Introduction To Neural Networks Using Matlab 6 0 S N Sivanandam Sumathi Deepal
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.
Introduction To Neural Networks Using Matlab 6 0 S N Sivanandam Sumathi Deepal
One of the major problems facing researchers is the selection of hidden neurons using neural networks (NN). This is very important while the neural network is trained to get very small errors which may not respond properly in wind speed prediction. There exists an overtraining issue in the design of NN training process. Over training is akin to the issue of overfitting data. The issue arises because the network matches the data so closely as to lose its generalization ability over the test data.
Thus various criteria were proposed for fixing hidden neuron by researchers during the last couple of decades. Most of researchers have fixed number of hidden neurons based on trial rule. In this paper, new method is proposed and is applied for Elman network for wind speed prediction. And the survey has been made for the fixation of hidden neuron in neural networks for the past 20 years. All proposed criteria are tested using convergence theorem which converges infinite sequences into finite sequences. The main objective is to minimize error, improve accuracy and stability of network. This review is to be useful for researchers working in this field and selects proper number of hidden neurons in neural networks.
In 2010, Doukim et al. [21] proposed a technique to find the number of hidden neurons in MLP network using coarse-to-fine search technique which is applied in skin detection. This technique includes binary search and sequential search. This implementation is trained by 30 networks and searched for lowest mean squared error. The sequential search is performed in order to find the best number of hidden neurons. Yuan et al. [22] proposed a method for estimation of hidden neuron based on information entropy. This method is based on decision tree algorithm. The goal is to avoid the overlearning problem because of exceeding numbers of the hidden neurons and to avoid the shortage of capacity because of few hidden neurons. The number of hidden neurons of feedforward neural network is generally decided on the basis of experience. In 2010, Wu and Hong [23] proposed the learning algorithms for determination of number of hidden neurons. In 2011, Panchal et al. [24] proposed a methodology to analyze the behavior of MLP. The number of hidden layers is inversely proportional to the minimal error.
In this paper, a survey has been made on the design of neural networks for fixing the number of hidden neurons. The proposed model was introduced and tested with real-time wind data. The results are compared with various statistical errors. The proposed approach aimed at implementing the selection of proper number of hidden neurons in Elman network for wind speed prediction in renewable energy systems. The better performance is also analyzed using statistical errors. The following conclusions were obtained. (1)Reviewing the methods to fix hidden neurons in neural networks for the past 20 years.(2)Selecting number of hidden neurons thus providing better framework for designing proposed Elman network.(3)Reduction of errors made by Elman network.(4)Predicting accurate wind speed in renewable energy systems.(5)Improving stability and accuracy of network. 350c69d7ab
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