Author
K. Gnana Sheela
Bio: K. Gnana Sheela is an academic researcher. The author has contributed to research in topics: Overfitting & Artificial neural network. The author has an hindex of 1, co-authored 1 publications receiving 561 citations.
Topics: Overfitting, Artificial neural network
Papers
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TL;DR: The experimental results show that with minimum errors the proposed approach can be used for wind speed prediction in renewable energy systems and the perfect design of the neural network based on the selection criteria is substantiated using convergence theorem.
Abstract: This paper reviews methods to fix a number of hidden neurons in neural networks for the past 20 years. And it also proposes a new method to fix the hidden neurons in Elman networks for wind speed prediction in renewable energy systems. The random selection of a number of hidden neurons might cause either overfitting or underfitting problems. This paper proposes the solution of these problems. To fix hidden neurons, 101 various criteria are tested based on the statistical errors. The results show that proposed model improves the accuracy and minimal error. The perfect design of the neural network based on the selection criteria is substantiated using convergence theorem. To verify the effectiveness of the model, simulations were conducted on real-time wind data. The experimental results show that with minimum errors the proposed approach can be used for wind speed prediction. The survey has been made for the fixation of hidden neurons in neural networks. The proposed model is simple, with minimal error, and efficient for fixation of hidden neurons in Elman networks.
748 citations
Cited by
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TL;DR: It is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined, and the neural network has the advantage in the case of large noise and complex models but only with many training data sets.
347 citations
TL;DR: The results showed that customization and customer involvement are the strongest antecedents of the intention to use m-commerce, which will be useful for m- commerce providers in formulating optimal marketing strategies to attract new consumers.
310 citations
TL;DR: A new research model used for the prediction of the most significant factors influencing the decision to use m-payment found that the mostificant variables impacting the intention to use were perceived usefulness and perceived security variables.
245 citations
TL;DR: In this article, an artificial neural network (ANN) and Kalman filter (KF) were used to handle nonlinearity and uncertainty problems in wind speed forecasting in order to improve the accuracy of wind power generation.
233 citations
TL;DR: This paper presents a survey on the application of recurrent neural networks to the task of statistical language modeling, and gives an overview of the most important extensions.
219 citations