Title :
Wind profile prediction using a Meta-cognitive Fully Complex-valued neural network
Author :
Sathish, E. ; Sivachitra, M. ; Savitha, Ramasamy ; Vijayachitra, S.
Author_Institution :
Dept. of EIE, Kongu Eng. Coll., Perundurai, India
Abstract :
This paper applies the recently developed Meta-cognitive Fully Complex-valued Radial Basis Function (Mc-FCRBF) network for predicting the speed and direction of wind. Mc-FCRBF network contains two components: a cognitive component and a meta-cognitive component. A Fully Complex-valued Radial Basis Function (FC-RBF) network is the cognitive component and a self-regulatory learning mechanism is its meta-cognitive component. In each epoch of the training, when the sample is presented to the Mc-FCRBF network, the meta-cognitive component decides what to learn, when to learn, and how to learn based on the knowledge acquired by the FC-RBF network and the new information contained in the sample. Performance comparison of the meta-cognitive fully complex-valued RBF network (Mc-FCRBF) applied for wind speed prediction shows better prediction of wind profile (Speed) characteristics when compared to a real-valued extreme learning machine and FC-RBF network.
Keywords :
geophysics computing; learning (artificial intelligence); radial basis function networks; wind power; FC-RBF network; Mc-FCRBF network; fully complex-valued radial basis function; meta-cognitive component; meta-cognitive fully complex-valued neural network; self-regulatory learning mechanism; wind direction prediction; wind profile prediction; wind speed prediction; Neurons; Radial basis function networks; Testing; Training data; Wind forecasting; Wind speed; Extreme Learning machine; FC-RBF and Mc-FCRBF; Neural Networks; Wind Profile prediction; Wind speed prediction;
Conference_Titel :
Advanced Computing (ICoAC), 2012 Fourth International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4673-5583-4
DOI :
10.1109/ICoAC.2012.6416850
