IEEE Transactions on Neural Networks and Learning Systems

Issue 9 • Sept. 2014

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  • Table of contents

    Publication Year: 2014, Page(s): C1
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  • IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

    Publication Year: 2014, Page(s): C2
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  • Guest Editorial Special Issue on Complex- and Hypercomplex-Valued Neural Networks

    Publication Year: 2014, Page(s):1597 - 1599
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  • Complex-Valued Recurrent Correlation Neural Networks

    Publication Year: 2014, Page(s):1600 - 1612
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2026 KB) | HTML iconHTML

    In this paper, we generalize the bipolar recurrent correlation neural networks (RCNNs) of Chiueh and Goodman for patterns whose components are in the complex unit circle. The novel networks, referred to as complex-valued RCNNs (CV-RCNNs), are characterized by a possible nonlinear function, which is applied on the real part of the scalar product of the current state and the original patterns. We sh... View full abstract»

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  • The Field of Values of a Matrix and Neural Networks

    Publication Year: 2014, Page(s):1613 - 1620
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1312 KB) | HTML iconHTML

    The field of values of a matrix, also known as the numerical range, is introduced in the context of neural networks. Using neural network techniques, an algorithm and a generalization are developed that find eigenpairs of a normal matrix. The dynamics of the algorithm can be observed on the complex plane. Only limited visualization is possible in the case when the matrix is Hermitian (or real symm... View full abstract»

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  • Different Complex ZFs Leading to Different Complex ZNN Models for Time-Varying Complex Generalized Inverse Matrices

    Publication Year: 2014, Page(s):1621 - 1631
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1550 KB) | HTML iconHTML

    As a special class of recurrent neural network, Zhang neural network (ZNN) has been recently proposed since 2001 for solving various time-varying problems, and has shown high efficiency and excellent performance for solving the problems in the real domain. In this paper, to solve online the time-varying complex generalized inverse (in most cases, the pseudoinverse) problem in the complex domain, a... View full abstract»

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  • MLMVN With Soft Margins Learning

    Publication Year: 2014, Page(s):1632 - 1644
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (986 KB) | HTML iconHTML

    In this paper, we consider a modified error-correction learning rule for the multilayer neural network with multivalued neurons (MLMVN). This modification is based on the soft margins technique, which leads to the minimization of the distance between a cluster center and the learning samples belonging to this cluster. MLMVN has a derivative-free learning algorithm based on the error-correction lea... View full abstract»

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  • Modified Multivalued Neuron With Periodic Tolerant Activation Function

    Publication Year: 2014, Page(s):1645 - 1658
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1581 KB) | HTML iconHTML

    The multivalued neuron with periodic activation function (MVN-P) was proposed by Aizenberg for solving classification problems. The boundaries between two distinct categories are crisply specified in MVN-P, which may result in slow convergence or being unable to converge at all in the learning process. In this paper, we propose a revised model of MVN-P based on the idea of unsharp boundaries. In t... View full abstract»

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  • A Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System

    Publication Year: 2014, Page(s):1659 - 1672
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1616 KB) | HTML iconHTML

    This paper presents a complex-valued interval type-2 neuro-fuzzy inference system (CIT2FIS) and derive its metacognitive projection-based learning (PBL) algorithm. Metacognitive CIT2FIS (Mc-CIT2FIS) consists of a CIT2FIS, which realizes Takagi-Sugeno-Kang type inference mechanism, as its cognitive component. A PBL with self-regulation is its metacognitive component. The rules of CIT2FIS employ int... View full abstract»

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  • Complex-Valued B-Spline Neural Networks for Modeling and Inverting Hammerstein Systems

    Publication Year: 2014, Page(s):1673 - 1685
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5955 KB) | HTML iconHTML

    Many communication signal processing applications involve modeling and inverting complex-valued (CV) Hammerstein systems. We develop a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. In particular, the CV nonlinear static function in the Hammerstein system is represented using the tenso... View full abstract»

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  • Fading Channel Prediction Based on Combination of Complex-Valued Neural Networks and Chirp Z-Transform

    Publication Year: 2014, Page(s):1686 - 1695
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3413 KB) | HTML iconHTML

    Channel prediction is an important process for channel compensation in a fading environment. If a future channel characteristic is predicted, adaptive techniques, such as pre-equalization and transmission power control, are applicable before transmission in order to avoid degradation of communications quality. Previously, we proposed channel prediction methods employing the chirp z-transform (CZT)... View full abstract»

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  • On the Correction of Anomalous Phase Oscillation in Entanglement Witnesses Using Quantum Neural Networks

    Publication Year: 2014, Page(s):1696 - 1703
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4100 KB) | HTML iconHTML

    Entanglement of a quantum system depends upon the relative phase in complicated ways, which no single measurement can reflect. Because of this, “entanglement witnesses” (measures that estimate entanglement) are necessarily limited in applicability and/or utility. We propose here a solution to the problem using quantum neural networks. A quantum system contains the information of its ... View full abstract»

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  • Global Stability Criterion for Delayed Complex-Valued Recurrent Neural Networks

    Publication Year: 2014, Page(s):1704 - 1708
    Cited by:  Papers (36)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (158 KB) | HTML iconHTML

    The stability problem for delayed complex-valued recurrent neural networks is considered in this paper. By separating complex-valued neural networks into real and imaginary parts, forming an equivalent real-valued system, and constructing appropriate Lyapunov functional, a sufficient condition to ascertain the existence, uniqueness, and globally asymptotical stability of the equilibrium point of c... View full abstract»

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  • Stability of Complex-Valued Recurrent Neural Networks With Time-Delays

    Publication Year: 2014, Page(s):1709 - 1713
    Cited by:  Papers (34)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (240 KB) | HTML iconHTML

    This brief points out two mistakes in a recently published paper on complex-valued recurrent neural networks (RNNs). Moreover, a new condition for the complex-valued activation function is presented, which is less conservative than the Lipschitz condition that is widely assumed in the literature. Based on the new condition and linear matrix inequality, some new criteria to ensure the existence, un... View full abstract»

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  • Threshold Complex-Valued Neural Associative Memory

    Publication Year: 2014, Page(s):1714 - 1718
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (440 KB) | HTML iconHTML

    In this brief, threshold complex-valued neural associative memory is proposed for information retrieval. The introduction of threshold improves network performance by excluding rotated patterns from spurious memories. A design method for constructing different types of network is developed based on complex matrix decomposition, which is capable of designing nonthreshold, threshold, non-Hermitian, ... View full abstract»

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  • Principal Component Analysis With Complex Kernel: The Widely Linear Model

    Publication Year: 2014, Page(s):1719 - 1726
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1010 KB) | HTML iconHTML

    Nonlinear complex representations, via the use of complex kernels, can be applied to model and capture the nonlinearities of complex data. Even though the theoretical tools of complex reproducing kernel Hilbert spaces (CRKHS) have been recently successfully applied to the design of digital filters and regression and classification frameworks, there is a limited research on component analysis and d... View full abstract»

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  • Ultrawideband Direction-of-Arrival Estimation Using Complex-Valued Spatiotemporal Neural Networks

    Publication Year: 2014, Page(s):1727 - 1732
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2119 KB) | HTML iconHTML

    We propose a direction of arrival (DoA) estimation method using a complex-valued neural network (CVNN) for ultrawideband (UWB) systems. We combine a complex-valued spatiotemporal neural network with power-inversion adaptive-array scheme for null-steering DoA estimation. Simulation and experiments demonstrate that the proposed method shows an estimation accuracy higher than that of conventional mul... View full abstract»

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  • Adaptive Dynamic Programming for a Class of Complex-Valued Nonlinear Systems

    Publication Year: 2014, Page(s):1733 - 1739
    Cited by:  Papers (49)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (541 KB) | HTML iconHTML

    In this brief, an optimal control scheme based on adaptive dynamic programming (ADP) is developed to solve infinite-horizon optimal control problems of continuous-time complex-valued nonlinear systems. A new performance index function is established on the basis of complex-valued state and control. Using system transformations, the complex-valued system is transformed into a real-valued one, which... View full abstract»

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  • IJCNN2015 Killarney, Ireland

    Publication Year: 2014, Page(s): 1740
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2014, Page(s): C3
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  • IEEE Transactions on Neural Networks information for authors

    Publication Year: 2014, Page(s): C4
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Aims & Scope

IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Haibo He
Dept. of Electrical, Computer, and Biomedical Engineering
University of Rhode Island
Kingston, RI 02881, USA
ieeetnnls@gmail.com