In this paper, an Artificial Neural Network is used for the prediction of pole slips of a synchronous generator, which is in the process of resynchronisaton after a fault. The measurements considered are the rotor spe...
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In this paper, an Artificial Neural Network is used for the prediction of pole slips of a synchronous generator, which is in the process of resynchronisaton after a fault. The measurements considered are the rotor speed and angle, at the moment the circuit breaker recloses. Given these in-puts, the Artificial Neural Network suggested is capable of predictfng whether the generator will resynchronise or it will skip a pole. This prediction is very fast, so that it could be used in real-time *** method proposed has been tested with a simulation program based on a synchronous generator model with seven state variables, including also the saturation in the d-axis.
The aim of this paper was to explore the usefulness of a backpropagation neural network (BNN) to estimate the biodegradability of benzene derivatives. 127 chemicals selected from the BIODEG data bank (Syracuse Researc...
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In this paper, we propose simple but powerful methods for fuzzy regression analysis using neural networks. Since neural networks have high capability as an approximator of nonlinear mappings, the proposed methods can ...
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In this paper, we propose simple but powerful methods for fuzzy regression analysis using neural networks. Since neural networks have high capability as an approximator of nonlinear mappings, the proposed methods can be applied to more complex systems than the existing LP based methods. First we propose learning algorithms of neural networks for determining a nonlinear interval model from the given input-output patterns. A nonlinear interval model whose outputs approximately include all the given patterns can be determined by two neural networks. Next we show two methods for deriving nonlinear fuzzy models from the interval model determined by the proposed algorithms. Nonlinear fuzzy models whose h-level sets approximately include all the given patterns can be derived. Last we show an application of the proposed methods to a real problem.
We propose two learning algorithms of neural networks for two-group discriminant problems from the view point of possibility and necessity. One algorithm corresponds to the possibility analysis and the other to the ne...
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We propose two learning algorithms of neural networks for two-group discriminant problems from the view point of possibility and necessity. One algorithm corresponds to the possibility analysis and the other to the necessity analysis. The proposed algorithms are similar to the back-propagation algorithm and the difference stems from a formulation of a cost function to be minimized in each algorithm. Each cost function of the proposed algorithms is the weighted sum of squared errors, that is, the sum of squared errors with different penalties. When we discuss the possibility of Group 1, the penalty for the squared errors relating to the patterns in Group 1 is greater than that of Group 2. This means that, in the possibility analysis of Group 1, we attach greater importance to the patterns in Group 1 than to those in Group 2. On the other hand, when we discuss the necessity of Group 1, the penalty for the squared errors relating to the patterns in Group 2 is greater than that of Group 1.
This paper describes an experimental study of pattern recognition of partial discharge (PD) in a crosslinked polyethylene (XLPE) cable by using a neural network (NN) system. The NN system was a three-layer artificial ...
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This paper describes an experimental study of pattern recognition of partial discharge (PD) in a crosslinked polyethylene (XLPE) cable by using a neural network (NN) system. The NN system was a three-layer artificial neural system with feedforward connections, and its learning method was a back-propagation algorithm incorporating an external teacher signal. Input information for the NN was a combination of the discharge magnitude, the number of pulse counts and the phase angle of applied voltage in which PD is produced. PD measurement was carried out using a PD pulse recorder for a 66 kV XLPE cable with an artificial defect under a 38 kV ac applied voltage. After learning 30 typical input patterns, the NN discriminated unknown patterns with 90% correct responses. The time duration including measuring time required for the NN to discriminate PD signal was approximately 30 s. In a long-term performance test of a 66 kV XLPE cable with an artificial defect, the NN-based alarm processor was able to recognize the presence of PD 1 h before breakdown of the cable, and successfully alerted the operator.
We describe a neural network based learning control scheme for the motion control of autonomous underwater vehicles (AUV). The described scheme has a number of advantages over the classical control schemes and convent...
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We describe a neural network based learning control scheme for the motion control of autonomous underwater vehicles (AUV). The described scheme has a number of advantages over the classical control schemes and conventional adaptive control techniques. The dynamics of the controlled vehicle need not be fully known. The controller with the aid of a gain layer learns the dynamics and adapts fast to give the correct control action. The dynamic response and tracking performance could be accurately controlled by adjusting the network learning rate. A modified direct control scheme using multilayered neural network architecture is used in the studies with backpropagation as the learning algorithm. Results of simulation studies using nonlinear AUV dynamics is described in detail. Also, the robustness of the control system to sudden and slow varying disturbances in the dynamics is studied and the results are presented.
Artificial Neural Network (ANN) Method is applied to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern include Saturda...
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Artificial Neural Network (ANN) Method is applied to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern include Saturday, Sunday, and Monday loads. A nonlinear load model is proposed and several structures of ANN for short-term load forecasting are tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers are tested with various combination of neurons, and results are compared in terms of forecasting error. The neural network, when grouped into different load patterns, gives good load forecast.
This paper shows a certain equivalence between the 3-layer feed forward back-propagation neural net of Rumelhart et al. and the committee net of Nilsson. This is used to produce an improvement in the performance of th...
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This paper shows a certain equivalence between the 3-layer feed forward back-propagation neural net of Rumelhart et al. and the committee net of Nilsson. This is used to produce an improvement in the performance of the former. It is found that (a) the number of epochs taken is reduced by a factor of between 6 and 10, (b) the time taken is reduced by a factor of about 20, and (c) the net converges under conditions when the back-propagation algorithm is trapped in local minima and fails to converge.
This paper presents a methodology for real-time fault diagnosis of manufacturing systems using occupancy grids and neural network techniques. The main advantages of the system over other existing methods are its abili...
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A fast learning rule for artificial neural systems which is based on modifications to a backpropagation algorithm is described. The rule minimises the error function along the direction of the gradient and backpropaga...
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A fast learning rule for artificial neural systems which is based on modifications to a backpropagation algorithm is described. The rule minimises the error function along the direction of the gradient and backpropagates the error pattern according to a constant error energy approach.
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