IEEE Transactions on Neural Networks

Volume 8 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 (3)
    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
  • Real-time classification of rotating shaft loading conditions using artificial neural networks

    Publication Year: 1997, Page(s):748 - 757
    Cited by:  Papers (37)  |  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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  • Temporal and spatial stability in translation invariant linear resistive networks

    Publication Year: 1997, Page(s):736 - 747
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (732 KB)

    Simple algebraic methods are proposed to evaluate the temporal and spatial stability of translation invariant linear resistive networks. Temporal stability is discussed for a finite number of nodes n. The proposed method evaluates stability of a Toeplitz pencil An(a)+μBn(b) in terms of parameters ai and bi. In many cases a simple method allows one to... View full abstract»

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  • Improving the error backpropagation algorithm with a modified error function

    Publication Year: 1997, Page(s):799 - 803
    Cited by:  Papers (51)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (188 KB)

    This letter proposes a modified error function to improve the error backpropagation (EBP) algorithm of multilayer perceptrons (MLPs) which suffers from slow learning speed. To accelerate the learning speed of the EBP algorithm, the proposed method reduces the probability that output nodes are near the wrong extreme value of sigmoid activation function. This is acquired through a strong error signa... View full abstract»

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  • Supervised neural networks for the classification of structures

    Publication Year: 1997, Page(s):714 - 735
    Cited by:  Papers (139)  |  Patents (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (660 KB)

    Standard neural networks and statistical methods are usually believed to be inadequate when dealing with complex structures because of their feature-based approach. In fact, feature-based approaches usually fail to give satisfactory solutions because of the sensitivity of the approach to the a priori selection of the features, and the incapacity to represent any specific information on the relatio... 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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  • Neural-network feature selector

    Publication Year: 1997, Page(s):654 - 662
    Cited by:  Papers (158)
    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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  • Constructive algorithms for structure learning in feedforward neural networks for regression problems

    Publication Year: 1997, Page(s):630 - 645
    Cited by:  Papers (225)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (240 KB)

    In this survey paper, we review the constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. By formulating the whole problem as a state-space search, we first describe the general issues in constructive algorithms, w... 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 (208)
    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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  • A new evolutionary system for evolving artificial neural networks

    Publication Year: 1997, Page(s):694 - 713
    Cited by:  Papers (419)  |  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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  • 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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  • A new approach to Kanerva's sparse distributed memory

    Publication Year: 1997, Page(s):791 - 794
    Cited by:  Papers (10)
    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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  • 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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  • SOIM: a self-organizing invertible map with applications in active vision

    Publication Year: 1997, Page(s):758 - 773
    Cited by:  Papers (7)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (376 KB)

    We propose a novel neural network, called the self-organized invertible map (SOIM), that is capable of learning many-to-one functionals mappings in a self-organized and online fashion. The design and performance of the SOIM are highlighted by learning a many-to-one functional mapping that exists in active vision for spatial representation of three-dimensional point targets. The learned spatial rep... View full abstract»

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  • Neural network for solving extended linear programming problems

    Publication Year: 1997, Page(s):803 - 806
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (160 KB)

    A neural network for solving extended linear programming problems is presented and is shown to be globally convergent to exact solutions. The proposed neural network only uses simple hardware in which no analog multiplier for variables is required, and has no parameter tuning problem. Finally, an application of the neural network to the L1 -norm minimization problem is given View full abstract»

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  • Acquiring rule sets as a product of learning in a logical neural architecture

    Publication Year: 1997, Page(s):461 - 474
    Cited by:  Papers (32)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (312 KB)

    Envisioning neural networks as systems that learn rules calls forth the verification issues already being studied in knowledge-based systems engineering, and complicates these with neural-network concepts such as nonlinear dynamics and distributed memories. We show that the issues can be clarified and the learned rules visualized symbolically by formalizing the semantics of rule-learning in the ma... 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 (36)  |  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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  • Fast parallel off-line training of multilayer perceptrons

    Publication Year: 1997, Page(s):646 - 653
    Cited by:  Papers (31)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (184 KB)

    Various approaches to the parallel implementation of second-order gradient-based multilayer perceptron training algorithms are described. Two main classes of algorithm are defined involving Hessian and conjugate gradient-based methods. The limited- and full-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms are selected as representative examples and used to show that the step size and grad... 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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  • Nonlinear control structures based on embedded neural system models

    Publication Year: 1997, Page(s):553 - 567
    Cited by:  Papers (85)
    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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  • 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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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