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Bottom profiling control of an UUV using neural networks

Bottom profiling control of an UUV using neural networks

引用

iee colloquium on control and Guidance of Underwater Vehicles

作者： C. Johnson R. Sutton G.N. Roberts Marine Dynamics Research Group University of Plymouth Plymouth Devon UK

Artificial neural networks offer an alternative strategy for the nonlinear control of unmanned underwater vehicles (UUVs). This paper investigates the use of a multi-layered perceptron (MLP) network in controlling an UUV over a sea-bed profile and compares the use of applying chemotaxis learning over that of the more commonly employed backpropagation algorithm. The results show for differing sized MLPs the chemotaxis algorithm produces a successful controller over the sea bed profile in an improved training time. To further vindicate the chemotaxis network, it was then presented with the problem of meeting a new profile to travel over. The results show from several simulation runs that the chemotaxis network provides a robust controller over numerous sea bed profiles of which it had no prior knowledge.< >

关键词： Feedforward neural networks Mobile robots Nonlinear systems

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The simulation study of robust fault detection approaches on a hydraulic test rig

The simulation study of robust fault detection approaches on...

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iee colloquium on Fault Diagnosis and control System Reconfiguration

作者： D. Yu D.N. Shields J.L. Mahtani Control and Applications Centre Coventry University Coventry UK

Describes the results of a simulation study of two fault detection approaches applied to a hydraulic test rig: an eigenstructure assignment approach; and a neural network based approach. The rig has a predominant natural frequency of the order of 20 Hz and is more representative of real industrial systems due to the presence of operating point nonlinearity, measurement noise and load disturbances.< >

关键词： Eigenvalues and eigenfunctions Fault location Hydraulic equipment neural networks Test equipment

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Classifier systems for control

Classifier systems for control

引用

iee colloquium on Genetic Algorithms for control systems Engineering

作者： T.C. Fogarty University of West of England Bristol UK

Classifier systems lie midway between neural networks and symbolic processing systems and potentially combine the benefits of both. They are parallel message-passing rule-based systems which use genetic algorithms to discover new rules as well as providing for reinforcement learning and programming. It has been proposed that a suitable application of genetic algorithms is to evolve robots. A most suitable way to use genetic algorithms to evolve the control systems for robots is within the framework provided by classifier systems. At a SERC workshop on learning systems a number of groups presented successful applications of the genetic algorithm to control problems. However, one cannot evolve complex systems with a simple genetic algorithm nor is it wise or safe to start from scratch in real applications where programmed knowledge can provide constraints for the genetic algorithm to work within. If the genetic algorithm is to be used to evolve control systems for industrial or commercial applications one of the best ways to do this is within the framework of classifier systems.< >

关键词： Genetic algorithms Knowledge based systems Pattern recognition Robots

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Fuzzy logic-based and neural network-based reasoning with application to blood pressure management

Fuzzy logic-based and neural network-based reasoning with ap...

引用

iee colloquium on Intelligent Decision Support systems and Medicine

作者： D.A. Linkens Junhong Nie Department of Automatic Control & Systems Engineering University of Sheffield UK

Two distinctive approaches to the implementation of approximate reasoning in rule-based fuzzy decision-making and control systems are presented. While the first approach, based on possibility theory, bears some resemblance to a traditional event-driven inference system with the incorporation of fuzzy concepts, the second scheme, based on the technique of neural networks, represents a substantial departure from the traditional one and shows some promise in dealing with these fundamental issues. To demonstrate the applicability of the proposed approaches, a problem of multivariable fuzzy management of blood pressure has been studied using the simulation method.< >

关键词： Blood Decision support systems Fuzzy logic Blood flow Inference mechanisms Biomedical computing neural networks

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The feasibility of using MLP neural networks for system identification

The feasibility of using MLP neural networks for system iden...

引用

iee colloquium on neural networks for systems: Principles and Applications

作者： J.R. Chen P. Mars School of Engineering and Applied Science University of Durham UK

Recently, there has been considerable interest in the use of artificial neural networks for system identification and control. In this paper the authors discuss some constraints faced by using the Model-I and Model-II neural network systems introduced in work by K.S. Narendra and K. Parthasarathy (see ieeE Tranc. on neural networks, vol.1, no.1, p.4-27 (1990)) for nonlinear system identification.< >

关键词： Identification neural networks Nonlinear systems

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neural networks: problems and opportunities!

Neural networks: problems and opportunities!

引用

iee colloquium on neural networks: Design Techniques and Tools

作者： P.D. Noakes A.D.P. Green Neural Research Laboratory Department of Electronics Systems Engineering University of Essex Colchester UK

Artificial neural networks attempt to model the massively parallel structure and the learning capability offered by biological neural systems. They have been shown to offer significant advantages over conventional processing techniques in certain recognition and control applications. However there are problems in selecting an appropriate neuron model and network configuration. The paper discusses these problems and identifies the need to establish a design methodology for neural systems.< >

关键词： neural networks

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Connectionist representations and control structures

Connectionist representations and control structures

引用

iee colloquium on neural networks for systems: Principles and Applications

作者： D. Sbarbaro K.J. Hunt P.J. Gawthrop Dept. of Mech. Eng. Glasgow Univ. UK Department of Mechanical Engineering University of Glasgow Glasgow UK

Focuses on the use of adaptive connectionist networks for identification and control of nonlinear dynamic systems. control problems are usually solved using information about the controlled system and a series of procedures to reach the desired control goal. From this point of view it is possible to decompose the control problem into two sub-problems: (1) a representation problem dealing with the way in which the information and dynamics of the system will be represented; and (2) a structural problem which concerns the definition of the architecture and the procedures which use the information on the system in order to reach the desired control goal. The connectionist approach has great potential for control applications since it addresses both problems, providing system representations and control procedures. The authors outline their work in both these areas.< >

关键词： Adaptive control Identification neural networks Nonlinear systems

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Minimum temperature prediction in agricultural area using artificial neural networks

Minimum temperature prediction in agricultural area using ar...

引用

iee colloquium on neural networks for systems: Principles and Applications

作者： N. Mort C.L. Chia Department of Control Engineering University of Sheffield UK

The motivation for this study is derived from the potential problem of frost damage to citrus trees in the region surrounding Catania on the Island of Sicily Italy. The frost prediction technique developed during the investigation is based on the application of a parallel and distributed processing methodology to solve a time series weather forecasting problem. Past meteorological data observed and recorded in the region around Catania is used to train a neural network so that, given readings for a particular day, a prediction on whether frost formation is imminent can be made.< >

关键词： Agriculture Ice neural networks Weather forecasting

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Autonomous vehicle identification, control and piloting through a new class of associative memory neural networks

Autonomous vehicle identification, control and piloting thro...

引用

iee colloquium on neural networks for systems: Principles and Applications

作者： C.J. Harris M. Brown Southampton Univ. UK

Addresses the use of a class of neural nets for the intelligent motion control and piloting of a variety of autonomous vehicles as part of an ESPRIT II mobile robotics project. Intelligent controllers are necessary in order to cope with the vehicle complexities, internal parametric changes, safety imposed dynamic constraints as well as the effects of a dynamic environment. Single-layer, associative memory neural networks, the modified Albus CMAC and B-splines, are proposed as the basis for an intelligent piloting system. These algorithms have an initially exponential convergence rate, are temporally stable (unlike the multilayer perceptron), noise resilient and exhibit known generalisation (interpolation) characteristics. Two alternative control architectures are presented and parallels are drawn with the more common fuzzy logic, radial basis functions and Kanerva's sparse distributed memory model.< >

关键词： Artificial intelligence Associative memories Identification Mobile robots neural networks Spline functions

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neural control using a hierarchically performed BP algorithm

Neural control using a hierarchically performed BP algorithm

引用

iee colloquium on neural networks for systems: Principles and Applications

作者： Q.H. Wu G.W. Irwin B.W. Hogg Queen''s University Belfast UK

Deals with the design of a neural network regulator (NNR) for nonlinear industrial dynamic systems, based on the multilayer perceptron (MLP) and using a hierarchically performed backpropagation (BP) algorithm. A novel network architecture is employed for regulator design: the NNR consists of two subnetworks, one of which is used for I-O (input-output) mapping, while the other acts as an adaptive controller. The BP algorithm is employed to reproduce a nonlinear relation between the inputs and outputs of the plant and to update regulator parameters. The proposed architecture has the flexibility for adding more sensory information and facilitates extension to multi-input, multi-output systems and multivariable controllers. The operation of the NNR does not require a reference model or inverse system model, or any probing signals, and can produce more acceptable control signals than are obtained using the sign of the plant errors during the backpropagation procedure. The regulator has been applied to a complex nonlinear turbogenerator system.< >

关键词： Adaptive control Digital control Industrial control Multivariable systems neural networks Nonlinear systems

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