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IEEE Transactions on Neural Networks

Issue 1 • Date Mar 1990

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Displaying Results 1 - 13 of 13
  • Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification

    Publication Year: 1990, Page(s):111 - 121
    Cited by:  Papers (130)  |  Patents (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (984 KB)

    Two methods for classification based on the Bayes strategy and nonparametric estimators for probability density functions are reviewed. The two methods are named the probabilistic neural network (PNN) and the polynomial Adaline. Both methods involve one-pass learning algorithms that can be implemented directly in parallel neural network architectures. The performances of the two methods are compar... View full abstract»

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  • Self-organizing network for optimum supervised learning

    Publication Year: 1990, Page(s):100 - 110
    Cited by:  Papers (67)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1040 KB)

    A new algorithm called the self-organizing neural network (SONN) is introduced. Its use is demonstrated in a system identification task. The algorithm constructs a network, chooses the node functions, and adjusts the weights. It is compared to the backpropagation algorithm in the identification of the chaotic time series. The results show that SONN constructs a simpler, more accurate model, requir... View full abstract»

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  • Variants of self-organizing maps

    Publication Year: 1990, Page(s):93 - 99
    Cited by:  Papers (85)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (664 KB)

    Self-organizing maps have a bearing on traditional vector quantization. A characteristic that makes them more closely resemble certain biological brain maps, however, is the spatial order of their responses, which is formed in the learning process. A discussion is presented of the basic algorithms and two innovations: dynamic weighting of the input signals at each input of each cell, which improve... View full abstract»

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  • Model-free distributed learning

    Publication Year: 1990, Page(s):58 - 70
    Cited by:  Papers (33)  |  Patents (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1116 KB)

    Model-free learning for synchronous and asynchronous quasi-static networks is presented. The network weights are continuously perturbed, while the time-varying performance index is measured and correlated with the perturbation signals; the correlation output determines the changes in the weights. The perturbation may be either via noise sources or orthogonal signals. The invariance to detailed net... View full abstract»

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  • Identification and control of dynamical systems using neural networks

    Publication Year: 1990, Page(s):4 - 27
    Cited by:  Papers (2698)  |  Patents (42)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1792 KB)

    It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, an... View full abstract»

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  • Two coding strategies for bidirectional associative memory

    Publication Year: 1990, Page(s):81 - 92
    Cited by:  Papers (120)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (772 KB)

    Enhancements of the encoding strategy of a discrete bidirectional associative memory (BAM) reported by B. Kosko (1987) are presented. There are two major concepts in this work: multiple training, which can be guaranteed to achieve recall of a single trained pair under suitable initial conditions of data, and dummy augmentation, which can be guaranteed to achieve recall of all trained pairs if atta... View full abstract»

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  • Unsupervised learning in noise

    Publication Year: 1990, Page(s):44 - 57
    Cited by:  Papers (45)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1340 KB)

    A new hybrid learning law, the differential competitive law, which uses the neuronal signal velocity as a local unsupervised reinforcement mechanism, is introduced, and its coding and stability behavior in feedforward and feedback networks is examined. This analysis is facilitated by the recent Gluck-Parker pulse-coding interpretation of signal functions in differential Hebbian learning systems. T... View full abstract»

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  • Neural controller for adaptive movements with unforeseen payloads

    Publication Year: 1990, Page(s):137 - 142
    Cited by:  Papers (27)  |  Patents (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (528 KB)

    A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits.... View full abstract»

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  • ATM communications network control by neural networks

    Publication Year: 1990, Page(s):122 - 130
    Cited by:  Papers (96)  |  Patents (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (900 KB)

    A learning method that uses neural networks for service quality control in the asynchronous transfer mode (ATM) communications network is described. Because the precise characteristics of the source traffic are not known and the service quality requirements change over time, building an efficient network controller which can control the network traffic is a difficult task. The proposed ATM network... View full abstract»

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  • Sensitivity of feedforward neural networks to weight errors

    Publication Year: 1990, Page(s):71 - 80
    Cited by:  Papers (137)  |  Patents (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (928 KB)

    An analysis is made of the sensitivity of feedforward layered networks of Adaline elements (threshold logic units) to weight errors. An approximation is derived which expresses the probability of error for an output neuron of a large network (a network with many neurons per layer) as a function of the percentage change in the weights. As would be expected, the probability of error increases with t... View full abstract»

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  • Survey of neural network technology for automatic target recognition

    Publication Year: 1990, Page(s):28 - 43
    Cited by:  Papers (89)  |  Patents (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1744 KB)

    A review is presented of ATR (automatic target recognition), and some of the highlights of neural network technology developments that have the potential for making a significant impact on ATR are presented. In particular, neural network technology developments in the areas of collective computation, learning algorithms, expert systems, and neurocomputer hardware could provide crucial tools for de... View full abstract»

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  • A parallel algorithm for tiling problems

    Publication Year: 1990, Page(s):143 - 145
    Cited by:  Papers (66)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (456 KB)

    A parallel algorithm for tiling with polyominoes is presented. The tiling problem is to pack polyominoes in a finite checkerboard. The algorithm using l×m×n processing elements requires O(1) time, where l is the number of different kinds of polyominoes on an m×n checkerboard. The algorithm can be used for placement of... View full abstract»

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  • Three-dimensional neural net for learning visuomotor coordination of a robot arm

    Publication Year: 1990, Page(s):131 - 136
    Cited by:  Papers (153)  |  Patents (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (648 KB)

    An extension of T. Kohonen's (1982) self-organizing mapping algorithm together with an error-correction scheme based on the Widrow-Hoff learning rule is applied to develop a learning algorithm for the visuomotor coordination of a simulated robot arm. Learning occurs by a sequence of trial movements without the need for an external teacher. Using input signals from a pair of cameras, the closed rob... 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