# IEEE Transactions on Neural Networks

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Displaying Results 1 - 17 of 17

Publication Year: 2009, Page(s): C1
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• ### IEEE Transactions on Neural Networks publication information

Publication Year: 2009, Page(s): C2
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• ### SoftDoubleMaxMinOver: Perceptron-Like Training of Support Vector Machines

Publication Year: 2009, Page(s):1061 - 1072
Cited by:  Papers (9)
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The well-known MinOver algorithm is a slight modification of the perceptron algorithm and provides the maximum-margin classifier without a bias in linearly separable two-class classification problems. DoubleMinOver as an extension of MinOver, which now includes a bias, is introduced. An O(t-1) convergence is shown, where t is the number of learning steps. The computational... View full abstract»

• ### Recurrent-Neural-Network-Based Boolean Factor Analysis and Its Application to Word Clustering

Publication Year: 2009, Page(s):1073 - 1086
Cited by:  Papers (11)
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The objective of this paper is to introduce a neural-network-based algorithm for word clustering as an extension of the neural-network-based Boolean factor analysis algorithm (Frolov , 2007). It is shown that this extended algorithm supports even the more complex model of signals that are supposed to be related to textual documents. It is hypothesized that every topic in textual data is characteri... View full abstract»

• ### A Hybrid Pareto Mixture for Conditional Asymmetric Fat-Tailed Distributions

Publication Year: 2009, Page(s):1087 - 1101
Cited by:  Papers (5)
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In many cases, we observe some variables X that contain predictive information over a scalar variable of interest Y, with (X, Y) pairs observed in a training set. We can take advantage of this information to estimate the conditional density p(Y|X=x). In this paper, we propose a conditional mixture model with hybrid Pareto components to estimate p(Y|X=x). The hybrid Pareto is a Gaussian whos... View full abstract»

• ### Stability and Synchronization of Discrete-Time Markovian Jumping Neural Networks With Mixed Mode-Dependent Time Delays

Publication Year: 2009, Page(s):1102 - 1116
Cited by:  Papers (191)
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In this paper, we introduce a new class of discrete-time neural networks (DNNs) with Markovian jumping parameters as well as mode-dependent mixed time delays (both discrete and distributed time delays). Specifically, the parameters of the DNNs are subject to the switching from one to another at different times according to a Markov chain, and the mixed time delays consist of both discrete and dist... View full abstract»

• ### A Granular Reflex Fuzzy Min–Max Neural Network for Classification

Publication Year: 2009, Page(s):1117 - 1134
Cited by:  Papers (18)
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Granular data classification and clustering is an upcoming and important issue in the field of pattern recognition. Conventionally, computing is thought to be manipulation of numbers or symbols. However, human recognition capabilities are based on ability to process nonnumeric clumps of information (information granules) in addition to individual numeric values. This paper proposes a granular neur... View full abstract»

• ### Learning of Spatio–Temporal Codes in a Coupled Oscillator System

Publication Year: 2009, Page(s):1135 - 1147
Cited by:  Papers (6)
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In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomog... View full abstract»

• ### Adaptive Neural Control for a Class of Nonlinear Systems With Uncertain Hysteresis Inputs and Time-Varying State Delays

Publication Year: 2009, Page(s):1148 - 1164
Cited by:  Papers (43)
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In this paper, adaptive variable structure neural control is investigated for a class of nonlinear systems under the effects of time-varying state delays and uncertain hysteresis inputs. The unknown time-varying delay uncertainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design, and the effect of the uncertain hysteresis with the Prandtl-Ishlinskii (PI) model r... View full abstract»

• ### Lag Synchronization of Unknown Chaotic Delayed Yang–Yang-Type Fuzzy Neural Networks With Noise Perturbation Based on Adaptive Control and Parameter Identification

Publication Year: 2009, Page(s):1165 - 1180
Cited by:  Papers (52)
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This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that ... View full abstract»

• ### The Global Kernel $k$-Means Algorithm for Clustering in Feature Space

Publication Year: 2009, Page(s):1181 - 1194
Cited by:  Papers (47)
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Kernel k-means is an extension of the standard k-means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedu... View full abstract»

• ### RKHS Bayes Discriminant: A Subspace Constrained Nonlinear Feature Projection for Signal Detection

Publication Year: 2009, Page(s):1195 - 1203
Cited by:  Papers (2)
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Given the knowledge of class probability densities, a priori probabilities, and relative risk levels, Bayes classifier provides the optimal minimum-risk decision rule. Specifically, focusing on the two-class (detection) scenario, under certain symmetry assumptions, matched filters provide optimal results for the detection problem. Noticing that the Bayes classifier is in fact a nonli... View full abstract»

• ### Adaptive Neural Control for Strict-Feedback Nonlinear Systems Without Backstepping

Publication Year: 2009, Page(s):1204 - 1209
Cited by:  Papers (47)
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In this brief, a new adaptive neurocontrol algorithm for a single-input-single-output (SISO) strict-feedback nonlinear system is proposed. Most of the previous adaptive neural control algorithms for strict-feedback nonlinear systems were based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it dem... View full abstract»

• ### Adaptive Neural Control for a Class of Strict-Feedback Nonlinear Systems With State Time Delays

Publication Year: 2009, Page(s):1209 - 1215
Cited by:  Papers (37)
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This brief proposes a simple control approach for a class of uncertain nonlinear systems with unknown time delays in strict-feedback form. That is, the dynamic surface control technique, which can solve the ldquoexplosion of complexityrdquo problem in the backstepping design procedure, is extended to nonlinear systems with unknown time delays. The unknown time-delay effects are removed by using ap... View full abstract»

• ### A Novel Geometric Approach to Binary Classification Based on Scaled Convex Hulls

Publication Year: 2009, Page(s):1215 - 1220
Cited by:  Papers (6)
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Geometric methods are very intuitive and provide a theoretical foundation to many optimization problems in the fields of pattern recognition and machine learning. In this brief, the notion of scaled convex hull (SCH) is defined and a set of theoretical results are exploited to support it. These results allow the existing nearest point algorithms to be directly applied to solve both the separable a... View full abstract»

• ### IEEE Computational Intelligence Society Information

Publication Year: 2009, Page(s): C3
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• ### Blank page [back cover]

Publication Year: 2009, 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