and Time-Delay Neural Networks

    Page(s): 1450 - 1462
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1023 KB) |  | HTML iconHTML  

    Neural networks are often used to process temporal information, i.e., any kind of information related to time series. In many cases, time series contain short-term and long-term trends or behavior. This paper presents a new approach to capture temporal information with various reference periods simultaneously. A least squares approximation of the time series with orthogonal polynomials will be used to describe short-term trends contained in a signal (average, increase, curvature, etc.). Long-term behavior will be modeled with the tapped delay lines of a time-delay neural network (TDNN). This network takes the coefficients of the orthogonal expansion of the approximating polynomial as inputs such considering short-term and long-term information efficiently. The advantages of the method will be demonstrated by means of artificial data and two real-world application examples, the prediction of the user number in a computer network and online tool wear classification in turning. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Complex-Valued Multistate Associative Memory With Nonlinear Multilevel Functions for Gray-Level Image Reconstruction

    Page(s): 1463 - 1473
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7160 KB) |  | HTML iconHTML  

    A widely used complex-valued activation function for complex-valued multistate Hopfield networks is revealed to be essentially based on a multilevel step function. By replacing the multilevel step function with other multilevel characteristics, we present two alternative complex-valued activation functions. One is based on a multilevel sigmoid function, while the other on a characteristic of a multistate bifurcating neuron. Numerical experiments show that both modifications to the complex-valued activation function bring about improvements in network performance for a multistate associative memory. The advantage of the proposed networks over the complex-valued Hopfield networks with the multilevel step function is more outstanding when a complex-valued neuron represents a larger number of multivalued states. Further, the performance of the proposed networks in reconstructing noisy 256 gray-level images is demonstrated in comparison with other recent associative memories to clarify their advantages and disadvantages. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Probabilistic PCA Self-Organizing Maps

    Page(s): 1474 - 1489
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4046 KB) |  | HTML iconHTML  

    In this paper, we present a probabilistic neural model, which extends Kohonen's self-organizing map (SOM) by performing a probabilistic principal component analysis (PPCA) at each neuron. Several SOMs have been proposed in the literature to capture the local principal subspaces, but our approach offers a probabilistic model while it has a low complexity on the dimensionality of the input space. This allows to process very high-dimensional data to obtain reliable estimations of the probability densities which are based on the PPCA framework. Experimental results are presented, which show the map formation capabilities of the proposal with high-dimensional data, and its potential in image and video compression applications. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints

    Page(s): 1490 - 1503
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1070 KB) |  | HTML iconHTML  

    In this paper, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved by iterative adaptive dynamic programming algorithm. First, a novel nonquadratic performance functional is introduced to overcome the control constraints, and then an iterative adaptive dynamic programming algorithm is developed to solve the optimal feedback control problem of the original constrained system with convergence analysis. In the present control scheme, there are three neural networks used as parametric structures for facilitating the implementation of the iterative algorithm. Two examples are given to demonstrate the convergence and feasibility of the proposed optimal control scheme. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Supplier Selection Based on a Neural Network Model Using Genetic Algorithm

    Page(s): 1504 - 1519
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1211 KB) |  | HTML iconHTML  

    In this paper, a decision-making model was developed to select suppliers using neural networks (NNs). This model used historical supplier performance data for selection of vendor suppliers. Input and output were designed in a unique manner for training purposes. The managers' judgments about suppliers were simulated by using a pairwise comparisons matrix for output estimation in the NN. To obtain the benefit of a search technique for model structure and training, genetic algorithm (GA) was applied for the initial weights and architecture of the network. The suppliers' database information (input) can be updated over time to change the suppliers' score estimation based on their performance. The case study illustrated shows how the model can be applied for suppliers' selection. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Projection-Based Adaptive Neurocontrol With Switching Logic Deadzone Tuning

    Page(s): 1520 - 1527
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (508 KB) |  | HTML iconHTML  

    In this brief, an adaptive neural network (NN) controller is proposed for multiple-input-multiple-output (MIMO) nonlinear systems with triangular control structure and unknown control directions. Deadzones are employed in the projection-based NN weight learning laws and the Nussbaum parameter update laws with levels tuned by an innovative switching logic tuning mechanism. Detailed analysis using a family of Lyapunov-like integral functions and the function approximation capability of NNs proves that all the tracking errors are semiglobal uniform ultimate bounded in small neighborhoods of the origin while the closed-loop system variables (state vector, NN weights, Nussbaum parameters) and the control law remain bounded. A simulation study confirms the theoretical results and verifies the effectiveness of the proposed design. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 2010 IEEE World Congress on Computational Intelligence (WCCI)

    Page(s): 1528
    Save to Project icon | Request Permissions | PDF file iconPDF (755 KB)  
    Freely Available from IEEE
  • IEEE Computational Intelligence Society Information

    Page(s): C3
    Save to Project icon | Request Permissions | PDF file iconPDF (36 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Neural Networks Information for authors

    Page(s): C4
    Save to Project icon | Request Permissions | PDF file iconPDF (39 KB)  
    Freely Available from IEEE

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