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IEEE Transactions on Neural Networks

Issue 4 • Date July 2006

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  • Table of contents

    Publication Year: 2006, Page(s):c1 - c4
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  • IEEE Transactions on Neural Networks publication information

    Publication Year: 2006, Page(s): c2
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  • A statistical property of multiagent learning based on Markov decision process

    Publication Year: 2006, Page(s):829 - 842
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (664 KB) | HTML iconHTML

    We exhibit an important property called the asymptotic equipartition property (AEP) on empirical sequences in an ergodic multiagent Markov decision process (MDP). Using the AEP which facilitates the analysis of multiagent learning, we give a statistical property of multiagent learning, such as reinforcement learning (RL), near the end of the learning process. We examine the effect of the condition... View full abstract»

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  • Learning lateral interactions for feature binding and sensory segmentation from prototypic basis interactions

    Publication Year: 2006, Page(s):843 - 862
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2232 KB) | HTML iconHTML

    We present a hybrid learning method bridging the fields of recurrent neural networks, unsupervised Hebbian learning, vector quantization, and supervised learning to implement a sophisticated image and feature segmentation architecture. This architecture is based on the competitive layer model (CLM), a dynamic feature binding model, which is applicable on a wide range of perceptual grouping and seg... View full abstract»

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  • Real-time learning capability of neural networks

    Publication Year: 2006, Page(s):863 - 878
    Cited by:  Papers (49)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1440 KB) | HTML iconHTML

    In some practical applications of neural networks, fast response to external events within an extremely short time is highly demanded and expected. However, the extensively used gradient-descent-based learning algorithms obviously cannot satisfy the real-time learning needs in many applications, especially for large-scale applications and/or when higher generalization performance is required. Base... View full abstract»

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  • Universal approximation using incremental constructive feedforward networks with random hidden nodes

    Publication Year: 2006, Page(s):879 - 892
    Cited by:  Papers (303)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (952 KB) | HTML iconHTML

    According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficient,... View full abstract»

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  • A study on SMO-type decomposition methods for support vector machines

    Publication Year: 2006, Page(s):893 - 908
    Cited by:  Papers (69)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (696 KB) | HTML iconHTML

    Decomposition methods are currently one of the major methods for training support vector machines. They vary mainly according to different working set selections. Existing implementations and analysis usually consider some specific selection rules. This paper studies sequential minimal optimization type decomposition methods under a general and flexible way of choosing the two-element working set.... View full abstract»

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  • Cooperative information maximization with Gaussian activation functions for self-organizing maps

    Publication Year: 2006, Page(s):909 - 918
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (600 KB) | HTML iconHTML

    In this paper, we propose a new information-theoretic method to produce explicit self-organizing maps (SOMs). Competition is realized by maximizing mutual information between input patterns and competitive units. Competitive unit outputs are computed by the Gaussian function of distance between input patterns and competitive units. A property of this Gaussian function is that, as distance becomes ... View full abstract»

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  • Motif discoveries in unaligned molecular sequences using self-organizing neural networks

    Publication Year: 2006, Page(s):919 - 928
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (720 KB) | HTML iconHTML

    In this paper, we study the problem of motif discoveries in unaligned DNA and protein sequences. The problem of motif identification in DNA and protein sequences has been studied for many years in the literature. Major hurdles at this point include computational complexity and reliability of the search algorithms. We propose a self-organizing neural network structure for solving the problem of mot... View full abstract»

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  • Fuzzy ARTMAP with input relevances

    Publication Year: 2006, Page(s):929 - 941
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (608 KB) | HTML iconHTML

    We introduce a new fuzzy ARTMAP (FAM) neural network: Fuzzy ARTMAP with relevance factor (FAMR). The FAMR architecture is able to incrementally "grow" and to sequentially accommodate input-output sample pairs. Each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase. The relevance factors are user-defined or computed. The FAMR ... View full abstract»

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  • On global-local artificial neural networks for function approximation

    Publication Year: 2006, Page(s):942 - 952
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (616 KB) | HTML iconHTML

    We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. The algorithm used is intended to identify global features of an input-output relationship before adding local detail to the approximating function. It aims to achieve efficient function approximation through the separate identi... View full abstract»

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  • Stable neurovisual servoing for robot manipulators

    Publication Year: 2006, Page(s):953 - 965
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1424 KB) | HTML iconHTML

    In this paper, we propose a stable neurovisual servoing algorithm for set-point control of planar robot manipulators in a fixed-camera configuration an show that all the closed-loop signals are uniformly ultimately bounded (UUB) and converge exponentially to a small compact set. We assume that the gravity term and Jacobian matrix are unknown. Radial basis function neural networks (RBFNNs) with onl... View full abstract»

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  • An incremental training method for the probabilistic RBF network

    Publication Year: 2006, Page(s):966 - 974
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (792 KB) | HTML iconHTML

    The probabilistic radial basis function (PRBF) network constitutes a probabilistic version of the RBF network for classification that extends the typical mixture model approach to classification by allowing the sharing of mixture components among all classes. The typical learning method of PRBF for a classification task employs the expectation-maximization (EM) algorithm and depends strongly on th... View full abstract»

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  • Nonlinear spatial-temporal prediction based on optimal fusion

    Publication Year: 2006, Page(s):975 - 988
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2376 KB) | HTML iconHTML

    The problem of spatial-temporal signal processing and modeling has been of great interest in recent years. A new spatial-temporal prediction method is presented in this paper. An optimal fusion scheme based on fourth-order statistic is first employed to combine the received signals at different spatial domains. The fused signal is then used to construct a spatial-temporal predictor by a support ve... View full abstract»

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  • A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks

    Publication Year: 2006, Page(s):989 - 1000
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (664 KB) | HTML iconHTML

    In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two... View full abstract»

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  • Polymer property prediction and optimization using neural networks

    Publication Year: 2006, Page(s):1001 - 1014
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (744 KB) | HTML iconHTML

    Prediction and optimization of polymer properties is a complex and highly nonlinear problem with no easy method to predict polymer properties directly and accurately. The problem is especially complicated with high molecular weight polymers such as engineering plastics which have the greatest use in industry. The effect of modifying a monomer (polymer repeat unit) on polymerization and the resulti... View full abstract»

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  • Processing of chemical sensor arrays with a biologically inspired model of olfactory coding

    Publication Year: 2006, Page(s):1015 - 1024
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1312 KB) | HTML iconHTML

    This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neurorece... View full abstract»

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  • Design and implementation of multipattern generators in analog VLSI

    Publication Year: 2006, Page(s):1025 - 1038
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2112 KB) | HTML iconHTML

    In recent years, computational biologists have shown through simulation that small neural networks with fixed connectivity are capable of producing multiple output rhythms in response to transient inputs. It is believed that such networks may play a key role in certain biological behaviors such as dynamic gait control. In this paper, we present a novel method for designing continuous-time recurren... View full abstract»

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  • Parallel sequential minimal optimization for the training of support vector machines

    Publication Year: 2006, Page(s):1039 - 1049
    Cited by:  Papers (33)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (592 KB)

    Sequential minimal optimization (SMO) is one popular algorithm for training support vector machine (SVM), but it still requires a large amount of computation time for solving large size problems. This paper proposes one parallel implementation of SMO for training SVM. The parallel SMO is developed using message passing interface (MPI). Specifically, the parallel SMO first partitions the entire tra... View full abstract»

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  • Real-time computing platform for spiking neurons (RT-spike)

    Publication Year: 2006, Page(s):1050 - 1063
    Cited by:  Papers (30)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1232 KB) | HTML iconHTML

    A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the ... View full abstract»

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  • A fast identification algorithm for box-cox transformation based radial basis function neural network

    Publication Year: 2006, Page(s):1064 - 1069
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB) | HTML iconHTML

    In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algo... View full abstract»

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  • Neural network mechanism for the orientation behavior of sand scorpions towards prey

    Publication Year: 2006, Page(s):1070 - 1076
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (560 KB) | HTML iconHTML

    Sand scorpions use their tactile sense organs on their legs to capture their prey. They are able to localize their prey by processing vibration signals generated by the prey movement. The central nervous system receives stimulus-locked neuron firings of the sense organs on their eight legs. It is believed that eight receptor neurons in the brain interact with each other with triad inhibitions and ... View full abstract»

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  • Delay-dependent state estimation for delayed neural networks

    Publication Year: 2006, Page(s):1077 - 1081
    Cited by:  Papers (34)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (200 KB) | HTML iconHTML

    In this letter, the delay-dependent state estimation problem for neural networks with time-varying delay is investigated. A delay-dependent criterion is established to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally exponentially stable. The proposed method is based on the free-weighting matrix approach and is applicable t... View full abstract»

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  • Face recognition using kernel scatter-difference-based discriminant analysis

    Publication Year: 2006, Page(s):1081 - 1085
    Cited by:  Papers (37)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (344 KB) | HTML iconHTML

    There are two fundamental problems with the Fisher linear discriminant analysis for face recognition. One is the singularity problem of the within-class scatter matrix due to small training sample size. The other is that it cannot efficiently describe complex nonlinear variations of face images because of its linear property. In this letter, a kernel scatter-difference-based discriminant analysis ... View full abstract»

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  • Analog neural network for support vector machine learning

    Publication Year: 2006, Page(s):1085 - 1091
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (350 KB) | HTML iconHTML

    An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems 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