The introduction of transmission system based on the Synchronous Digital Hierarchy (SDH) has created new opportunities for flexible and resilient transmission networks. This paper looks at reasons to deploy SDH in rai...
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The introduction of transmission system based on the Synchronous Digital Hierarchy (SDH) has created new opportunities for flexible and resilient transmission networks. This paper looks at reasons to deploy SDH in railway networks, and reviews some of the design considerations. The complementary technologies of WDM, ATM and flexible adaptation multiplexers are also introduced. Some of the new services which will become available once broadband capacity can be provisioned from the network to stations, control centres and the railway lineside are described.
Recent developments in the instrumentation of plants has led to multivariate statistical process control (MSPC) techniques becoming increasingly popular for process monitoring in the chemical industry over the last fe...
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Recent developments in the instrumentation of plants has led to multivariate statistical process control (MSPC) techniques becoming increasingly popular for process monitoring in the chemical industry over the last few years. This paper examines one such algorithm, Partial Least Squares (PLS), and shows how the basic principles of this linear technique can be extended into the nonlinear domain via the application of Radial Basis Function (RBF) neuralnetworks. Results showing the successful application of these methods to fault detection in a validated model of an industrial overheads condenser and reflux drum plant are also given.
Although a large number of neural architectures exist and are applied to a wide range of problems, there continues a need for fast real-time neural network classifiers, especially in the area of sensor interpretation....
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Although a large number of neural architectures exist and are applied to a wide range of problems, there continues a need for fast real-time neural network classifiers, especially in the area of sensor interpretation. This paper describes a novel neural network architecture and implementation, which has the potential to eventually lead to a system that will be able to satisfy the above needs. A modified Radial Basis Function (RBF) neural network algorithm has been presented, that uses several methods to gain a speed advantage over the original RBF algorithm. A hardware platform has also been proposed, using PIC 16V84 micro-controllers for the implementation of the algorithm. An application has also been discussed, for the above system. This was the real-time condition monitoring and control in an automotive spark-ignition engine. A neural network system as described above can also be applied to a number of other problems, where output classes are limited and response time is important.
The application of artificial neural network techniques to the diagnosis of non-catastrophic faults in the integrated dry route (IDR) process of British Nuclear Fuels are described. It was shown through simulation and...
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The application of artificial neural network techniques to the diagnosis of non-catastrophic faults in the integrated dry route (IDR) process of British Nuclear Fuels are described. It was shown through simulation and with real IDR process data that one neural network trained with data from a primary operating points are conditioned using the same data pre-processing methods. These data pre-processing methods enable the secondary operating point data to be converted as close to the primary operating point as possible. This is useful if there are not enough data available from different operating points to train separate neuralnetworks for each point.
In this paper a method for expanding the limited vocabulary of neural-network based language systems is introduced. The proposed method draws on developmental constraints observed in human language acquisition, to gen...
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In this paper a method for expanding the limited vocabulary of neural-network based language systems is introduced. The proposed method draws on developmental constraints observed in human language acquisition, to generate increasingly specialist feature maps in linked orthogonal spaces. Each space acts as a semantic filter, channelling words to more specialist spaces. The resultant trace through each space corresponds to a full feature list for the word, which can be manipulated symbolically or by another network. This approach allows arbitrary feature accuracy for any word, whilst limiting input dimensionality to the minimum required to uniquely specify the word in the relevant specialist space. Consequently crossover between unrelated words is also minimised, so avoiding the n-squared relation between computation and vocabulary size found in fully connected networks. The resultant topology of spaces also suggests that complex inferences are possible, and the use of a perception-based feature set allows a common knowledge base to be shared between languages.
Neurofuzzy modelling combines the attractive attributes of fuzzy systems and neuralnetworks, and is ideally suited to data modelling. The resulting models possess the ability to learn empirical data, and their behavi...
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Neurofuzzy modelling combines the attractive attributes of fuzzy systems and neuralnetworks, and is ideally suited to data modelling. The resulting models possess the ability to learn empirical data, and their behaviour can be described by a series of humanly understandable fuzzy rules. However, conventional neurofuzzy modelling is essentially restricted to low dimensional problems for which good quality expert knowledge and empirical data are available. This observation has motivated the development of constructive neurofuzzy modelling techniques, which iteratively identify parsimonious neurofuzzy models based on a combination of available a priori knowledge and empirical data. Bayesian inferencing techniques are adapted to perform local regularisation producing a method for successfully controlling superfluous model parameters, further improving model generalisation and data interpretation by the generation of valid models. This paper examines the merits of this approach by applying the techniques to a real world data set. The technique successfully produces an accurate transparent model and highlights inadequacies in the data.
This paper presents firstly the use of artificial neuralnetworks to classify power system faults. Examples will be used to demonstrate this approach such as faults occurring in high voltage transmission systems, or t...
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ISBN:
(纸本)0852969120
This paper presents firstly the use of artificial neuralnetworks to classify power system faults. Examples will be used to demonstrate this approach such as faults occurring in high voltage transmission systems, or those stored with a digital recorder. The paper proposes an adaptive scheme employing the neural network for developing digital distance relay. High impedance faults and variable source impedance will also be considered. An example based on a three-terminal line configuration will be used to illustrate the effectiveness of the method. Secondly, a discussion on the future use of NN in protection will be given. In conclusion, neuralnetworks should be integrated with different computational techniques to enhance its application to fault classification and protection.
The importance of models of power systems has long been recognized A set of accurate models can be obtained through field tests by means of modern identification methods. In this paper a new way to establish power sys...
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ISBN:
(纸本)0852969120
The importance of models of power systems has long been recognized A set of accurate models can be obtained through field tests by means of modern identification methods. In this paper a new way to establish power system models with the artificial neuralnetworks (ANN) is presented. Both power generator using fast backpropagation neuralnetworks(FBP) and excitation system model using. radial basis function network(RBFN) are developed The simulation results of field and laboratory tests demonstrate that the application of developed ANN approach to power generator and excitation system modeling with fast training procedure and high precision is promising.
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.
This paper presents the development of an intelligent active noise control framework using neuralnetworks. An active control system is designed utilising a feedforward control structure for optimum cancellation of br...
This paper presents the development of an intelligent active noise control framework using neuralnetworks. An active control system is designed utilising a feedforward control structure for optimum cancellation of broadband noise. The controller design relations are formulated such that to allow online design and implementation and, thus, yield a self-tuning control strategy. neuralnetworks are used at the modelling and control contexts and thus incorporated into the control strategy. Two alternative neuro-adaptive active control algorithms are proposed on the basis of this approach. The algorithms thus developed are tested and verified in the cancellation of broadband noise in free-field.
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