Recently, there has been considerable interest in the use of artificial neuralnetworks for system identification and control. In this paper the authors discuss some constraints faced by using the Model-I and Model-II...
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Recently, there has been considerable interest in the use of artificial neuralnetworks 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 neuralnetworks, vol.1, no.1, p.4-27 (1990)) for nonlinear system identification.< >
Artificial neuralnetworks attempt to model the massively parallel structure and the learning capability offered by biological neuralsystems. They have been shown to offer significant advantages over conventional pro...
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Artificial neuralnetworks attempt to model the massively parallel structure and the learning capability offered by biological neuralsystems. 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 neuralsystems.< >
The paper presents a practical artificial neural network (ANN) based relay algorithm for electric distribution high impedance fault detection. The scheme utilizes the characteristics of high impedance faults (HIFs) in...
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The paper presents a practical artificial neural network (ANN) based relay algorithm for electric distribution high impedance fault detection. The scheme utilizes the characteristics of high impedance faults (HIFs) in the resulting waveforms of the three phase residual current, voltage, admittance and power. By using Fourier analysis, their low order harmonic vectors were worked out which were then fed to a neural network. The network was based on either perceptron or feed forward algorithm. The trained network was verified using other distribution systems.
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 proc...
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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.< >
Spacecraft attitude control is conventionally achieved by the use of reaction wheel or thruster based control schemes. The authors investigate the use of a neural network controller for a thruster based spacecraft att...
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Spacecraft attitude control is conventionally achieved by the use of reaction wheel or thruster based control schemes. The authors investigate the use of a neural network controller for a thruster based spacecraft attitude control system. They propose to train the neural network using a genetic algorithm.< >
Traditionally, power system control and management functions have been performed in centralised locations, with unprocessed data being collected from several measuring points throughout the power system and returned t...
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Traditionally, power system control and management functions have been performed in centralised locations, with unprocessed data being collected from several measuring points throughout the power system and returned to the central point for analysis. Whilst many technology advances have been made in schemes such as this, including the introduction of expert systems, neuralnetworks, and parallel computing methods at power system control centres, the potential now exists to consider distribution centres, the potential now exists to consider distributing this information technology throughout the power system by realising the concept of intelligent substations. This paper will explore the possibilities for a distributed control and management system, with major transmission substations performing tasks such as alarm processing, fault diagnosis and conditions monitoring on a local basis;that is, accepting data from local and adjacent substations, processing this data, and sending concise and summarised messages to the control centre (with the potential for localised executive action in some instances). The advantages of such an arrangement will be demonstrated, including: reduction in SCADA system load, especially during critical periods;increased local autonomy, thus facilitating substation automation;faster response times due to the distributed nature of the processing throughout the power system. Examples of ongoing research into the realisation of such a system will be given, showing test results from package already developed, using data provided by utilities and manufactures. Problems with the implementation of such systems will also be covered, and ideas for the future solutions to these problems will be suggested.
The application of neural network methods is demonstrated for the analysis of data obtained in a study of a hypertext system employed in computer assisted learning (CAL). The authors also discuss the future role of ne...
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The application of neural network methods is demonstrated for the analysis of data obtained in a study of a hypertext system employed in computer assisted learning (CAL). The authors also discuss the future role of neuralnetworks in the dynamic control of such systems.< >
Non-linear techniques have been developed for the purpose of filtering navigational data and control of ships. Recently, research at the Institute of Marine Studies into ship control employed artificial intelligence i...
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Non-linear techniques have been developed for the purpose of filtering navigational data and control of ships. Recently, research at the Institute of Marine Studies into ship control employed artificial intelligence in the form of neuralnetworks. Mathematical and scale models of the controlsystems are developed to provide an intelligent autopilot ship control capable of emulating the human operator.
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...
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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 neuralnetworks, 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.< >
The paper describes the forward-backward module: a simple building block that allows the evolution of neuralnetworks with intrinsic supervised learning ability. This expands the range of networks that can be efficien...
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The paper describes the forward-backward module: a simple building block that allows the evolution of neuralnetworks with intrinsic supervised learning ability. This expands the range of networks that can be efficiently evolved compared to previous approaches, and also enables the networks to be invertible i.e. once a network has been evolved for a given problem domain, and trained on a particular dataset, the network can then be run backwards to observe what kind of mapping has been learned, or for use in control problems. A demonstration is given of the kind of self training networks that could be evolved.
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