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IEEE Transactions on Neural Networks

Issue 6 • Date Nov. 2004

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Displaying Results 1 - 25 of 27
  • Table of contents

    Publication Year: 2004, Page(s):c1 - c4
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  • IEEE Transactions on Neural Networks publication information

    Publication Year: 2004, Page(s): c2
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  • A general framework for learning rules from data

    Publication Year: 2004, Page(s):1333 - 1349
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1045 KB) | HTML iconHTML

    With the aim of getting understandable symbolic rules to explain a given phenomenon, we split the task of learning these rules from sensory data in two phases: a multilayer perceptron maps features into propositional variables and a set of subsequent layers operated by a PAC-like algorithm learns Boolean expressions on these variables. The special features of this procedure are that: i) the neural... View full abstract»

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  • Feedforward sigmoidal networks - equicontinuity and fault-tolerance properties

    Publication Year: 2004, Page(s):1350 - 1366
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (461 KB) | HTML iconHTML

    Sigmoidal feedforward artificial neural networks (FFANNs) have been established to be universal approximators of continuous functions. The universal approximation results are summarized to identify the function sets represented by the sigmoidal FFANNs with the universal approximation properties. The equicontinuous properties of the identified sets is analyzed. The equicontinuous property is relate... View full abstract»

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  • Context-dependent neural nets-structures and learning

    Publication Year: 2004, Page(s):1367 - 1377
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (648 KB) | HTML iconHTML

    A novel approach toward neural networks modeling is presented in the paper. It is unique in the fact that allows nets' weights to change according to changes of some environmental factors even after completing the learning process. The models of context-dependent (cd) neuron, one- and multilayer feedforward net are presented, with basic learning algorithms and examples of functioning. The Vapnik-C... View full abstract»

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  • Novel direct and self-regulating approaches to determine optimum growing multi-experts network structure

    Publication Year: 2004, Page(s):1378 - 1395
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (883 KB) | HTML iconHTML

    This work presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the... View full abstract»

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  • Contextual processing of structured data by recursive cascade correlation

    Publication Year: 2004, Page(s):1396 - 1410
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1117 KB) | HTML iconHTML

    This paper propose a first approach to deal with contextual information in structured domains by recursive neural networks. The proposed model, i.e., contextual recursive cascade correlation (CRCC), a generalization of the recursive cascade correlation (RCC) model, is able to partially remove the causality assumption by exploiting contextual information stored in frozen units. We formally characte... View full abstract»

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  • Magnified gradient function with deterministic weight modification in adaptive learning

    Publication Year: 2004, Page(s):1411 - 1423
    Cited by:  Papers (39)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (569 KB) | HTML iconHTML

    This work presents two novel approaches, backpropagation (BP) with magnified gradient function (MGFPROP) and deterministic weight modification (DWM), to speed up the convergence rate and improve the global convergence capability of the standard BP learning algorithm. The purpose of MGFPROP is to increase the convergence rate by magnifying the gradient function of the activation function, while the... View full abstract»

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  • Hidden space support vector machines

    Publication Year: 2004, Page(s):1424 - 1434
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (681 KB) | HTML iconHTML

    Hidden space support vector machines (HSSVMs) are presented in this paper. The input patterns are mapped into a high-dimensional hidden space by a set of hidden nonlinear functions and then the structural risk is introduced into the hidden space to construct HSSVMs. Moreover, the conditions for the nonlinear kernel function in HSSVMs are more relaxed, and even differentiability is not required. Co... View full abstract»

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  • Encoding nondeterministic fuzzy tree automata into recursive neural networks

    Publication Year: 2004, Page(s):1435 - 1449
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (942 KB) | HTML iconHTML

    Fuzzy neural systems have been a subject of great interest in the last few years, due to their abilities to facilitate the exchange of information between symbolic and subsymbolic domains. However, the models in the literature are not able to deal with structured organization of information, that is typically required by symbolic processing. In many application domains, the patterns are not only s... View full abstract»

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  • Reproducing chaos by variable structure recurrent neural networks

    Publication Year: 2004, Page(s):1450 - 1457
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (602 KB) | HTML iconHTML

    In this paper, we present a new approach for chaos reproduction using variable structure recurrent neural networks (VSRNN). A neural network identifier is designed, with a variable structure that will change according to its output performance as compared to the given orbits of an unknown chaotic systems. A tradeoff between identification errors and computational complexity is discussed. View full abstract»

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  • A Hopfield network learning method for bipartite subgraph problem

    Publication Year: 2004, Page(s):1458 - 1465
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (440 KB) | HTML iconHTML

    We present a gradient ascent learning method of the Hopfield neural network for bipartite subgraph problem. The method is intended to provide a near-optimum parallel algorithm for solving the bipartite subgraph problem. To do this we use the Hopfield neural network to get a near-maximum bipartite subgraph, and increase the energy by modifying weights in a gradient ascent direction of the energy to... View full abstract»

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  • Heterogeneous fuzzy logic networks: fundamentals and development studies

    Publication Year: 2004, Page(s):1466 - 1481
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2045 KB) | HTML iconHTML

    The recent trend in the development of neurofuzzy systems has profoundly emphasized the importance of synergy between the fundamentals of fuzzy sets and neural networks. The resulting frameworks of the neurofuzzy systems took advantage of an array of learning mechanisms primarily originating within the theory of neurocomputing and the use of fuzzy models (predominantly rule-based systems) being we... View full abstract»

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  • Robust redesign of a neural network controller in the presence of unmodeled dynamics

    Publication Year: 2004, Page(s):1482 - 1490
    Cited by:  Papers (23)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (419 KB) | HTML iconHTML

    This work presents a neural network control redesign, which achieves robust stabilization in the presence of unmodeled dynamics restricted to be input to output practically stable (IOpS), without requiring any prior knowledge on any bounding function. Moreover, the state of the unmodeled dynamics is permitted to go unbounded provided that the nominal system state and/or the control input also go u... View full abstract»

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  • Adaptive hybrid control for linear piezoelectric ceramic motor drive using diagonal recurrent CMAC network

    Publication Year: 2004, Page(s):1491 - 1506
    Cited by:  Papers (33)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (913 KB) | HTML iconHTML

    This work presents an adaptive hybrid control system using a diagonal recurrent cerebellar-model-articulation-computer (DRCMAC) network to control a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive hybrid control system ... View full abstract»

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  • An adaptive H controller design for bank-to-turn missiles using ridge Gaussian neural networks

    Publication Year: 2004, Page(s):1507 - 1516
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (470 KB) | HTML iconHTML

    A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotat... View full abstract»

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  • The pre-image problem in kernel methods

    Publication Year: 2004, Page(s):1517 - 1525
    Cited by:  Papers (170)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4413 KB) | HTML iconHTML

    In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applications, such as on using kernel principal component analysis (PCA) for image denoising. Unlike the traditional method in which relies on nonlinear optimization, our proposed method directly finds the location of the pre-imag... View full abstract»

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  • Adaptive stochastic resonance in noisy neurons based on mutual information

    Publication Year: 2004, Page(s):1526 - 1540
    Cited by:  Papers (55)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (881 KB) | HTML iconHTML

    Noise can improve how memoryless neurons process signals and maximize their throughput information. Such favorable use of noise is the so-called "stochastic resonance" or SR effect at the level of threshold neurons and continuous neurons. This work presents theoretical and simulation evidence that 1) lone noisy threshold and continuous neurons exhibit the SR effect in terms of the mutual informati... View full abstract»

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  • A neural network learning for adaptively extracting cross-correlation features between two high-dimensional data streams

    Publication Year: 2004, Page(s):1541 - 1554
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (406 KB) | HTML iconHTML

    This paper proposes a novel cross-correlation neural network (CNN) model for finding the principal singular subspace of a cross-correlation matrix between two high-dimensional data streams. We introduce a novel nonquadratic criterion (NQC) for searching the optimum weights of two linear neural networks (LNN). The NQC exhibits a single global minimum attained if and only if the weight matrices of t... View full abstract»

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  • Fusing images with different focuses using support vector machines

    Publication Year: 2004, Page(s):1555 - 1561
    Cited by:  Papers (64)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1092 KB) | HTML iconHTML

    Many vision-related processing tasks, such as edge detection, image segmentation and stereo matching, can be performed more easily when all objects in the scene are in good focus. However, in practice, this may not be always feasible as optical lenses, especially those with long focal lengths, only have a limited depth of field. One common approach to recover an everywhere-in-focus image is to use... View full abstract»

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  • New dynamical optimal learning for linear multilayer FNN

    Publication Year: 2004, Page(s):1562 - 1570
    Cited by:  Papers (20)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (349 KB) | HTML iconHTML

    This letter presents a new dynamical optimal learning (DOL) algorithm for three-layer linear neural networks and investigates its generalization ability. The optimal learning rates can be fully determined during the training process. The mean squared error (mse) is guaranteed to be stably decreased and the learning is less sensitive to initial parameter settings. The simulation results illustrate ... View full abstract»

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  • A columnar competitive model for solving combinatorial optimization problems

    Publication Year: 2004, Page(s):1568 - 1574
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (198 KB) | HTML iconHTML

    The major drawbacks of the Hopfield network when it is applied to some combinatorial problems, e.g., the traveling salesman problem (TSP), are invalidity of the obtained solutions, trial-and-error setting value process of the network parameters and low-computation efficiency. This letter presents a columnar competitive model (CCM) which incorporates winner-takes-all (WTA) learning rule for solving... View full abstract»

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  • International Joint Conference on Neural Networks (IJCNN 2005)

    Publication Year: 2004, Page(s): 1574
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  • The 2nd International Neural Conference on Engineering

    Publication Year: 2004, Page(s): 1575
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  • Have you visited lately? www.ieee.org [advertisement]

    Publication Year: 2004, Page(s): 1576
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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