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# IEEE Transactions on Neural Networks

## Filter Results

Displaying Results 1 - 25 of 29

Publication Year: 2007, Page(s):C1 - C4
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• ### IEEE Transactions on Neural Networks publication information

Publication Year: 2007, Page(s): C2
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• ### Guest Editorial Special Issue on Neural Networks for Feedback Control Systems

Publication Year: 2007, Page(s):969 - 972
Cited by:  Papers (8)
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The twenty-two papers in this special issue are devoted to neural networks for feedback control systems. Covers some of the following topics: reinforcement learning; applications; neurocontrol systems; discrete time systems; and network architectures and training methods. View full abstract»

• ### Kernel-Based Least Squares Policy Iteration for Reinforcement Learning

Publication Year: 2007, Page(s):973 - 992
Cited by:  Papers (92)
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In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels ... View full abstract»

• ### Robust Reinforcement Learning Control Using Integral Quadratic Constraints for Recurrent Neural Networks

Publication Year: 2007, Page(s):993 - 1002
Cited by:  Papers (13)
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The applicability of machine learning techniques for feedback control systems is limited by a lack of stability guarantees. Robust control theory offers a framework for analyzing the stability of feedback control loops, but for the integral quadratic constraint (IQC) framework used here, all components are required to be represented as linear, time-invariant systems plus uncertainties with, for IQ... View full abstract»

• ### Training Recurrent Neurocontrollers for Real-Time Applications

Publication Year: 2007, Page(s):1003 - 1015
Cited by:  Papers (27)
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In this paper, we introduce a new approach to train recurrent neurocontrollers for real-time applications. We begin with training a recurrent neurocontroller for robustness on high-fidelity models of physical systems. For training, we use a recently developed derivative-free Kalman filter method which we enhance for controller training. After training, we fix weights of our recurrent neurocontroll... View full abstract»

• ### SVM-Based Tree-Type Neural Networks as a Critic in Adaptive Critic Designs for Control

Publication Year: 2007, Page(s):1016 - 1030
Cited by:  Papers (20)
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In this paper, we use the approach of adaptive critic design (ACD) for control, specifically, the action-dependent heuristic dynamic programming (ADHDP) method. A least squares support vector machine (SVM) regressor has been used for generating the control actions, while an SVM-based tree-type neural network (NN) is used as the critic. After a failure occurs, the critic and action are retrained in... View full abstract»

• ### Least Squares Solutions of the HJB Equation With Neural Network Value-Function Approximators

Publication Year: 2007, Page(s):1031 - 1041
Cited by:  Papers (13)
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In this paper, we present an empirical study of iterative least squares minimization of the Hamilton-Jacobi-Bellman (HJB) residual with a neural network (NN) approximation of the value function. Although the nonlinearities in the optimal control problem and NN approximator preclude theoretical guarantees and raise concerns of numerical instabilities, we present two simple methods for promoting con... View full abstract»

• ### Neural Network Control for Position Tracking of a Two-Axis Inverted Pendulum System: Experimental Studies

Publication Year: 2007, Page(s):1042 - 1048
Cited by:  Papers (32)
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In this paper, experimental studies of a decentralized neural network control scheme of the reference compensation technique applied to control a 2-degrees-of-freedom (2-DOF) inverted pendulum on an x-y plane are presented. Each axis is controlled by two separate neural network controllers to have a decoupled control structure. Neural network controllers are applied not only to balance the angle o... View full abstract»

• ### Neural Network Adaptive Output Feedback Control for Intensive Care Unit Sedation and Intraoperative Anesthesia

Publication Year: 2007, Page(s):1049 - 1066
Cited by:  Papers (35)
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The potential applications of neural adaptive control for pharmacology, in general, and anesthesia and critical care unit medicine, in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical ph... View full abstract»

• ### Fault Detection in Mechanical Systems With Friction Phenomena: An Online Neural Approximation Approach

Publication Year: 2007, Page(s):1067 - 1082
Cited by:  Papers (15)
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In this paper, the problem of fault detection in mechanical systems performing linear motion, under the action of friction phenomena is addressed. The friction effects are modeled through the dynamic LuGre model. The proposed architecture is built upon an online neural network (NN) approximator, which requires only system's position and velocity. The friction internal state is not assumed to be av... View full abstract»

• ### Neural Network Controller Development and Implementation for Spark Ignition Engines With High EGR Levels

Publication Year: 2007, Page(s):1083 - 1100
Cited by:  Papers (17)
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Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to... View full abstract»

• ### Neural Control of Fast Nonlinear Systems— Application to a Turbocharged SI Engine With VCT

Publication Year: 2007, Page(s):1101 - 1114
Cited by:  Papers (37)
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Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, b... View full abstract»

• ### Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

Publication Year: 2007, Page(s):1115 - 1128
Cited by:  Papers (23)
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An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. ldquoSnapshotrdquo solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis fu... View full abstract»

• ### Semiglobal ISpS Disturbance Attenuation With Output Tracking via Direct Adaptive Design

Publication Year: 2007, Page(s):1129 - 1148
Cited by:  Papers (15)
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Direct adaptive partial state feedback control is presented to achieve semiglobally input-to-state practically stable (ISpS) disturbance attenuation with output tracking for a class of uncertain time-varying nonlinear systems in which the unmeasured dynamics do not possess a constant disturbance attenuation level (CDAL). Identifying a necessary condition for the existence of a CDAL, direct adaptiv... View full abstract»

• ### Adaptive Control of a Class of Nonaffine Systems Using Neural Networks

Publication Year: 2007, Page(s):1149 - 1159
Cited by:  Papers (41)
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A neural control synthesis method is considered for a class of nonaffine uncertain single-input-single-output (SISO) systems. The method eliminates a fixed-point assumption and does not assume boundedness on the time derivative of a control effectiveness term. One or the other of these assumptions exist in earlier papers on this subject. Using Lyapunov's direct method, it is shown that all the sig... View full abstract»

• ### Novel ${cal L}_{1}$ Neural Network Adaptive Control Architecture With Guaranteed Transient Performance

Publication Year: 2007, Page(s):1160 - 1171
Cited by:  Papers (37)
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In this paper, we present a novel neural network (NN) adaptive control architecture with guaranteed transient performance. With this new architecture, both input and output signals of an uncertain nonlinear system follow a desired linear system during the transient phase, in addition to stable tracking. This new architecture uses a low-pass filter in the feedback loop, which consequently enables t... View full abstract»

• ### Active State Estimation for Nonlinear Systems: A Neural Approximation Approach

Publication Year: 2007, Page(s):1172 - 1184
Cited by:  Papers (12)
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In this paper, we consider the problem of actively providing an estimate of the state of a stochastic dynamic system over a (possibly long) finite time horizon. The active estimation problem (AEP) is formulated as a stochastic optimal control one, in which the minimization of a suitable uncertainty measure is carried out. Toward this end, the use of the Renyi entropy as an information measure is p... View full abstract»

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

Publication Year: 2007, Page(s):1185 - 1195
Cited by:  Papers (71)
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This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on t... View full abstract»

• ### Neural-Network-Based Approximate Output Regulation of Discrete-Time Nonlinear Systems

Publication Year: 2007, Page(s):1196 - 1208
Cited by:  Papers (8)
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The existing approaches to the discrete-time nonlinear output regulation problem rely on the offline solution of a set of mixed nonlinear functional equations known as discrete regulator equations. For complex nonlinear systems, it is difficult to solve the discrete regulator equations even approximately. Moreover, for systems with uncertainty, these approaches cannot offer a reliable solution. By... View full abstract»

• ### Support Vector Networks in Adaptive Friction Compensation

Publication Year: 2007, Page(s):1209 - 1219
Cited by:  Papers (8)
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This paper presents our research on how support vector regression (SVR) and parametric adaptive learning, which are normally used independently, can be exploited together to benefit adaptive neural control. In the context of friction compensation for servo-motion control systems, we present the notion of support vector networks which play an essential role in combining SVR and adaptive neural netw... View full abstract»

• ### Self-Organizing Approximation-Based Control for Higher Order Systems

Publication Year: 2007, Page(s):1220 - 1231
Cited by:  Papers (15)
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Adaptive approximation-based control typically uses approximators with a predefined set of basis functions. Recently, spatially dependent methods have defined self-organizing approximators where new locally supported basis elements were incorporated when existing basis elements were insufficiently excited. In this paper, performance-dependent self-organizing approximators will be defined. The desi... View full abstract»

• ### Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems

Publication Year: 2007, Page(s):1232 - 1241
Cited by:  Papers (88)
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This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) via sliding-mode approach for a class of nonlinear systems. The proposed SAFNC system is comprised of a computation controller and a supervisory controller. The computation controller including a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller. The SOFNN identifier is used to online esti... View full abstract»

• ### Characterization of Analog Local Cluster Neural Network Hardware for Control

Publication Year: 2007, Page(s):1242 - 1253
Cited by:  Papers (1)
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The local cluster neural network (LCNN) was designed for analog realization especially suited to applications in control systems. It uses clusters of sigmoidal neurons to generate basis functions that are localized in multidimensional input space. Sigmoidal neurons are well suited to analog electronic realization. In this paper, we report the results of extensive measurements that characterize the... View full abstract»

• ### Application of Neural Network to Hybrid Systems With Binary Inputs

Publication Year: 2007, Page(s):1254 - 1261
Cited by:  Papers (7)
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Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching on and off of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is bas... View full abstract»

## 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