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Neural Networks, IEEE Transactions on

Issue 7 • Date July 2008

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Displaying Results 1 - 18 of 18
  • Table of contents

    Publication Year: 2008 , Page(s): C1
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  • IEEE Transactions on Neural Networks publication information

    Publication Year: 2008 , Page(s): C2
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  • Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes

    Publication Year: 2008 , Page(s): 1145 - 1153
    Cited by:  Papers (34)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (484 KB) |  | HTML iconHTML  

    The stationarity requirement for the process generating the data is a common assumption in classifiers' design. When such hypothesis does not hold, e.g., in applications affected by aging effects, drifts, deviations, and faults, classifiers must react just in time, i.e., exactly when needed, to track the process evolution. The first step in designing effective just-in-time classifiers requires detection of the temporal instant associated with the process change, and the second one needs an update of the knowledge base used by the classification system to track the process evolution. This paper addresses the change detection aspect leaving the design of just-in-time adaptive classification systems to a companion paper. Two completely automatic tests for detecting nonstationarity phenomena are suggested, which neither require a priori information nor assumptions about the process generating the data. In particular, an effective computational intelligence-inspired test is provided to deal with multidimensional situations, a scenario where traditional change detection methods are generally not applicable or scarcely effective. View full abstract»

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  • Distributed EM Algorithm for Gaussian Mixtures in Sensor Networks

    Publication Year: 2008 , Page(s): 1154 - 1166
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1058 KB) |  | HTML iconHTML  

    This paper presents a distributed expectation-maximization (EM) algorithm over sensor networks. In the E-step of this algorithm, each sensor node independently calculates local sufficient statistics by using local observations. A consensus filter is used to diffuse local sufficient statistics to neighbors and estimate global sufficient statistics in each node. By using this consensus filter, each node can gradually diffuse its local information over the entire network and asymptotically the estimate of global sufficient statistics is obtained. In the M-step of this algorithm, each sensor node uses the estimated global sufficient statistics to update model parameters of the Gaussian mixtures, which can maximize the log-likelihood in the same way as in the standard EM algorithm. Because the consensus filter only requires that each node communicate with its neighbors, the distributed EM algorithm is scalable and robust. It is also shown that the distributed EM algorithm is a stochastic approximation to the standard EM algorithm. Thus, it converges to a local maximum of the log-likelihood. Several simulations of sensor networks are given to verify the proposed algorithm. View full abstract»

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  • Distributed Parallel Support Vector Machines in Strongly Connected Networks

    Publication Year: 2008 , Page(s): 1167 - 1178
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1172 KB) |  | HTML iconHTML  

    In this paper, we propose a distributed parallel support vector machine (DPSVM) training mechanism in a configurable network environment for distributed data mining. The basic idea is to exchange support vectors among a strongly connected network (SCN) so that multiple servers may work concurrently on distributed data set with limited communication cost and fast training speed. The percentage of servers that can work in parallel and the communication overhead may be adjusted through network configuration. The proposed algorithm further speeds up through online implementation and synchronization. We prove that the global optimal classifier can be achieved iteratively over an SCN. Experiments on a real-world data set show that the computing time scales well with the size of the training data for most networks. Numerical results show that a randomly generated SCN may achieve better performance than the state of the art method, cascade SVM, in terms of total training time. View full abstract»

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  • Fault-Tolerant Indirect Adaptive Neurocontrol for a Static Synchronous Series Compensator in a Power Network With Missing Sensor Measurements

    Publication Year: 2008 , Page(s): 1179 - 1195
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1861 KB) |  | HTML iconHTML  

    Identification and control of nonlinear systems depend on the availability and quality of sensor measurements. Measurements can be corrupted or interrupted due to sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software (referred to as missing sensor measurements in this paper). This paper proposes a novel fault-tolerant indirect adaptive neurocontroller (FTIANC) for controlling a static synchronous series compensator (SSSC), which is connected to a power network. The FTIANC consists of a sensor evaluation and (missing sensor) restoration scheme (SERS), a radial basis function neuroidentifier (RBFNI), and a radial basis function neurocontroller (RBFNC). The SERS provides a set of fault-tolerant measurements to the RBFNI and RBFNC. The resulting FTIANC is able to provide fault-tolerant effective control to the SSSC when some crucial time-varying sensor measurements are not available. Simulation studies are carried out on a single machine infinite bus (SMIB) as well as on the IEEE 10-machine 39-bus power system, for the SSSC equipped with conventional PI controllers (CONVC) and the FTIANC without any missing sensors, as well as for the FTIANC with multiple missing sensors. Results show that the transient performances of the proposed FTIANC with and without missing sensors are both superior to the CONVC used by the SSSC (without any missing sensors) over a wide range of system operating conditions. The proposed fault-tolerant control is readily applicable to other plant models in power systems. View full abstract»

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  • On Real-Time AER 2-D Convolutions Hardware for Neuromorphic Spike-Based Cortical Processing

    Publication Year: 2008 , Page(s): 1196 - 1219
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3446 KB) |  | HTML iconHTML  

    In this paper, a chip that performs real-time image convolutions with programmable kernels of arbitrary shape is presented. The chip is a first experimental prototype of reduced size to validate the implemented circuits and system level techniques. The convolution processing is based on the address-event-representation (AER) technique, which is a spike-based biologically inspired image and video representation technique that favors communication bandwidth for pixels with more information. As a first test prototype, a pixel array of 16times16 has been implemented with programmable kernel size of up to 16times16. The chip has been fabricated in a standard 0.35 mum complimentary metal-oxide-semiconductor (CMOS) process. The technique also allows to process larger size images by assembling 2D arrays of such chips. Pixel operation exploits low-power mixed analog-digital circuit techniques. Because of the low currents involved (down to nanoamperes or even picoamperes), an important amount of pixel area is devoted to mismatch calibration. The rest of the chip uses digital circuit techniques, both synchronous and asynchronous. The fabricated chip has been thoroughly tested, both at the pixel level and at the system level. Specific computer interfaces have been developed for generating AER streams from conventional computers and feeding them as inputs to the convolution chip, and for grabbing AER streams coming out of the convolution chip and storing and analyzing them on computers. Extensive experimental results are provided. At the end of this paper, we provide discussions and results on scaling up the approach for larger pixel arrays and multilayer cortical AER systems. View full abstract»

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  • Stability and Almost Disturbance Decoupling Analysis of Nonlinear System Subject to Feedback Linearization and Feedforward Neural Network Controller

    Publication Year: 2008 , Page(s): 1220 - 1230
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (895 KB) |  | HTML iconHTML  

    This paper studies the tracking and almost disturbance decoupling problem of nonlinear system based on the feedback linearization and multilayered feedforward neural network approach. The feedback linearization and neural network controller guarantees exponentially global uniform ultimate bounded stability and the almost disturbance decoupling performance without using any learning or adaptive algorithms. The proposed approach provides the architecture of the neural network and the weights among the layers in order to guarantee stability of the system. Moreover, the new approach renders the system to be stable with the almost disturbance decoupling property at each step of selecting weights to enhance the performance if the proposed sufficient conditions are maintained. This study constructs a controller, under appropriate conditions, such that the resulting closed-loop system is valid for any initial condition and bounded tracking signal with the following characteristics: input-to-state stability with respect to disturbance inputs and almost disturbance decoupling performance. One example, which cannot be solved by the first paper on the almost disturbance decoupling problem, is proposed in this study to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by our proposed approach. In order to demonstrate the practical applicability, a famous ball-and-beam system has been investigated. View full abstract»

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  • Dynamic Neural-Network-Based Model-Predictive Control of an Industrial Baker's Yeast Drying Process

    Publication Year: 2008 , Page(s): 1231 - 1242
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    This paper presents dynamic neural-network-based model-predictive control (MPC) structure for a baker's yeast drying process. Mathematical model consists of two partial nonlinear differential equations that are obtained from heat and mass balances inside dried granules. The drying curves that are obtained from granule-based model were used as training data for neural network (NN) models. The target is to predict the moisture content and product activity, which are very important parameters in drying process, for different horizon values. Genetic-based search algorithm determines the optimal drying profile by solving optimization problem in MPC. As a result of the performance evaluation of the proposed control structure, which is compared with the model based on nonlinear partial differential equation (PDE) and with feedforward neural network (FFN) models, it is particularly satisfactory for the drying process of a baker's yeast. View full abstract»

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  • Neurodynamic Programming and Zero-Sum Games for Constrained Control Systems

    Publication Year: 2008 , Page(s): 1243 - 1252
    Cited by:  Papers (38)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (861 KB) |  | HTML iconHTML  

    In this paper, neural networks are used along with two-player policy iterations to solve for the feedback strategies of a continuous-time zero-sum game that appears in L2-gain optimal control, suboptimal Hinfin control, of nonlinear systems affine in input with the control policy having saturation constraints. The result is a closed-form representation, on a prescribed compact set chosen a priori, of the feedback strategies and the value function that solves the associated Hamilton-Jacobi-Isaacs (HJI) equation. The closed-loop stability, L2-gain disturbance attenuation of the neural network saturated control feedback strategy, and uniform convergence results are proven. Finally, this approach is applied to the rotational/translational actuator (RTAC) nonlinear benchmark problem under actuator saturation, offering guaranteed stability and disturbance attenuation. View full abstract»

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  • Exchange Monte Carlo Sampling From Bayesian Posterior for Singular Learning Machines

    Publication Year: 2008 , Page(s): 1253 - 1266
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1473 KB) |  | HTML iconHTML  

    Many singular learning machines such as neural networks, normal mixtures, Bayesian networks, and hidden Markov models belong to singular learning machines and are widely used in practical information systems. In these learning machines, it is well known that Bayesian learning provides better generalization performance than maximum-likelihood estimation. However, it needs huge computational cost to sample from a Bayesian posterior distribution of a singular learning machine by a conventional Markov chain Monte Carlo (MCMC) method, such as the metropolis algorithm, because of singularities. Recently, the exchange Monte Carlo (MC) method, which is well known as an improved algorithm of MCMC method, has been proposed to apply to Bayesian neural network learning in the literature. In this paper, we propose the idea that the exchange MC method has a better effect on Bayesian learning in singular learning machines than that in regular learning machines, and show its effectiveness by comparing the numerical stochastic complexity with the theoretical one. View full abstract»

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  • A General Wrapper Approach to Selection of Class-Dependent Features

    Publication Year: 2008 , Page(s): 1267 - 1278
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (610 KB) |  | HTML iconHTML  

    In this paper, we argue that for a C-class classification problem, C 2-class classifiers, each of which discriminating one class from the other classes and having a characteristic input feature subset, should in general outperform, or at least match the performance of, a C-class classifier with one single input feature subset. For each class, we select a desirable feature subset, which leads to the lowest classification error rate for this class using a classifier for a given feature subset search algorithm. To fairly compare all models, we propose a weight method for the class-dependent classifier, i.e., assigning a weight to each model's output before the comparison is carried out. The method's performance is evaluated on two artificial data sets and several real-world benchmark data sets, with the support vector machine (SVM) as the classifier , and with the RELIEF, class separability, and minimal-redundancy–maximal-relevancy (mRMR) as attribute importance measures. Our results indicate that the class-dependent feature subsets found by our approach can effectively remove irrelevant or redundant features, while maintaining or improving (sometimes substantially ) the classification accuracy, in comparison with other feature selection methods. View full abstract»

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  • A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

    Publication Year: 2008 , Page(s): 1279 - 1298
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3358 KB) |  | HTML iconHTML  

    Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autoxnomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh , 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments. View full abstract»

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  • Delay-Distribution-Dependent Exponential Stability Criteria for Discrete-Time Recurrent Neural Networks With Stochastic Delay

    Publication Year: 2008 , Page(s): 1299 - 1306
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (852 KB) |  | HTML iconHTML  

    This brief is concerned with the analysis problem of global exponential stability in the mean square sense for a class of linear discrete-time recurrent neural networks (DRNNs) with stochastic delay. Different from the prior research works, the effects of both variation range and probability distribution of the time delay are involved in the proposed method. First, a modeling method is proposed by translating the probability distribution of the time delay into parameter matrices of the transformed DRNN model, where the delay is characterized by a stochastic binary distributed variable. Based on the new method, the global exponential stability in the mean square sense for the DRNNs with stochastic delay is investigated by using the Lyapunov-Krasovskii functional and exploiting some new analysis techniques. A numerical example is provided to show the effectiveness and the applicability of the proposed method. View full abstract»

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  • Semisupervised Learning Based on Generalized Point Charge Models

    Publication Year: 2008 , Page(s): 1307 - 1311
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1047 KB) |  | HTML iconHTML  

    The recent years have witnessed a surge of interest in semisupervised learning. Numerous methods have been proposed for learning from partially labeled data. In this brief, a novel semisupervised learning approach based on an electrostatic field model is proposed. We treat the labeled data points as point charges, therefore the remaining unlabeled data points are placed in the electrostatic fields generated by these charges. The labels of these unlabeled data points can be regarded as the electric potentials of the electrostatic field at their corresponding places. Moreover, we also develop an efficient way to extend our method for out-of-sample data and analyze theoretically the relationship between our method and the traditional graph-based methods. Finally, the experimental results on both toy and real-world data sets are provided to show the effectiveness of our method. View full abstract»

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    Publication Year: 2008 , Page(s): 1312
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    Publication Year: 2008 , Page(s): C3
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    Publication Year: 2008 , 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