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# IEEE Transactions on Neural Networks

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Displaying Results 1 - 19 of 19

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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• ### The Effect of Target Vector Selection on the Invariance of Classifier Performance Measures

Publication Year: 2009, Page(s):745 - 757
Cited by:  Papers (1)  |  Patents (4)
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In this paper, the multiclass supervised training problem is considered when a discrete set of classes is assumed. Upon generating affine models for finite data sets, we have observed the invariance of certain measures of performance after a trained classifier has been presented with test data of unknown classification. Specifically, after constructing mappings between training vectors and their d... View full abstract»

• ### Neural Network Control of Multifingered Robot Hands Using Visual Feedback

Publication Year: 2009, Page(s):758 - 767
Cited by:  Papers (23)
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It is interesting to observe that humans are able to manipulate an object easily and skillfully without the exact knowledge of the object, contact points, or kinematics of our fingers. However, research so far on multifingered robot control has assumed that the kinematics and contact points of the fingers are known exactly. In many applications of multifingered robot hands, the kinematics and cont... View full abstract»

• ### Spatio–Temporal Memories for Machine Learning: A Long-Term Memory Organization

Publication Year: 2009, Page(s):768 - 780
Cited by:  Papers (15)  |  Patents (24)
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Design of artificial neural structures capable of reliable and flexible long-term spatio-temporal memory is of paramount importance in machine intelligence. To this end, we propose a novel, biologically inspired, long-term memory (LTM) architecture. We intend to use it as a building block of a neuron-level architecture that is able to mimic natural intelligence through learning, anticipation, and ... View full abstract»

• ### State Estimation for Coupled Uncertain Stochastic Networks With Missing Measurements and Time-Varying Delays: The Discrete-Time Case

Publication Year: 2009, Page(s):781 - 793
Cited by:  Papers (125)
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This paper is concerned with the problem of state estimation for a class of discrete-time coupled uncertain stochastic complex networks with missing measurements and time-varying delay. The parameter uncertainties are assumed to be norm-bounded and enter into both the network state and the network output. The stochastic Brownian motions affect not only the coupling term of the network but also the... View full abstract»

• ### Convergence in Networks With Counterclockwise Neural Dynamics

Publication Year: 2009, Page(s):794 - 804
Cited by:  Papers (4)
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The notion of counterclockwise (ccw) input-output (I-O) dynamics, introduced by Angeli (2006) to deal with questions of multistability in interconnected dynamical systems, is applied and further developed in order to analyze convergence and stability of neural networks. By pursuing a modular approach, we interpret a cellular nonlinear network (CNN) as a positive feedback of a parallel block of sin... View full abstract»

• ### Model-Based Clustering by Probabilistic Self-Organizing Maps

Publication Year: 2009, Page(s):805 - 826
Cited by:  Papers (15)
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In this paper, we consider the learning process of a probabilistic self-organizing map (PbSOM) as a model-based data clustering procedure that preserves the topological relationships between data clusters in a neural network. Based on this concept, we develop a coupling-likelihood mixture model for the PbSOM that extends the reference vectors in Kohonen's self-organizing map (SOM) to multivariate ... View full abstract»

• ### Building Sparse Multiple-Kernel SVM Classifiers

Publication Year: 2009, Page(s):827 - 839
Cited by:  Papers (41)
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The support vector machines (SVMs) have been very successful in many machine learning problems. However, they can be slow during testing because of the possibly large number of support vectors obtained. Recently, Wu (2005) proposed a sparse formulation that restricts the SVM to use a small number of expansion vectors. In this paper, we further extend this idea by integrating with techniques from m... View full abstract»

• ### Almost Sure Exponential Stability of Recurrent Neural Networks With Markovian Switching

Publication Year: 2009, Page(s):840 - 855
Cited by:  Papers (108)
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This paper presents new stability results for recurrent neural networks with Markovian switching. First, algebraic criteria for the almost sure exponential stability of recurrent neural networks with Markovian switching and without time delays are derived. The results show that the almost sure exponential stability of such a neural network does not require the stability of the neural network at ev... View full abstract»

• ### A Novel Blood Glucose Regulation Using TSK$^{0}$-FCMAC: A Fuzzy CMAC Based on the Zero-Ordered TSK Fuzzy Inference Scheme

Publication Year: 2009, Page(s):856 - 871
Cited by:  Papers (22)
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This paper presents a novel blood glucose regulation for type I (insulin-dependent) diabetes mellitus patients using biologically inspired TSK0-FCMAC, a fuzzy cerebellar model articulation controller (CMAC) based on the zero-ordered Takagi-Sugeno-Kang (TSK) fuzzy inference scheme. TSK0-FCMAC is capable of performing localized online training with an effective fuzzy inference ... View full abstract»

• ### Fault Detection and Diagnosis Based on Modeling and Estimation Methods

Publication Year: 2009, Page(s):872 - 881
Cited by:  Papers (32)
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This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the ... View full abstract»

• ### Block-Quantized Support Vector Ordinal Regression

Publication Year: 2009, Page(s):882 - 890
Cited by:  Papers (8)
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Support vector ordinal regression (SVOR) is a recently proposed ordinal regression (OR) algorithm. Despite its theoretical and empirical success, the method has one major bottleneck, which is the high computational complexity. In this brief, we propose a both practical and theoretical guaranteed algorithm, block-quantized support vector ordinal regression (BQSVOR), where we approximate the kernel ... View full abstract»

• ### A Novel Template Reduction Approach for the $K$-Nearest Neighbor Method

Publication Year: 2009, Page(s):890 - 896
Cited by:  Papers (42)
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The K-nearest neighbor (KNN) rule is one of the most widely used pattern classification algorithms. For large data sets, the computational demands for classifying patterns using KNN can be prohibitive. A way to alleviate this problem is through the condensing approach. This means we remove patterns that are more of a computational burden but do not contribute to better classification accura... View full abstract»

• ### Comments on "Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks

Publication Year: 2009, Page(s):897 - 898
Cited by:  Papers (5)
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The purpose of this comment is to point out some mistakes in the above paper. It is shown that the main results of the paper cannot stand in general. Also, it is pointed out that after some corrections, the proposed control algorithm is still applicable to a more simple system. For simplicity, all the symbols in this comment are the same as those in the above paper. View full abstract»

Publication Year: 2009, Page(s): 899
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Publication Year: 2009, Page(s): 900
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• ### 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