# IEEE Transactions on Neural Networks

## Filter Results

Displaying Results 1 - 24 of 24

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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• ### On Some Necessary and Sufficient Conditions for a Recurrent Neural Network Model With Time Delays to Generate Oscillations

Publication Year: 2010, Page(s):1197 - 1205
Cited by:  Papers (5)
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In this paper, the existence of oscillations for a class of recurrent neural networks with time delays between neural interconnections is investigated. By using the fixed point theory and Liapunov functional, we prove that a recurrent neural network might have a unique equilibrium point which is unstable. This particular type of instability, combined with the boundedness of the solutions of the sy... View full abstract»

• ### A Fast Algorithm for Robust Mixtures in the Presence of Measurement Errors

Publication Year: 2010, Page(s):1206 - 1220
Cited by:  Papers (8)
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In experimental and observational sciences, detecting atypical, peculiar data from large sets of measurements has the potential of highlighting candidates of interesting new types of objects that deserve more detailed domain-specific followup study. However, measurement data is nearly never free of measurement errors. These errors can generate false outliers that are not truly interesting. Althoug... View full abstract»

• ### Blind Multiuser Detector for Chaos-Based CDMA Using Support Vector Machine

Publication Year: 2010, Page(s):1221 - 1231
Cited by:  Papers (21)
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The algorithm and the results of a blind multiuser detector using a machine learning technique called support vector machine (SVM) on a chaos-based code division multiple access system is presented in this paper. Simulation results showed that the performance achieved by using SVM is comparable to existing minimum mean square error (MMSE) detector under both additive white Gaussian noise (AWGN) an... View full abstract»

• ### On the Selection of Weight Decay Parameter for Faulty Networks

Publication Year: 2010, Page(s):1232 - 1244
Cited by:  Papers (18)
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The weight-decay technique is an effective approach to handle overfitting and weight fault. For fault-free networks, without an appropriate value of decay parameter, the trained network is either overfitted or underfitted. However, many existing results on the selection of decay parameter focus on fault-free networks only. It is well known that the weight-decay method can also suppress the effect ... View full abstract»

• ### Control of Unknown Nonlinear Systems With Efficient Transient Performance Using Concurrent Exploitation and Exploration

Publication Year: 2010, Page(s):1245 - 1261
Cited by:  Papers (13)
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Learning mechanisms that operate in unknown environments should be able to efficiently deal with the problem of controlling unknown dynamical systems. Many approaches that deal with such a problem face the so-called exploitation-exploration dilemma where the controller has to sacrifice efficient performance for the sake of learning “better” control strategies than the ones already kn... View full abstract»

• ### Backpropagation and Ordered Derivatives in the Time Scales Calculus

Publication Year: 2010, Page(s):1262 - 1269
Cited by:  Papers (5)
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Backpropagation is the most widely used neural network learning technique. It is based on the mathematical notion of an ordered derivative. In this paper, we present a formulation of ordered derivatives and the backpropagation training algorithm using the important emerging area of mathematics known as the time scales calculus. This calculus, with its potential for application to a wide variety of... View full abstract»

• ### Approximate Robust Policy Iteration Using Multilayer Perceptron Neural Networks for Discounted Infinite-Horizon Markov Decision Processes With Uncertain Correlated Transition Matrices

Publication Year: 2010, Page(s):1270 - 1280
Cited by:  Papers (12)
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We study finite-state, finite-action, discounted infinite-horizon Markov decision processes with uncertain correlated transition matrices in deterministic policy spaces. Existing robust dynamic programming methods cannot be extended to solving this class of general problems. In this paper, based on a robust optimality criterion, an approximate robust policy iteration using a multilayer perceptron ... View full abstract»

• ### Automatic Induction of Projection Pursuit Indices

Publication Year: 2010, Page(s):1281 - 1295
Cited by:  Papers (11)
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Projection techniques are frequently used as the principal means for the implementation of feature extraction and dimensionality reduction for machine learning applications. A well established and broad class of such projection techniques is the projection pursuit (PP). Its core design parameter is a projection index, which is the driving force in obtaining the transformation function via optimiza... View full abstract»

• ### Fast Support Vector Data Descriptions for Novelty Detection

Publication Year: 2010, Page(s):1296 - 1313
Cited by:  Papers (39)
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Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems. However, the decision function of SVDD is expressed in terms of the kernel expansion, which results in a run-time complexity linear in the number of support vectors. For applications where fast real-time response is needed, how to speed up the decision funct... View full abstract»

• ### Robust Exponential Stability of Markovian Jump Impulsive Stochastic Cohen-Grossberg Neural Networks With Mixed Time Delays

Publication Year: 2010, Page(s):1314 - 1325
Cited by:  Papers (169)
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This paper is concerned with the problem of exponential stability for a class of Markovian jump impulsive stochastic Cohen-Grossberg neural networks with mixed time delays and known or unknown parameters. The jumping parameters are determined by a continuous-time, discrete-state Markov chain, and the mixed time delays under consideration comprise both time-varying delays and continuously distribut... View full abstract»

• ### An Extension of the Standard Mixture Model for Image Segmentation

Publication Year: 2010, Page(s):1326 - 1338
Cited by:  Papers (42)
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Standard Gaussian mixture modeling (GMM) is a well-known method for image segmentation. However, the pixels themselves are considered independent of each other, making the segmentation result sensitive to noise. To reduce the sensitivity of the segmented result with respect to noise, Markov random field (MRF) models provide a powerful way to account for spatial dependences between image pixels. Ho... View full abstract»

• ### Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function

Publication Year: 2010, Page(s):1339 - 1345
Cited by:  Papers (208)
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In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how... View full abstract»

• ### Marginalized Neural Network Mixtures for Large-Scale Regression

Publication Year: 2010, Page(s):1345 - 1351
Cited by:  Papers (4)
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For regression tasks, traditional neural networks (NNs) have been superseded by Gaussian processes, which provide probabilistic predictions (input-dependent error bars), improved accuracy, and virtually no overfitting. Due to their high computational cost, in scenarios with massive data sets, one has to resort to sparse Gaussian processes, which strive to achieve similar performance with much smal... View full abstract»

Publication Year: 2010, Page(s):1351 - 1358
Cited by:  Papers (156)
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A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the following agents can track the leader's t... View full abstract»

• ### Exponential ${\rm H}_{\infty}$ Synchronization of General Discrete-Time Chaotic Neural Networks With or Without Time Delays

Publication Year: 2010, Page(s):1358 - 1365
Cited by:  Papers (42)
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This brief studies exponential H∞ synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H∞ control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization betwe... View full abstract»

• ### Delay-Derivative-Dependent Stability for Delayed Neural Networks With Unbound Distributed Delay

Publication Year: 2010, Page(s):1365 - 1371
Cited by:  Papers (45)
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In this brief, based on Lyapunov-Krasovskii functional approach and appropriate integral inequality, a new sufficient condition is derived to guarantee the global stability for delayed neural networks with unbounded distributed delay, in which the improved delay-partitioning technique and general convex combination are employed. The LMI-based criterion heavily depends on both the upper and lower b... View full abstract»

• ### Multistability of Recurrent Neural Networks With Time-varying Delays and the Piecewise Linear Activation Function

Publication Year: 2010, Page(s):1371 - 1377
Cited by:  Papers (99)
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In this brief, stability of multiple equilibria of recurrent neural networks with time-varying delays and the piecewise linear activation function is studied. A sufficient condition is obtained to ensure that n-neuron recurrent neural networks can have (4k-1)n equilibrium points and (2k)n of them are locally exponentially stable. This condition improves and extends the existing stability results i... View full abstract»

• ### 2011 International Joint Conference on Neural Networks

Publication Year: 2010, Page(s): 1378
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• ### Access over 1 million articles - The IEEE Digital Library [advertisement]

Publication Year: 2010, Page(s): 1379
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Publication Year: 2010, Page(s): 1380
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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