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IEEE Transactions on Neural Networks

Issue 6 • 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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    Freely Available from IEEE
  • Subject index

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

    Publication Year: 2000, Page(s):1373 - 1384
    Cited by:  Papers (10)
    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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  • Variational Gaussian process classifiers

    Publication Year: 2000, Page(s):1458 - 1464
    Cited by:  Papers (51)
    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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  • Reinforcement and backpropagation training for an optical neural network using self-lensing effects

    Publication Year: 2000, Page(s):1450 - 1457
    Cited by:  Papers (3)
    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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  • 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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  • Elementary function generators for neural-network emulators

    Publication Year: 2000, Page(s):1438 - 1449
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (356 KB)

    Piecewise first- and second-order approximations are employed to design commonly used elementary function generators for neural-network emulators. Three novel schemes are proposed for the first-order approximations. The first scheme requires one multiplication, one addition, and a 28-byte lookup table. The second scheme requires one addition, a 14-byte lookup table, and no multiplication. The thir... 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 (30)
    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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  • Blind extraction of singularly mixed source signals

    Publication Year: 2000, Page(s):1413 - 1422
    Cited by:  Papers (24)
    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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  • 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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  • State-based SHOSLIF for indoor visual navigation

    Publication Year: 2000, Page(s):1300 - 1314
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (412 KB)

    In this paper, we investigate vision-based navigation using the self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) that incorporates states and a visual attention mechanism. With states to keep the history information and regarding the incoming video input as an observation vector, the vision-based navigation is formulated as an observation-driven Markov model... View full abstract»

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  • Voronoi networks and their probability of misclassification

    Publication Year: 2000, Page(s):1361 - 1372
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (300 KB)

    To reduce the memory requirements and the computation cost, many algorithms have been developed that perform nearest neighbor classification using only a small number of representative samples obtained from the training set. We call the classification model underlying all these algorithms as Voronoi networks (Vnets). We analyze the generalization capabilities of these networks by bounding the gene... View full abstract»

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  • Asynchronous self-organizing maps

    Publication Year: 2000, Page(s):1315 - 1322
    Cited by:  Papers (4)
    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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  • On-line learning of dynamical systems in the presence of model mismatch and disturbances

    Publication Year: 2000, Page(s):1272 - 1283
    Cited by:  Papers (7)
    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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  • Generalization of adaptive neuro-fuzzy inference systems

    Publication Year: 2000, Page(s):1332 - 1346
    Cited by:  Papers (60)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (376 KB)

    The adaptive network-based fuzzy inference systems (ANFIS) of Jang (1993) is extended to the generalized ANFIS (GANFIS) by proposing a generalized fuzzy model (GFM) and considering a generalized radial basis function (GRBF) network. The GFM encompasses both the Takagi-Sugeno (TS)-model and the compositional rule of inference (CRI) model. The conditions by which the proposed GFM converts to TS-mode... 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 (10)
    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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  • Building cost functions minimizing to some summary statistics

    Publication Year: 2000, Page(s):1263 - 1271
    Cited by:  Papers (16)
    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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  • Approximating the maximum weight clique using replicator dynamics

    Publication Year: 2000, Page(s):1228 - 1241
    Cited by:  Papers (36)
    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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  • A comment on "On equilibria, stability, and instability of Hopfield neural networks" [and reply]

    Publication Year: 2000, Page(s):1506 - 1507
    Cited by:  Papers (12)
    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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  • A robust neural controller for underwater robot manipulators

    Publication Year: 2000, Page(s):1465 - 1470
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (156 KB)

    Presents a robust control scheme using a multilayer neural network with the error backpropagation learning algorithm. The multilayer neural network acts as a compensator of the conventional sliding mode controller to improve the control performance when initial assumptions of uncertainty bounds of system parameters are not valid. The proposed controller is applied to control a robot manipulator op... View full abstract»

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  • An iterative inversion approach to blind source separation

    Publication Year: 2000, Page(s):1423 - 1437
    Cited by:  Papers (37)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (328 KB)

    We present an iterative inversion (II) approach to blind source separation (BSS). It consists of a quasi-Newton method for the resolution of an estimating equation obtained from the implicit inversion of a robust estimate of the mixing system. The resulting learning rule includes several existing algorithms for BSS as particular cases giving them a novel and unified interpretation. It also provide... 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 (22)
    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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  • Synthesis of feedforward networks in supremum error bound

    Publication Year: 2000, Page(s):1213 - 1227
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (500 KB)

    The main result of this paper is a constructive proof of a formula for the upper bound of the approximation error in L∞ (supremum norm) of multidimensional functions by feedforward networks with one hidden layer of sigmoidal units and a linear output. This result is applied to formulate a new method of neural-network synthesis. The result can also be used to estimate complexity of... 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 (58)
    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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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