Prediction of Net Bandwidth using Artificial neural Network
ثبت نشده
چکیده
Multi step prediction is a complex task that has attracted increasing interest in recent years. The contribution in this work is the development of nonlinear neural network models for the purpose of building multi step Prediction of Internet Bandwidth i.e. bits per second transmission record of server. It is observed that such problems exhibit a rich chaotic behavior and also leads to strange attractor. . This paper compares the performance of four neural network configurations namely a Multilayer Perceptron (MLP) , generalized feed forward network(GFF) , Self organized feature map (SOFM), and the Jorden –Elmen network with regards to various performance measures Mean square error (M.S.E.),Normalized mean square error (N.M.S.E) and regression (r) . The standard back propagation algorithm with momentum term has been used for all the models. There are various parameters like number of processing elements, step size, momentum value in hidden layer, in output layer the various transfer functions like tanh, sigmoid, linear-tan-h and linear sigmoid, different error norms L1,L2 ,Lp to L infinity, Epochs variations and different combination of training and testing samples are exhaustively experimented for obtaining the proposed robust model for long term as well as short step ahead prediction.
منابع مشابه
Surface Tension Prediction of Hydrocarbon Mixtures Using Artificial Neural Network
In this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. Experimental data was divided into two parts (70% for training and 30% for testing). Optimal configuration of the network was obtained with minimization of prediction error on testing data. The accuracy of our proposed model was compared with four well-known empirical equations. The arti...
متن کاملStream Flow Prediction in Flood Plain by Using Artificial Neural Network (Case Study: Sepidroud Watershed)
In order to determine hydrological behavior and water management of Sepidroud River (North of Iran-Guilan) the present study has focused on stream flow prediction by using artificial neural network. Ten years observed inflow data (2000-2009) of Sepidroud River were selected; then these data have been forecasted by using neural network. Finally, predicted results are compared to the observed dat...
متن کاملSolubility Prediction of Drugs in Supercritical Carbon Dioxide Using Artificial Neural Network
The descriptors computed by HyperChem® software were employed to represent the solubility of 40 drug molecules in supercritical carbon dioxide using an artificial neural network with the architecture of 15-4-1. The accuracy of the proposed method was evaluated by computing average of absolute error (AE) of calculated and experimental logarithm of solubilities. The AE (±SD) of data sets was 0.4 ...
متن کاملEvaluation of Ultimate Torsional Strength of Reinforcement Concrete Beams Using Finite Element Analysis and Artificial Neural Network
Due to lack of theory of elasticity, estimation of ultimate torsional strength of reinforcement concrete beams is a difficult task. Therefore, the finite element methods could be applied for determination of strength of concrete beams. Furthermore, for complicated, highly nonlinear and ambiguous status, artificial neural networks are appropriate tools for prediction of behavior of such states. ...
متن کاملPrediction of Entrance Length for Magnetohydrodynamics Channels Flow using Numerical simulation and Artificial Neural Network
This paper focuses on using the numerical finite volume method (FVM) and artificial neural network (ANN) in order to propose a correlation for computing the entrance length of laminar magnetohydrodynamics (MHD) channels flow. In the first step, for different values of the Reynolds (Re) and Hartmann (Ha) numbers (600<ReL increases.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013