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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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  • Subject index

    Publication Year: 2000, Page(s):1516 - 1529
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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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  • Self-stabilized gradient algorithms for blind source separation with orthogonality constraints

    Publication Year: 2000, Page(s):1490 - 1497
    Cited by:  Papers (38)  |  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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  • Generalization of adaptive neuro-fuzzy inference systems

    Publication Year: 2000, Page(s):1332 - 1346
    Cited by:  Papers (59)
    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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  • Lp approximation of Sigma-Pi neural networks

    Publication Year: 2000, Page(s):1485 - 1489
    Cited by:  Papers (5)
    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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  • 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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  • Variational Gaussian process classifiers

    Publication Year: 2000, Page(s):1458 - 1464
    Cited by:  Papers (50)
    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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  • 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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  • Global stability for cellular neural networks with time delay

    Publication Year: 2000, Page(s):1481 - 1484
    Cited by:  Papers (165)
    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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  • 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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  • 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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  • An iterative inversion approach to blind source separation

    Publication Year: 2000, Page(s):1423 - 1437
    Cited by:  Papers (36)
    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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  • Neural discriminant analysis

    Publication Year: 2000, Page(s):1394 - 1401
    Cited by:  Papers (12)
    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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  • 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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  • Heteroassociations of spatio-temporal sequences with the bidirectional associative memory

    Publication Year: 2000, Page(s):1503 - 1505
    Cited by:  Papers (11)
    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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  • 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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  • 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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  • Anisotropic noise injection for input variables relevance determination

    Publication Year: 2000, Page(s):1201 - 1212
    Cited by:  Papers (7)
    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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  • A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints

    Publication Year: 2000, Page(s):1251 - 1262
    Cited by:  Papers (90)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (404 KB)

    This paper presents a continuous-time recurrent neural-network model for nonlinear optimization with any continuously differentiable objective function and bound constraints. Quadratic optimization with bound constraints is a special problem which can be solved by the recurrent neural network. The proposed recurrent neural network has the following characteristics. 1) It is regular in the sense th... View full abstract»

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  • Elementary function generators for neural-network emulators

    Publication Year: 2000, Page(s):1438 - 1449
    Cited by:  Papers (15)
    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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  • Learning parametric specular reflectance model by radial basis function network

    Publication Year: 2000, Page(s):1498 - 1503
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (364 KB)

    For the shape from shading problem, it is known that most real images usually contain specular components and are affected by unknown reflectivity. In the paper, these limitations are addressed and a neural-based specular reflectance model is proposed. The idea of this method is to optimize a proper specular model by learning the parameters of a radial basis function network and to recover the obj... 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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  • Stable neural controller design for unknown nonlinear systems using backstepping

    Publication Year: 2000, Page(s):1347 - 1360
    Cited by:  Papers (98)
    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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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