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IEEE Transactions on Neural Networks

Issue 6 • Date Nov. 2000

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Displaying Results 1 - 25 of 34
  • Book reviews

    Publication Year: 2000, Page(s):1508 - 1511
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    Freely Available from IEEE
  • Author index

    Publication Year: 2000, Page(s):1512 - 1516
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  • Subject index

    Publication Year: 2000, Page(s):1516 - 1529
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  • User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks

    Publication Year: 2000, Page(s):1373 - 1384
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (276 KB)

    Based on self-growing probabilistic decision-based neural networks (SPDNNs), user adaptation of the parameters of SPDNN is formulated as incremental reinforced and anti-reinforced learning procedures, which are easily integrated into the batched training procedures of the SPDNN. In this study, we developed: 1) an SPDNN based handwriting recognition system; 2) a two-stage recognition structure; and... View full abstract»

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  • A hybrid linear-neural model for time series forecasting

    Publication Year: 2000, Page(s):1402 - 1412
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (268 KB)

    This paper considers a linear model with time varying parameters controlled by a neural network to analyze and forecast nonlinear time series. We show that this formulation, called neural coefficient smooth transition autoregressive model, is in close relation to the threshold autoregressive model and the smooth transition autoregressive model with the advantage of naturally incorporating linear m... View full abstract»

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  • A comment on "On equilibria, stability, and instability of Hopfield neural networks" [and reply]

    Publication Year: 2000, Page(s):1506 - 1507
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (76 KB)

    It is pointed out that the main analysis results about the existence, uniqueness, and global asymptotic stability of the equilibrium of a continuous-time Hopfield type neural network given in the paper by Zhi-Hong Guan et al. (2000) are special cases of relevant ones previously obtained in the literature. In reply the original authors consider the reasoning of Xue-Bin Liang's comments and state th... View full abstract»

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  • On-line learning of dynamical systems in the presence of model mismatch and disturbances

    Publication Year: 2000, Page(s):1272 - 1283
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (304 KB)

    This paper is concerned with the online learning of unknown dynamical systems using a recurrent neural network. The unknown dynamic systems to be learned are subject to disturbances and possibly unstable. The neural-network model used has a simple architecture with one layer of adaptive connection weights. Four learning rules are proposed for the cases where the system state is measurable in conti... View full abstract»

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  • Fast combinatorial optimization with parallel digital computers

    Publication Year: 2000, Page(s):1323 - 1331
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (192 KB)

    This paper presents an algorithm which realizes fast search for the solutions of combinatorial optimization problems with parallel digital computers. With the standard weight matrices designed for combinatorial optimization, many iterations are required before convergence to a quasioptimal solution even when many digital processors can be used in parallel. By removing the components of the eigenve... View full abstract»

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  • Approximating the maximum weight clique using replicator dynamics

    Publication Year: 2000, Page(s):1228 - 1241
    Cited by:  Papers (20)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB)

    Given an undirected graph with weights on the vertices, the maximum weight clique problem (MWCP) is to find a subset of mutually adjacent vertices (a clique) having the largest total weight. This is a generalization of the problem of finding the maximum cardinality clique of an unweighted graph, which is the special case of the MWCP when all vertex weights are equal. The problem is NP-hard for arb... View full abstract»

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  • Global stability for cellular neural networks with time delay

    Publication Year: 2000, Page(s):1481 - 1484
    Cited by:  Papers (121)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (92 KB)

    A sufficient condition related to the existence of a unique equilibrium point and its global asymptotic stability for cellular network networks with delay (DCNNs) is derived. It is shown that the condition relies on the feedback matrices and is independent of the delay parameter. Furthermore, this condition is less restrictive than that given in the literature. View full abstract»

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  • Lp approximation of Sigma-Pi neural networks

    Publication Year: 2000, Page(s):1485 - 1489
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (184 KB)

    A feedforward Sigma-Pi neural network with a single hidden layer of m neurons is given by mΣj=1cjg(nΠk=1xkkjkj) where cj, θkj, λk∈R. We investigate the approximation of arbitrary functions f: Rn→R ... View full abstract»

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  • Variational Gaussian process classifiers

    Publication Year: 2000, Page(s):1458 - 1464
    Cited by:  Papers (32)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (180 KB)

    Gaussian processes are a promising nonlinear regression tool, but it is not straightforward to solve classification problems with them. In the paper the variational methods of Jaakkola and Jordan (2000) are applied to Gaussian processes to produce an efficient Bayesian binary classifier. View full abstract»

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  • Self-stabilized gradient algorithms for blind source separation with orthogonality constraints

    Publication Year: 2000, Page(s):1490 - 1497
    Cited by:  Papers (33)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (188 KB)

    Developments in self-stabilized algorithms for gradient adaptation of orthonormal matrices have resulted in simple but powerful principal and minor subspace analysis methods. We extend these ideas to develop algorithms for instantaneous prewhitened blind separation of homogeneous signal mixtures. Our algorithms are proven to be self-stabilizing to the Stiefel manifold of orthonormal matrices, such... View full abstract»

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  • Convergent on-line algorithms for supervised learning in neural networks

    Publication Year: 2000, Page(s):1284 - 1299
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (372 KB)

    We define online algorithms for neural network training, based on the construction of multiple copies of the network, which are trained by employing different data blocks. It is shown that suitable training algorithms can be defined, in a way that the disagreement between the different copies of the network is asymptotically reduced, and convergence toward stationary points of the global error fun... View full abstract»

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  • Building cost functions minimizing to some summary statistics

    Publication Year: 2000, Page(s):1263 - 1271
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (180 KB)

    A learning machine-or a model-is usually trained by minimizing a given criterion (the expectation of the cost function), measuring the discrepancy between the model output and the desired output. As is already well known, the choice of the cost function has a profound impact on the probabilistic interpretation of the output of the model, after training. In this work, we use the calculus of variati... View full abstract»

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  • Morphology and autowave metric on CNN applied to bubble-debris classification

    Publication Year: 2000, Page(s):1385 - 1393
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (376 KB)

    We present the initial results of cellular neural network (CNN)-based autowave metric to high-speed pattern recognition of gray-scale images. The approach is applied to a problem involving separation of metallic wear debris particles from air bubbles. This problem arises in an optical-based system for determination of mechanical wear. This paper focuses on distinguishing debris particles suspended... View full abstract»

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  • Stable neural controller design for unknown nonlinear systems using backstepping

    Publication Year: 2000, Page(s):1347 - 1360
    Cited by:  Papers (73)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (320 KB)

    We propose, from an adaptive control perspective, a neural controller for a class of unknown, minimum phase, feedback linearizable nonlinear system with known relative degree. The control scheme is based on the backstepping design technique in conjunction with a linearly parametrized neural-network structure. The resulting controller, however, moves the complex mechanics involved in a typical back... View full abstract»

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  • Neural discriminant analysis

    Publication Year: 2000, Page(s):1394 - 1401
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (160 KB)

    The role of bootstrap is highlighted for nonlinear discriminant analysis using a feedforward neural network model. Statistical techniques are formulated in terms of the principle of the likelihood of a neural-network model when the data consist of ungrouped binary responses and a set of predictor variables. We illustrate that the information criterion based on the bootstrap method is shown to be f... View full abstract»

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  • Asynchronous self-organizing maps

    Publication Year: 2000, Page(s):1315 - 1322
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (196 KB)

    A recently defined energy function which leads to a self-organizing map is used as a foundation for an asynchronous neural-network algorithm. We generalize the existing stochastic gradient approach to an asynchronous parallel stochastic gradient method for generating a topological map on a distributed computer system (MIMD). A convergence proof is presented and simulation results on a set of probl... View full abstract»

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  • Local PCA algorithms

    Publication Year: 2000, Page(s):1242 - 1250
    Cited by:  Papers (26)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (224 KB)

    Within the last years various principal component analysis (PCA) algorithms have been proposed. In this paper we use a general framework to describe those PCA algorithms which are based on Hebbian learning. For an important subset of these algorithms, the local algorithms, we fully describe their equilibria, where all lateral connections are set to zero and their local stability. We show how the p... View full abstract»

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  • Anisotropic noise injection for input variables relevance determination

    Publication Year: 2000, Page(s):1201 - 1212
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (256 KB)

    There are two archetypal ways to control the complexity of a flexible regressor: subset selection and ridge regression. In neural-networks jargon, they are, respectively, known as pruning and weight decay. These techniques may also be adapted to estimate which features of the input space are relevant for predicting the output variables. Relevance is given by a binary indicator for subset selection... View full abstract»

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  • Blind extraction of singularly mixed source signals

    Publication Year: 2000, Page(s):1413 - 1422
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (240 KB)

    This paper introduces a novel technique for sequential blind extraction of singularly mixed sources. First, a neural-network model and an adaptive algorithm for single-source blind extraction are introduced. Next, an extractability analysis is presented for singular mixing matrix, and two sets of necessary and sufficient extractability conditions are derived. The adaptive algorithm and neural-netw... View full abstract»

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  • Decentralized sliding mode adaptive controller design based on fuzzy neural networks for interconnected uncertain nonlinear systems

    Publication Year: 2000, Page(s):1471 - 1480
    Cited by:  Papers (42)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (224 KB)

    A new type controller, fuzzy neural networks sliding mode controller (FNNSMC), is developed for a class of large-scale systems with unknown bounds of high-order interconnections and disturbances. Although sliding mode control is simple and insensitive to uncertainties and disturbances, there are two main problems in the sliding mode controller (SMC): control input chattering and the assumption of ... View full abstract»

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  • Reinforcement and backpropagation training for an optical neural network using self-lensing effects

    Publication Year: 2000, Page(s):1450 - 1457
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (228 KB)

    The optical bench training of an optical feedforward neural network, developed by the authors, is presented. The network uses an optical nonlinear material for neuron processing and a trainable applied optical pattern as the network weights. The nonlinear material, with the applied weight pattern, modulates the phase front of a forward propagating information beam by dynamically altering the index... View full abstract»

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  • Heteroassociations of spatio-temporal sequences with the bidirectional associative memory

    Publication Year: 2000, Page(s):1503 - 1505
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (92 KB)

    Autoassociations of spatio-temporal sequences have been discussed by a number of authors. We propose a mechanism for storing and retrieving pairs of spatio-temporal sequences with the network architecture of the standard bidirectional associative memory (BAM), thereby achieving heteroassociations of spatio-temporal sequences. 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