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IEEE Transactions on Neural Networks

Issue 3 • May 1997

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Displaying Results 1 - 25 of 37
  • Comments on "Diagonal recurrent neural networks for dynamic systems control". Reproof of theorems 2 and 4 [with reply]

    Publication Year: 1997, Page(s):811 - 814
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (98 KB)

    In their original paper, C.-C. Ku and K.Y. Lee (ibid., vol.6, p.144-56, 1995) designed a diagonal recurrent neural network architecture for control systems. Liang asserts that a condition assumed in the proof of its convergence does not necessarily apply, and presents alternative theorems and proofs. Lee replies that Liang has misunderstood the original paper, and also that he made mistakes in his... View full abstract»

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  • Author's reply And Revision For Time-varying Weights

    Publication Year: 1997, Page(s):813 - 814
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (67 KB)

    First Page of the Article
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  • Pattern Recognition And Neural Networks [Book Reviews]

    Publication Year: 1997, Page(s):815 - 816
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    Freely Available from IEEE
  • Neural Network Design [Books in Brief]

    Publication Year: 1997, Page(s): 817
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    Freely Available from IEEE
  • Computational Intelligence Pc Tools [Books in Brief]

    Publication Year: 1997, Page(s): 817
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    Freely Available from IEEE
  • The effects of limited-precision weights on the threshold Adaline

    Publication Year: 1997, Page(s):549 - 552
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (148 KB)

    The effect of limited-precision weights on the functional capability of a threshold Adaline is examined. The number of logic functions which can be implemented by the threshold Adaline serves as the primary measure of functional capability. Closed-form expressions are provided for the number of logic functions which can be implemented by a threshold Adaline with four different levels of weight pre... View full abstract»

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  • Implementations of artificial neural networks using current-mode pulse width modulation technique

    Publication Year: 1997, Page(s):532 - 548
    Cited by:  Papers (16)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (532 KB)

    The use of a current-mode pulse width modulation (CM-PWM) technique to implement analog artificial neural networks (ANNs) is presented. This technique can be used to efficiently implement the weighted summation operation (WSO) that are required in the realization of a general ANN. The sigmoidal transformation is inherently performed by the nonlinear transconductance amplifier, which is a key compo... View full abstract»

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  • An iterative pruning algorithm for feedforward neural networks

    Publication Year: 1997, Page(s):519 - 531
    Cited by:  Papers (118)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (340 KB)

    The problem of determining the proper size of an artificial neural network is recognized to be crucial, especially for its practical implications in such important issues as learning and generalization. One popular approach for tackling this problem is commonly known as pruning and it consists of training a larger than necessary network and then removing unnecessary weights/nodes. In this paper, a... View full abstract»

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  • On convergence properties of pocket algorithm

    Publication Year: 1997, Page(s):623 - 629
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB)

    The problem of finding optimal weights for a single threshold neuron starting from a general training set is considered. Among the variety of possible learning techniques, the pocket algorithm has a proper convergence theorem which asserts its optimality. However, the original proof ensures the asymptotic achievement of an optimal weight vector only if the inputs in the training set are integer or... View full abstract»

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  • A methodology for constructing fuzzy algorithms for learning vector quantization

    Publication Year: 1997, Page(s):505 - 518
    Cited by:  Papers (46)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (612 KB)

    This paper presents a general methodology for the development of fuzzy algorithms for learning vector quantization (FALVQ). The design of specific FALVQ algorithms according to existing approaches reduces to the selection of the membership function assigned to the weight vectors of an LVQ competitive neural network, which represent the prototypes. The development of a broad variety of FALVQ algori... View full abstract»

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  • Transmission of vector quantized data over a noisy channel

    Publication Year: 1997, Page(s):582 - 589
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (240 KB)

    In the transmission of vector quantized data, the vector quantizer and the communication system are usually designed separately. With such an approach, the channel noise results in significant degradations in the performance of the vector quantizer. To solve this problem, we should properly create the mapping from the codebook of the quantizer to the channel signal set of the communication system.... View full abstract»

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  • Supervised learning of perceptron and output feedback dynamic networks: a feedback analysis via the small gain theorem

    Publication Year: 1997, Page(s):612 - 622
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (424 KB)

    This paper provides a time-domain feedback analysis of the perceptron learning algorithm and of training schemes for dynamic networks with output feedback. It studies the robustness performance of the algorithms in the presence of uncertainties that might be due to noisy perturbations in the reference signals or due to modeling mismatch. In particular, bounds are established on the step-size param... View full abstract»

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  • A class of neural networks for independent component analysis

    Publication Year: 1997, Page(s):486 - 504
    Cited by:  Papers (204)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (640 KB)

    Independent component analysis (ICA) is a recently developed, useful extension of standard principal component analysis (PCA). The ICA model is utilized mainly in blind separation of unknown source signals from their linear mixtures. In this application only the source signals which correspond to the coefficients of the ICA expansion are of interest. In this paper, we propose neural structures rel... View full abstract»

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  • Hierarchical graph visualization using neural networks

    Publication Year: 1997, Page(s):794 - 799
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (200 KB)

    An algorithm based on a Hopfield network for solving the hierarchical graph visualization problem is presented. It simultaneously minimizes the number of crossings and total path length to produce two-dimensional drawings easily interpreted by human observers. Traditional heuristics often follow a more local optimization approach where “readability” criteria are sequentially applied, s... View full abstract»

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  • On self-organizing algorithms and networks for class-separability features

    Publication Year: 1997, Page(s):663 - 678
    Cited by:  Papers (35)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (772 KB)

    We describe self-organizing learning algorithms and associated neural networks to extract features that are effective for preserving class separability. As a first step, an adaptive algorithm for the computation of Q-1/2 (where Q is the correlation or covariance matrix of a random vector sequence) is described. Convergence of this algorithm with probability one is proven by using stocha... View full abstract»

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  • Rotation-invariant neural pattern recognition system estimating a rotation angle

    Publication Year: 1997, Page(s):568 - 581
    Cited by:  Papers (28)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (348 KB)

    A rotation-invariant neural pattern recognition system, which can recognize a rotated pattern and estimate its rotation angle, is considered. It is well-known that humans sometimes recognize a rotated form by means of mental rotation. The occurrence of mental rotation can be explained in terms of the theory of information types. Therefore, we first examine the applicability of the theory to a rota... View full abstract»

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  • Neural networks for convex hull computation

    Publication Year: 1997, Page(s):601 - 611
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (496 KB)

    Computing convex hull is one of the central problems in various applications of computational geometry. In this paper, a convex hull computing neural network (CHCNN) is developed to solve the related problems in the N-dimensional spaces. The algorithm is based on a two-layered neural network, topologically similar to ART, with a newly developed adaptive training strategy called excited learning. T... View full abstract»

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  • Adaptive control using neural networks and approximate models

    Publication Year: 1997, Page(s):475 - 485
    Cited by:  Papers (249)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (416 KB)

    The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural control... View full abstract»

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  • A new evolutionary system for evolving artificial neural networks

    Publication Year: 1997, Page(s):694 - 713
    Cited by:  Papers (403)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (396 KB)

    This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Clos... View full abstract»

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  • A new approach to Kanerva's sparse distributed memory

    Publication Year: 1997, Page(s):791 - 794
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (100 KB)

    The sparse distributed memory (SDM) was originally developed to tackle the problem of storing large binary data patterns. The model succeeded well in storing random input data. However, its efficiency, particularly in handling nonrandom data, was poor. In its original form it is a static and inflexible system. Most of the recent work on the SDM has concentrated on improving the efficiency of a mod... View full abstract»

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  • Neural-network feature selector

    Publication Year: 1997, Page(s):654 - 662
    Cited by:  Papers (153)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (216 KB)

    Feature selection is an integral part of most learning algorithms. Due to the existence of irrelevant and redundant attributes, by selecting only the relevant attributes of the data, higher predictive accuracy can be expected from a machine learning method. In this paper, we propose the use of a three-layer feedforward neural network to select those input attributes that are most useful for discri... View full abstract»

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  • Extended least squares based algorithm for training feedforward networks

    Publication Year: 1997, Page(s):806 - 810
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (148 KB)

    An extended least squares-based algorithm for feedforward networks is proposed. The weights connecting the last hidden and output layers are first evaluated by least squares algorithm. The weights between input and hidden layers are then evaluated using the modified gradient descent algorithms. This arrangement eliminates the stalling problem experienced by the pure least squares type algorithms; ... View full abstract»

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  • Real-time classification of rotating shaft loading conditions using artificial neural networks

    Publication Year: 1997, Page(s):748 - 757
    Cited by:  Papers (34)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (192 KB)

    Vibration analysis can give an indication of the condition of a rotating shaft highlighting potential faults such as unbalance and rubbing. Faults may however only occur intermittently and consequently to detect these requires continuous monitoring with real time analysis. This paper describes the use of artificial neural networks (ANNs) for classification of condition and compares these with othe... View full abstract»

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  • An improved recurrent neural network for M-PAM symbol detection

    Publication Year: 1997, Page(s):779 - 783
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (168 KB)

    In this paper, a fully connected recurrent neural network (RNN) is presented for the recovery of M-ary pulse amplitude modulated (M-PAM) signals in the presence of intersymbol interference and additive white Gaussian noise. The network makes use of two different activation functions. One is the traditional two-level sigmoid function, which is used at its hidden nodes, and the other is the M-level ... View full abstract»

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  • Nonlinear control structures based on embedded neural system models

    Publication Year: 1997, Page(s):553 - 567
    Cited by:  Papers (83)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (448 KB)

    This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The dif... View full abstract»

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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