Volume 24 Issue 3 • March 2013
Filter Results
-
Table of contents
Publication Year: 2013, Page(s): C1|
PDF (115 KB)
-
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information
Publication Year: 2013, Page(s): C2|
PDF (138 KB)
-
Dissipativity Analysis for Discrete-Time Stochastic Neural Networks With Time-Varying Delays
Publication Year: 2013, Page(s):345 - 355
Cited by: Papers (54)In this paper, the problem of dissipativity analysis is discussed for discrete-time stochastic neural networks with time-varying discrete and finite-distributed delays. The discretized Jensen inequality and lower bounds lemma are adopted to deal with the involved finite sum quadratic terms, and a sufficient condition is derived to ensure the considered neural networks to be globally asymptotically... View full abstract»
-
Model-Based Online Learning With Kernels
Publication Year: 2013, Page(s):356 - 369
Cited by: Papers (27)New optimization models and algorithms for online learning with Kernels (OLK) in classification, regression, and novelty detection are proposed in a reproducing Kernel Hilbert space. Unlike the stochastic gradient descent algorithm, called the naive online Reg minimization algorithm (NORMA), OLK algorithms are obtained by solving a constrained optimization problem based on the proposed models. By ... View full abstract»
-
Adaptive Control for Nonlinear Pure-Feedback Systems With High-Order Sliding Mode Observer
Publication Year: 2013, Page(s):370 - 382
Cited by: Papers (56)Most of the available control schemes for pure-feedback systems are derived based on the backstepping technique. On the contrary, this paper presents a novel adaptive control design for nonlinear pure-feedback systems without using backstepping. By introducing a set of alternative state variables and the corresponding transform, state-feedback control of the pure-feedback system can be viewed as o... View full abstract»
-
Low-Rank Structure Learning via Nonconvex Heuristic Recovery
Publication Year: 2013, Page(s):383 - 396
Cited by: Papers (51)In this paper, we propose a nonconvex framework to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilizes convex norms to measure the sparseness, our method introduces more reasonable nonconvex measurements to enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions. We will, respectively, introduce... View full abstract»
-
Synaptic Variability in a Cortical Neuromorphic Circuit
Publication Year: 2013, Page(s):397 - 409
Cited by: Papers (12)Variable behavior has been observed in several mechanisms found in biological neurons, resulting in changes in neural behavior that might be useful to capture in neuromorphic circuits. This paper presents a neuromorphic cortical neuron with synaptic neurotransmitter-release variability, which is designed to be used in neural networks as part of the Biomimetic Real-Time Cortex project. This neuron ... View full abstract»
-
Sampled-Data Synchronization of Chaotic Lur'e Systems With Time Delays
Publication Year: 2013, Page(s):410 - 421
Cited by: Papers (68)This paper studies the problem of sampled-data control for master-slave synchronization schemes that consist of identical chaotic Lur'e systems with time delays. It is assumed that the sampling periods are arbitrarily varying but bounded. In order to take full advantage of the available information about the actual sampling pattern, a novel Lyapunov functional is proposed, which is positive defini... View full abstract»
-
Multiplicative Update Rules for Concurrent Nonnegative Matrix Factorization and Maximum Margin Classification
Publication Year: 2013, Page(s):422 - 434
Cited by: Papers (10)The state-of-the-art classification methods which employ nonnegative matrix factorization (NMF) employ two consecutive independent steps. The first one performs data transformation (dimensionality reduction) and the second one classifies the transformed data using classification methods, such as nearest neighbor/centroid or support vector machines (SVMs). In the following, we focus on using NMF fa... View full abstract»
-
Distributed Synchronization of Coupled Neural Networks via Randomly Occurring Control
Publication Year: 2013, Page(s):435 - 447
Cited by: Papers (123)In this paper, we study the distributed synchronization and pinning distributed synchronization of stochastic coupled neural networks via randomly occurring control. Two Bernoulli stochastic variables are used to describe the occurrences of distributed adaptive control and updating law according to certain probabilities. Both distributed adaptive control and updating law for each vertex in a netwo... View full abstract»
-
Portfolio of Automated Trading Systems: Complexity and Learning Set Size Issues
Publication Year: 2013, Page(s):448 - 459
Cited by: Papers (9)In this paper, we consider using profit/loss histories of multiple automated trading systems (ATSs) as N input variables in portfolio management. By means of multivariate statistical analysis and simulation studies, we analyze the influences of sample size (L) and input dimensionality on the accuracy of determining the portfolio weights. We find that degradation in portfolio performance due to ine... View full abstract»
-
Regularized Mixture Density Estimation With an Analytical Setting of Shrinkage Intensities
Publication Year: 2013, Page(s):460 - 470
Cited by: Papers (4)In this paper, we propose a method for P-variate probability density estimation assuming a Gaussian mixture model (GMM). Our method exploits a regularization technique for improving the estimation accuracy of the GMM component covariance matrices. We derive an expectation maximization algorithm for fitting our regularized GMM (RGMM), which exploits an analytical Ledoit-Wolf-type shrinkage estimati... View full abstract»
-
Stochastic Optimal Controller Design for Uncertain Nonlinear Networked Control System via Neuro Dynamic Programming
Publication Year: 2013, Page(s):471 - 484
Cited by: Papers (51)The stochastic optimal controller design for the nonlinear networked control system (NNCS) with uncertain system dynamics is a challenging problem due to the presence of both system nonlinearities and communication network imperfections, such as random delays and packet losses, which are not unknown a priori. In the recent literature, neuro dynamic programming (NDP) techniques, based on value and ... View full abstract»
-
Simple Exponential Family PCA
Publication Year: 2013, Page(s):485 - 497
Cited by: Papers (26)Principal component analysis (PCA) is a widely used model for dimensionality reduction. In this paper, we address the problem of determining the intrinsic dimensionality of a general type data population by selecting the number of principal components for a generalized PCA model. In particular, we propose a generalized Bayesian PCA model, which deals with general type data by employing exponential... View full abstract»
-
Dynamics Analysis of a Population Decoding Model
Publication Year: 2013, Page(s):498 - 503
Cited by: Papers (1)Information processing in the nervous system involves the activity of large populations of neurons. It is difficult to extract information from these population codes because of the noise inherent in neuronal responses. We propose a divisive normalization model to read the population codes. The dynamics of the model are analyzed by continuous attractor theory. Under certain conditions, the model p... View full abstract»
-
Learning With Kernel Smoothing Models and Low-Discrepancy Sampling
Publication Year: 2013, Page(s):504 - 509
Cited by: Papers (11)This brief presents an analysis of the performance of kernel smoothing models used to estimate an unknown target function, addressing the case where the choice of the training set is part of the learning process. In particular, we consider a choice of the points at which the function is observed based on low-discrepancy sequences, which is a family of sampling methods commonly employed for efficie... View full abstract»
-
2014 IEEE World Congress on Computational Intelligence
Publication Year: 2013, Page(s): 510|
PDF (3429 KB)
-
Open Access
Publication Year: 2013, Page(s): 511|
PDF (1156 KB)
-
IEEE Xplore Digital Library
Publication Year: 2013, Page(s): 512|
PDF (1372 KB)
-
IEEE Computational Intelligence Society Information
Publication Year: 2013, Page(s): C3|
PDF (39 KB)
-
IEEE Transactions on Neural Networks information for authors
Publication Year: 2013, Page(s): C4|
PDF (39 KB)
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.
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