IEEE Transactions on Neural Networks and Learning Systems
Issue 6 • June-2012
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Year: 2012 | Volume: 23 | Issue: 6
IEEE Transactions on Neural Networks and Learning Systems publication information
Publication Year: 2012,Page(s):C2 - C2
IEEE Transactions on Neural Networks and Learning Systems publication information
Year: 2012 | Volume: 23 | Issue: 6
Global Stability of Complex-Valued Recurrent Neural Networks With Time-Delays
Publication Year: 2012,Page(s):853 - 865
Cited by: Papers (325)
Since the last decade, several complex-valued neural networks have been developed and applied in various research areas. As an extension of real-valued recurrent neural networks, complex-valued recurrent neural networks use complex-valued states, connection weights, or activation functions with much more complicated properties than real-valued ones. This paper presents several sufficient condition...Show More
Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects
Publication Year: 2012,Page(s):866 - 875
Cited by: Papers (313)
This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations...Show More
Sparse Tensor Discriminant Color Space for Face Verification
Su-Jing Wang;Jian Yang;Ming-Fang Sun;Xu-Jun Peng;Ming-Ming Sun;Chun-Guang Zhou
Publication Year: 2012,Page(s):876 - 888
Cited by: Papers (106)
As one of the fundamental features, color provides useful information and plays an important role for face recognition. Generally, the choice of a color space is different for different visual tasks. How can a color space be sought for the specific face recognition problem? To address this problem, we propose a sparse tensor discriminant color space (STDCS) model that represents a color image as a...Show More
Programming Time-Multiplexed Reconfigurable Hardware Using a Scalable Neuromorphic Compiler
Kirill Minkovich;Narayan Srinivasa;Jose M. Cruz-Albrecht;Youngkwan Cho;Aleksey Nogin
Publication Year: 2012,Page(s):889 - 901
Scalability and connectivity are two key challenges in designing neuromorphic hardware that can match biological levels. In this paper, we describe a neuromorphic system architecture design that addresses an approach to meet these challenges using traditional complementary metal-oxide-semiconductor (CMOS) hardware. A key requirement in realizing such neural architectures in hardware is the ability...Show More
Laplacian Embedded Regression for Scalable Manifold Regularization
Publication Year: 2012,Page(s):902 - 915
Cited by: Papers (52)
Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Lapl...Show More
Spiking neurons can realize several computational operations when firing cooperatively. This is a prevalent notion, although the mechanisms are not yet understood. A way by which neural assemblies compute is proposed in this paper. It is shown how neural coalitions represent things (and world states), memorize them, and control their hierarchical relations in order to perform algorithms. It is des...Show More
Extracting Representative Information to Enhance Flexible Data Queries
Publication Year: 2012,Page(s):928 - 941
Cited by: Papers (9)
Extracting representative information is of great interest in data queries and web applications nowadays, where approximate match between attribute values/records is an important issue in the extraction process. This paper proposes an approach to extracting representative tuples from data classes under an extended possibility-based data model, and to introducing a measure (namely, relation compact...Show More
Robust Synchronization for 2-D Discrete-Time Coupled Dynamical Networks
Publication Year: 2012,Page(s):942 - 953
Cited by: Papers (57)
In this paper, a new synchronization problem is addressed for an array of 2-D coupled dynamical networks. The class of systems under investigation is described by the 2-D nonlinear state space model which is oriented from the well-known Fornasini-Marchesini second model. For such a new 2-D complex network model, both the network dynamics and the couplings evolve in two independent directions. A ne...Show More
Network-Based High Level Data Classification
Publication Year: 2012,Page(s):954 - 970
Cited by: Papers (75)
Traditional supervised data classification considers only physical features (e.g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification tha...Show More
Neural Network Structure for Spatio-Temporal Long-Term Memory
Vu Anh Nguyen;Janusz A. Starzyk;Wooi-Boon Goh;Daniel Jachyra
Publication Year: 2012,Page(s):971 - 983
Cited by: Papers (33)
This paper proposes a neural network structure for spatio-temporal learning and recognition inspired by the long-term memory (LTM) model of the human cortex. Our structure is able to process real-valued and multidimensional sequences. This capability is attained by addressing three critical problems in sequential learning, namely the error tolerance, the significance of sequence elements and memor...Show More
Feedback Optimal Control of Distributed Parameter Systems by Using Finite-Dimensional Approximation Schemes
Publication Year: 2012,Page(s):984 - 996
Cited by: Papers (32)
Optimal control for systems described by partial differential equations is investigated by proposing a methodology to design feedback controllers in approximate form. The approximation stems from constraining the control law to take on a fixed structure, where a finite number of free parameters can be suitably chosen. The original infinite-dimensional optimization problem is then reduced to a math...Show More
Generalized SMO Algorithm for SVM-Based Multitask Learning
Publication Year: 2012,Page(s):997 - 1003
Cited by: Papers (55)
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as “lear...Show More
Complexity-Reduced Scheme for Feature Extraction With Linear Discriminant Analysis
Publication Year: 2012,Page(s):1003 - 1009
Cited by: Papers (10)
Owing to the singularity of the within-class scatter, linear discriminant analysis (LDA) becomes ill-posed for small sample size (SSS) problems. Null-space-based LDA (NLDA), which is an extension of LDA, provides good discriminant performances for SSS problems. Yet, as the original scheme for the feature extractor (FE) of NLDA suffers from a complexity burden, a few modified schemes have since bee...Show More
2013 IEEE Symposium Series on Computational Intelligence–IEEE SSCI 2013
Publication Year: 2012,Page(s):1010 - 1010
2013 IEEE Symposium Series on Computational Intelligence–IEEE SSCI 2013
Year: 2012 | Volume: 23 | Issue: 6
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Year: 2012 | Volume: 23 | Issue: 6
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