Improved Methods of BP Neural Network Algorithm and its Limitation
IFITA '10 Proceedings of the 2010 International Forum on Information Technology and Applications - Volume 01
Self-organizing multilayer perceptron
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A simple method to derive bounds on the size and to train multilayer neural networks
IEEE Transactions on Neural Networks
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This paper proposes a Neural Network based hybrid computing model for wind speed prediction in renewable energy systems. Wind energy is one of the renewable energy sources which lower the cost of electricity production. Due to the fluctuation and nonlinearity of wind, the accurate wind speed prediction plays a major role in renewable energy systems. To increase the accuracy of wind speed prediction, a hybrid computing model is proposed. The proposed model is tested on real time wind data. The objective is to predict accurate wind speed based on proposed hybrid computing model which integrates Self Organizing feature Maps and Multilayer Perceptron network. The key advantages include higher accuracy, precision and minimal error. The results are computed by the training and testing methodologies. The experimental result shows that as compared to the conventional neural network models, the proposed hybrid model performs better in terms of minimization of errors.