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# IEEE Transactions on Neural Networks

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Displaying Results 1 - 20 of 20

Publication Year: 2010, Page(s): C1
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• ### IEEE Transactions on Neural Networks publication information

Publication Year: 2010, Page(s): C2
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• ### A Neural Network of Smooth Hinge Functions

Publication Year: 2010, Page(s):1381 - 1395
Cited by:  Papers (7)
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Smooth hinging hyperplane (SHH) has been proposed as an improvement over the well-known hinging hyperplane (HH) by the fact that it retains the useful features of HH while overcoming HH's drawback of nondifferentiability. This paper introduces a formal characterization of smooth hinge function (SHF), which can be used to generate SHH as a neural network. A method for the general construction of SH... View full abstract»

• ### Exponential Stability Analysis for Delayed Neural Networks With Switching Parameters: Average Dwell Time Approach

Publication Year: 2010, Page(s):1396 - 1407
Cited by:  Papers (119)
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This paper is concerned with the problem of exponential stability analysis of continuous-time switched delayed neural networks. By using the average dwell time approach together with the piecewise Lyapunov function technique and by combining a novel Lyapunov-Krasovskii functional, which benefits from the delay partitioning method, with the free-weighting matrix technique, sufficient conditions are... View full abstract»

• ### Identification of Finite State Automata With a Class of Recurrent Neural Networks

Publication Year: 2010, Page(s):1408 - 1421
Cited by:  Papers (10)
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A class of recurrent neural networks is proposed and proven to be capable of identifying any discrete-time dynamical system. The application of the proposed network is addressed in the encoding, identification, and extraction of finite state automata (FSAs). Simulation results show that the identification of FSAs using the proposed network, trained by the hybrid greedy simulated annealing with a m... View full abstract»

• ### Novel Hysteretic Noisy Chaotic Neural Network for Broadcast Scheduling Problems in Packet Radio Networks

Publication Year: 2010, Page(s):1422 - 1433
Cited by:  Papers (18)
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Noisy chaotic neural network (NCNN), which can exhibit stochastic chaotic simulated annealing (SCSA), has been proven to be a powerful tool in solving combinatorial optimization problems. In order to retain the excellent optimization property of SCSA and improve the optimization performance of the NCNN using hysteretic dynamics without increasing network parameters, we first construct an equivalen... View full abstract»

• ### Equivalences Between Neural-Autoregressive Time Series Models and Fuzzy Systems

Publication Year: 2010, Page(s):1434 - 1444
Cited by:  Papers (7)
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Soft computing (SC) emerged as an integrating framework for a number of techniques that could complement one another quite well (artificial neural networks, fuzzy systems, evolutionary algorithms, probabilistic reasoning). Since its inception, a distinctive goal has been to dig out the deep relationships among their components. This paper considers two wide families of SC models. On the one hand, ... View full abstract»

• ### Similarity Preserving Principal Curve: An Optimal 1-D Feature Extractor for Data Representation

Publication Year: 2010, Page(s):1445 - 1456
Cited by:  Papers (3)
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This paper discusses the problem of what kind of learning model is suitable for the tasks of feature extraction for data representation and suggests two evaluation criteria for nonlinear feature extractors: reconstruction error minimization and similarity preservation. Based on the suggested evaluation criteria, a new type of principal curve-similarity preserving principal curve (SPPC) is proposed... View full abstract»

• ### Quaternion-Based Adaptive Output Feedback Attitude Control of Spacecraft Using Chebyshev Neural Networks

Publication Year: 2010, Page(s):1457 - 1471
Cited by:  Papers (47)
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This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to descr... View full abstract»

• ### Blind Extraction of Global Signal From Multi-Channel Noisy Observations

Publication Year: 2010, Page(s):1472 - 1481
Cited by:  Papers (7)
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We propose a novel efficient method of blind signal extraction from multi-sensor networks when each observed signal consists of one global signal and local uncorrelated signals. Most of existing blind signal separation and extraction methods such as independent component analysis have constraints such as statistical independence, non-Gaussianity, and underdetermination, and they are not suitable f... View full abstract»

• ### Self-Organizing Potential Field Network: A New Optimization Algorithm

Publication Year: 2010, Page(s):1482 - 1495
Cited by:  Papers (8)
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This paper presents a novel optimization algorithm called self-organizing potential field network (SOPFN). The SOPFN algorithm is derived from the idea of the vector potential field. In the proposed network, the neuron with the best weight is considered as the target with the attractive force, while the neuron with the worst weight is considered as the obstacle with the repulsive force. The compet... View full abstract»

• ### Analysis and Design of a $k$ -Winners-Take-All Model With a Single State Variable and the Heaviside Step Activation Function

Publication Year: 2010, Page(s):1496 - 1506
Cited by:  Papers (34)
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This paper presents a k-winners-take-all (kWTA) neural network with a single state variable and a hard-limiting activation function. First, following several kWTA problem formulations, related existing kWTA networks are reviewed. Then, the kWTA model model with a single state variable and a Heaviside step activation function is described and its global stability ... View full abstract»

• ### A $Q$-Modification Neuroadaptive Control Architecture for Discrete-Time Systems

Publication Year: 2010, Page(s):1507 - 1511
Cited by:  Papers (1)
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This brief extends the new neuroadaptive control framework for continuous-time nonlinear uncertain dynamical systems based on a Q -modification architecture to discrete-time systems. As in the continuous-time case, the discrete-time update laws involve auxiliary terms, or Q-modification terms, predicated on an estimate of the unknown neural network weights which in turn involve a set... View full abstract»

• ### Real-Time Simulation of Biologically Realistic Stochastic Neurons in VLSI

Publication Year: 2010, Page(s):1511 - 1517
Cited by:  Papers (23)  |  Patents (3)
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Neuronal variability has been thought to play an important role in the brain. As the variability mainly comes from the uncertainty in biophysical mechanisms, stochastic neuron models have been proposed for studying how neurons compute with noise. However, most papers are limited to simulating stochastic neurons in a digital computer. The speed and the efficiency are thus limited especially when a ... View full abstract»

• ### Constructive Approximation to Multivariate Function by Decay RBF Neural Network

Publication Year: 2010, Page(s):1517 - 1523
Cited by:  Papers (23)
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It is well known that single hidden layer feedforward networks with radial basis function (RBF) kernels are universal approximators when all the parameters of the networks are obtained through all kinds of algorithms. However, as observed in most neural network implementations, tuning all the parameters of the network may cause learning complicated, poor generalization, overtraining and unstable. ... View full abstract»

• ### Scalable Large-Margin Mahalanobis Distance Metric Learning

Publication Year: 2010, Page(s):1524 - 1530
Cited by:  Papers (33)
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For many machine learning algorithms such as k-nearest neighbor ( k-NN) classifiers and k-means clustering, often their success heavily depends on the metric used to calculate distances between different data points. An effective solution for defining such a metric is to learn it from a set of labeled training samples. In this work, we propose a fast and scalable algorithm to ... View full abstract»

• ### Special issue on data-based optimization, control and modeling

Publication Year: 2010, Page(s): 1531
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Publication Year: 2010, Page(s): 1532
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• ### IEEE Computational Intelligence Society Information

Publication Year: 2010, Page(s): C3
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• ### IEEE Transactions on Neural Networks Information for authors

Publication Year: 2010, Page(s): C4
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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