artificial neural network applications for power system protection

... USING ARTIFICIAL NEURAL NETWORK In this paper, we use Multi Layer Perceptron (MLP) Neural Network with back propagation learning algorithm. A. Multi layer Perceptron (MLP) Neural Network ... Classification Using Artificial Neural Networks [10] 73.3% Facial Expression Classification Using Multi Artificial Neural Network [11] 83.0% Proposal System (Canny_PCA_ANN) ... with Rapid Facial Expression Classification Using Artificial Neural Network [10], Facial Expression Classification Using Multi Artificial Neural Network [11] in the same JAFFE database. TABLE...

... computational performance and application domain for various neural network architectures. 4.2 Artificial Neural Networks Artificial neural networks have emerged in recent years as a major means for ... LLC 4 Neural Network Applications for Group Technology and Cellular Manufacturing 4.1 Introduction 4.2 Artificial Neural Networks 4.3 A Taxonomy of Neural Network Application for GT/CM ... neural networks (ANNs) for pattern recognition, researchers first began to apply neural networks for group technology (GT) applications in the late 1980s and early 1990s. After a decade of effort,...

... processing information in a parallel distributed fashion. Feedforward neural networks and recur- rent neural networks are two major classes of artificial neural networks. Feedforward neural networks, ... of neural networks included ART networks, Hopfield networks, and SOM neural networks. Weaknesses of neural networks for modeling and design of manufacturing systems result from neural networks ... feasibility of neural networks for a high-reliability and low-cost quality assurance system for wire bonding process control. Joseph and Hanratty [97] presented a multilayer feedforward neural network for...

... control performance. In carrying out these tasks the networks are embedded into the system in several forms. The first form is to use the networks as an aid to the conventional controller. For example, ... approaches, the LVQ neural network classifier [Kim and Cho, 1995] is one example of such neural network applications for solder joint inspection. A three-color tiered illumination system to acquire ... needed for the feedback control of the process. Figure 12.11 shows a neural network based control system developed for this purpose [Azouzi and Guillot, 1996]. Two network models are used here for...

... Multilayer feedfor- ward networks are universal approximators. Neural Networks 2, 359–366. Jain, A., Zongker, D., 1997. Feature selection: evaluation, application and small sample performance. IEEE ... the per- formance function of regularized mean squared error (RMSE), hyperbolic sigmoid tangent for the hidden layers and linear for the output layer. The performance assessment was performed in ... very important features in practical applications. 4. Conclusions In this paper a genetic algorithm was tested for designing the multi-layer perceptron model for forecast- ing urban air pollution....

... many Neural Networks together, so we call it Multi Artificial Neural Network (MANN). 3 Multi Artificial Neural Network apply for image classification 3.1 The proposal MANN model Multi Artificial ... at present is to use Artificial Neural Network for the pattern classification. Artificial Neural Network will be trained with the patterns to find the weight collection for the classification ... Artificial Neural Network (MANN), applying for pattern or image classification with parameters (m, L), has m Sub -Neural Network (SNN) and a global frame (GF) consisting L Component Neural Network...

... we describe the development and use of an artificial neural network architecture for recognizing handwritten digit data. The feed-forward neural network, which was implemented in Java, was ... Specifically, for the study described in this paper, we focused on the use of neural network learning techniques for handwritten digit recognition. Our objective was two-fold: to test the neural network ... variety of network parameters and sizes, and to determine the best network structure and settings for the handwritten digit data set. The results provide evidence for the use of neural network...

... your test set, for example, varying it between 0.0 and 1.0. Neural Network Classes The neural network is composed from the following classes: ANNetwork ANNLayer ANeuron ANLink The ANNetwork class ... negative classes, and train it for 1000 epochs. The neural network file format is described in my Face Detection article. To start with random initialized weights before the training session, you ... application for backprop training are optional. You may use them for validation and testing of your network, for input data normalization, and error limits during training process. >ann1dn t network. nn...

... the model. NetworkHelper training data elements. NeuralNetwork A generic neural network. This is a concrete implementation of INeuralNetwork NeuralNetworkCollection A collection of neural networks Neuron ... understand. 5.4. A Neural Network In BrainNet library Now, let us see how the Neural Network is implemented. Any concrete neural network should implement the INeuralNetwork interface. INeuralNetwork interface ... 4-4-2 neural network with 4 neurons in input layer, 4 neurons in hidden layer and 2 neurons in output layer. An artificial neural network can learn from a set of samples. For training a neural network, ...

... backpropagation to recur- rent and higher order neural networks, Proceedings of the IEEE Conference on Neural Networks and Information Processing Systems, Denver, Colo Rack PMH, Westbury DR (1969) ... addressed by the artificial intelligence research. Our future goals include building a neural- network archi- tecture capable of providing a uniform representa- tional framework for environment ... on Artificial Intelligence, Brighton, pp 507-517 Miyamoto H, Kawato M, Setoyama T, Suzuki R (1988) Feed- back error learning neural network for trajectory control of robotic manipulator. Neural...

... 37 Informations internally represented in the brain are shown in ovals. Possible algorithms are shown in parentheses. Fig. 2 Three ill-posed problems in sensory-motor control. Fig. 3 A repetitive neural network model learns and minimizes energy for generation of torque waveforms which realize minimum torque-change arm trajectory. Fig. 4 Two schemes for learning inverse dynamics model of a controlled object. $a$ . direct inverse modeling. $b$ . feedback error learning scheme. Fig. 5 A feedback error learning neural network model. The inverse dynamics model is acquired in the three layer neural network. 26 Neural Network Models for Formation and Control of Multi-joint Arm Trajectory 川 人 光男 Mitsuo Kawato ATR Auditory and Visual Perception Research Laboratories, Twin 21 Bldg. MID Tower, Shiromi $2- ... \min$ learning. Moreover, the effect of leaming for faster and quite different movement pattem from the training pattem was marked, that is the network has great capability of learning generalization. Regarding the second possibility, we [12] succeeded in learning control of the robotic manipulator by an inverse-dynamics model made of a three-layer neural network (Fig. 5). $\frac{A^{u}}{*,4}$ Introductio n 2 $\gamma[$ A computational model for voluntary movement is proposed (Fig. 1) which accounts for Marr’s [15] first level for understanding complex ... 37 Informations internally represented in the brain are shown in ovals. Possible algorithms are shown in parentheses. Fig. 2 Three ill-posed problems in sensory-motor control. Fig. 3 A repetitive neural network model learns and minimizes energy for generation of torque waveforms which realize minimum torque-change arm trajectory. Fig. 4 Two schemes for learning inverse dynamics model of a controlled object. $a$ . direct inverse modeling. $b$ . feedback error learning scheme. Fig. 5 A feedback error learning neural network model. The inverse dynamics model is acquired in the three layer neural network. ...

... the model to be able to cope with this force and the performance as for the reach- ing errors have been evaluated. Results and Discussion The proposed neural system is able to achieve a complete coverage ... hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling ... with no force. In figure 20 it is possible to observe the behaviour of the system in the force field and after the short re-learning phase in the new environment. Conclusion A neural- network...

... Background ã Electric power is essential to our life and the economy ã Power systems are getting more and more sophisticated and smarter ã Teleprotection plays a key role ã Telecommunication and network ... role ã Telecommunication and network technology have changed our life ã Communication network is more and more important for electric power ã Next big wave DNP3 vs. IEC 60870-5 ã Both are used world-wide, ... - Negative sequence voltage 50 - Instantaneous overcurrent (N for neutral, G for ground current) 51 - Inverse Time overcurrent (N for neutral, G from ground current) 59 - Over Voltage (59LL...