A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation
In this paper, a locally feedback dynamic fuzzy neural network (LF-DFNN) for modeling of temporal processes is suggested. The model is composed of dynamic TSK-type fuzzy rules where the consequent sub-models are implemented by recurrent neural networks with internal feedback paths and dynamic neuron synapses. The LF-DFNN exhibits some interesting features, such as enhanced representation power, local modeling characteristics, model parsimony, and stable learning. Training of the LF-DFNN models is achieved using an optimal on-line learning scheme, the decoupled recursive prediction error algorithm (DRPE). The method has reduced computational demands and is derived through decomposition of the weight vector to several mutually exclusive weight groups. The partial derivatives required for the implementation of the training algorithm are calculated using the adjoint model approach, adapted to the fuzzy network's architecture exercised here. The paper deals with the wind speed prediction in wind farms, using spatial information from remote measurement stations. The LF-DFNN networks are used as advanced forecast models, providing multi-step ahead wind speed estimates from 15min to 3h ahead. Extensive simulation results demonstrate that our models exhibit superior performance compared to other network types suggested in the literature. Furthermore, it is shown that DRPE outperforms three gradient descent algorithms, in training of the recurrent forecast models.
local recurrent fuzzy neural networks spatial correlation dynamic neurons recursive prediction error algorithm adjoint models wind speed forecasting