CDC2000 Novel Applications of Neural Networks

Novel Applications of Neural Networks
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation Adaptive Control: Linear Systems Estimation and Closed Loop Identification Control of Markov Processes Nonlinear Filtering and Control Modelling, Identification and Validation of Nonlinear Systems Differential Geometric Control Theory for Mechanical Systems Nonlinear Output Feedback Control Pneumatics and Compression Systems Control of Quantum Phenomena II Stability of Hybrid Systems Performance Analysis in Communication Networks Adaptive Control of Nonlinear Systems LMI Methods in Design Robust Control of Time Delay Systems Subspace Identification Methods Nonlinear Stochastic Filtering and Estimation Bifurcations, Chaos and Control I New Progress in Synthesis of Nonlinear Systems I Implementation Issues of Sliding Mode Control Theory Control of Mixing in Shear Flows Novel Neural Network Control Techniques for Industrial Motion Control Systems Physiological Control Systems Optimal Control of Hybrid Systems Stochastic Models for Communication Networks Control and Stabilisation of Nonlinear Systems New Directions in Robust Control Linear Systems Theory Advanced Topics in Systems Theory Estimation in Action Bifurcations, Chaos and Control II New Progress in Synthesis of Nonlinear Systems II Numerical Design and Analysis Techniques for Nonlinear Systems Analysis and Control of Underactuated Systems Sliding Mode Control I Challenges in the Application of Control to Computer Systems Estimation and Diagnosis of Discrete Event Systems Communications and Games Optimal Control Stochastic Systems Model Reduction Methodologies Identification and Subspace Methods Applications of Nonlinear Adaptive Control Advances in Nonlinear Output Feedback Design The Behavioural Approach to Systems and Control Vision Based Estimation and Control: Recent Advances and Open Problems Agile Control of Military Operations Sliding Mode Control II Model-based Fault Diagnosis of Industrial Processes Discrete Event Systems / Petri Nets System Identification and Confidence Estimation New Approaches to H-Infinity Control I Probabilistic Approaches to Robust Control Time Delay System Stabilisation Identification Methods Controlled Stochastic Processes Output Feedback of Nonlinear Systems Topics in Nonlinear Stabilisation Mobile Robots: Tracking Control Robust Control of Nonlinear Systems Power Systems Stabilisation and Control Disk Drive Control Hybrid Control Applications Discrete Time Systems New Approaches to H-Infinity Control II Linear Systems with Saturating Actuators New Theories in Distributed Parameter Systems Applications of Estimation and Identification Stochastic Control and Tuning Methodologies Control of Nonlinear Systems Iterative Learning and Control Coordinating Robot Systems Nonlinear Time Varying Systems Novel Applications of Neural Networks Aerospace Applications Switched Systems Implicit and Descriptor Systems LQG Periodic Systems and Disturbances New Horizons for Distributed Parameter Systems State Estimation Learning and Neuro-Control Nonlinear Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Lipschitz Continuous Neural Networks On (cal) L_P Authors: Vincent Fromion, Volume: 1, Page 3528 Paper number 1925 Abstract: This paper presents simple conditions ensuring that dynamical neural networks are incrementally stable, that is Lipschitz continuous, on (cal) L_p. A first interest of this result is that it ensures obviously the continuity of the system as an operator from a signal space to another signal space. This property may be interpreted in this context as the ability for dynamical neural networks to interpolate. In some sense, it is an extension of a well-known property of static neural networks. A second interest of this result is linked to the fact that the behaviors of Lipschitz continuous systems with respect to specific inputs or initial condition problems can be completely analyzed. Indeed, Lipschitz continuous systems have the steady-state property with respect to any inputs belonging to (cal) L_p^e with p(in) [1,(infinity)], i.e., their asymptotic behavior is uniquely determined by the asymptotic behavior of the input. Moreover, the Lipschitz continuity guarantees the existence of globally asymptotic stable (in sense of Lyapunov) equilibrium points for all constant inputs. CD001925.PDF (From Author) TOP Backlash Compensation with Filtered Prediction in Discrete Time Nonlinear Systems by Dynamic Inversion Using Neural Networks Authors: Javier Campos, Frank L. Lewis, Rastko R. Selmic, Volume: 1, Page 3534 Paper number 8006 Abstract: A dynamics inversion compensation scheme is designed for control of nonlinear discrete-time systems with input backlash. This paper extends the dynamic inversion technique to discrete-time systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discrete-time tuning algorithm is given for the NN weights so that the backlash compensation scheme guarantees bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discrete-time adaptive control techniques, no certainty equivalence (CE) or linear-in-the-parameters (LIP) assumptions are needed. CD008006.PDF (From Author) TOP Model-Based Traffic Monitoring by Means of Neural Networks Authors: Dimitri Lefebvre, Philippe Thomas, Jean Marc Thiriet, Nadhir Messai, Abdellah El Moudni, Volume: 1, Page 3541 Paper number 1591 Abstract: This article is about traffic monitoring by the use of neural networks. Magnetic sensors are used in order to extract on-line the flow and density variables. Such variables are processed by the monitoring network in order to detect and isolate incidents that disturb the traffic. Our approach is based on a macroscopic model, and more precisely on the fundamental flow-density diagram that characterizes the traffic. An admissible region is defined in the flow-density space from this diagram. Incidents are detected when the measured data are out of the admissible region. The classification properties of neural networks are used to design the monitoring network. The proposed method is applied to the monitoring of a complex road junction in the city of Nancy in France. CD001591.PDF (From Author) TOP A k-Winners-Take-All Neural Net Authors: Bruce D. Calvert, Corneliu A. Marinov, Volume: 1, Page 3547 Paper number 1073 Abstract: An analog Hopfield type neural network is given, that identifies the K largest components of a list d of N real numbers. The list to be processed is a summand of the input currents of the neurons, and the network is started from 0. We provide easily computable restrictions on the parameters. The trajectories are shown to eventually have positive components precisely in the positions given by the K largest elements in the input list. CD001073.PDF (From Author) TOP Neuro-Fuzzy Control for DC Motor Friction Compensation Authors: Jun Oh Jang, Pyeong Gi Lee, Volume: 1, Page 3550 Paper number 82 Abstract: This paper presents an application of a neuro-fuzzy controller for compensating the effects induced by the friction in a DC motor system. The neuro-fuzzy controller is a combination of a linear controller and a neuro-fuzzy network which compensates for nonlinear friction. The proposed scheme is implemented and tested on an IBM PC-based DC motor control system. The algorithm, simulations, and experimental results are described. The results are relevant for precision drive, such those found in industrial robots. CD000082.PDF (From Author) CD000082.PDF (Scanned) TOP Constructive On-Line Learning for a Neuro-Fuzzy Network with Fuzzy Sets Obtained by Delaunay Triangulation Authors: Carlos Pereira, Antonio Dourado, Robert Babuska, Volume: 1, Page 3556 Paper number 1710 Abstract: This paper addresses the design and gradual building of a rule based neuro-fuzzy network using piecewise linear multidimensional membership functions obtained by Delaunay partition of the input space. On-line growing and pruning techniques are used to obtain a parsimonious structure. The proposed network is shown to be useful in approximating unknown nonlinearities of dynamic systems. A control framework is applied, taking advantage of the piecewise linear property of the model. For each simplex, the local inverse model can easily be calculated. The operation of this adaptive control scheme using the on-line constructive algorithm and the inverse of the local linear model is demonstrated using a simulation example and a laboratory scale process. CD001710.PDF (From Author) TOP An ANN Evolved by a New Evolutionary System and Its Application Authors: Chunkai Zhang, Huihe Shao, Volume: 1, Page 3562 Paper number 9116 Abstract: The paper describes a new evolutionary system for evolving artificial neural networks (ANN) called PSONN, which is based on the particle swarm optimisation (PSO) algorithm. The PSO algorithm is used to evolve both the architecture and weights of ANN's, this means that the network architecture is adaptively adjusted by the PSO algorithm, then this algorithm is employed again to evolve the nodes of ANNs with a given architecture. This process is repeated until the best network is accepted or the maximum number of generations has been reached. In PSONN, a new strategy of evolving nodes is used to maintain a close behavioural link between the parents and their offspring, which improves the efficiency of evolving ANNs. An ANNs evolved by PSONN has been used in modelling product quality estimator for a fractionator of the hydrocracking unit in the oil refining industry. The results show that the ANNs evolved by PSONN has good accuracy and generalisation ability. CD009116.PDF (From Author) TOP