mean square error criterion
A De Noising Method for Track State Detection Signal Based on EMD
... A De-Noising Method on EMD under Consecutive Mean Square Error Criterion The thought of a de-noising method on EMD under consecutive mean square error criterion is [14] [15], the first d[r] ...
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Identification & control of nonlinear systems
... Both recursive and direct algorithms are developed using either an unbiased or minimum mean square error criterion to obtain estimates of the Fourier transform and the power spectrum den[r] ...
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Biometric Person Identification from 3D Ear Images and a Time Efficient Ear Shape Recognition
... Chen and Bhanu proposed “Contour Matching for 3D Ear Recognition”. In this, ICP algorithm is to match a data shape to a model shape by iteratively minimizing the distance between corresponding pairs. In the training ...
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RMSE (Root Mean Square Error), respectively.
... In this research, we are interest in time series analysis with the most popular method, that is, the Box and Jenkins method [3]. The result model of this method is quite accurate compared to other methods and can be ...
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Mean square error optimal weighting for multitaper cepstrum estimation
... ‘A’ of recorded data of the Swedish word Hallå (Hallo) as well as of the whole word Hallå from the same speakers (three males and three females). The reason for analyz- ing ‘A’ is the more stationary character of vowels ...
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Study of Road Accident Prediction Model at Accident Blackspot Area: A Case Study at Selangor Haikal Aiman Hartika *1 , Mohd Zakwan Ramli 1, Muhamad Zaihafiz Zainal Abidin2 , Mohd Hafiz Zawawi 1
... the criterion of the correlation coefficient and the mean square error produced by both models, in which the results from Artificial Neural Network (ANN) shows greater correlation and lesser ...
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Orthogonal forward selection for constructing the radial basis function network with tunable nodes
... A novel construction algorithm has been proposed for RBF networks with tunable nodes. Unlike most of the sparse RBF or kernel modelling methods, the RBF centres are not restricted to the training input data points and ...
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Multiuser Detection Using Adaptive Multistage Matrix Wiener Filtering Schemes with Stage-Selection Criteria in DS-UWB
... Adaptive reduced-rank (RR) multistage matrix Wiener filtering (MMWF) techniques, based on the minimum mean-square error (MMSE) criterion, are proposed for direct-sequence (DS) ultra-wideband ...
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A Comparative Study in Predicting Colon Rectum Cancer using Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) Models
... The optimal prediction data for various ANN models were obtained by comparing with the parameter of error estimates such as Mean Absolute Error MAE, Mean Square Error MSE, Mean Absolute [r] ...
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Process improvement to the anodize line through design of experiments
... Analysis of Variance 18 ANOVA of Freedom Degrees Sum of Squares Model, Error, Total Model, Error, Total Model Mean Square Error Mean Square F Ratio Prob>F "The analysis of variance obser[r] ...
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Kinetic Analysis of the Adsorption of the Brominated Flame Retardant 4-bromodiphenyl Ether onto Biochar-immobilized Sphingomonas sp.
... using non-linear regression. The Elovich model was the poorest in fitting the curve based on visual observation followed by Pseudo-2nd order (Figs. 1-3). Statistical analysis based on root- mean-square ...
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Wavelet based Image Denoising using Weighted Highpass Filtering Coefficient and Exposure based Sub-Image Histogram Equalization Enhancement Technique
... The mean filter is a simple spatial filter. It is a sliding-window filter that replaces the center value in the window. It replaces with the common mean of all the pixel values in the kernel or window. The ...
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Efficient biased estimation and applications to linear models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University
... The best proce dure seems to be that we choose subspaces in which the components are liable to be similar in value to one another and shrink within those spaces , either to a fixed point if we have good prior knowledge , ...
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Efficiency of antlion trap construction
... each square on the grid being ...the mean height of the points on the rim of the ...least mean square adjustment on the conical surface given by the ...
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Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model
... The epidemiological literature on disease spread does not provide recommendations on the threshold propor- tion of infected individuals that needs to be detected to avoid disease propagation. Obviously, this number ...
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The Implementation of Forecasting Method by Incorporating Human Judgment
... 2) Forecasting results for the qualitative forecasting method: Moreover, for the analysis of the qualitative forecasting method, the Pure Judgment method is conducted for the whole demand data (28 periods). There is no ...
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Mean Square Error of Blind Channel Estimation Using a Constant Modulus Algorithm
... These results are justifiable in that the LS estimation assumes independent components of the frequency response which does not use the correlation properties of the channel making it sensitive to error. The ...
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Improvement of the Preliminary Test Estimator When Stochastic Restrictions are Available in Linear Regression Model
... When different estimators are available for the same parameter vector in the linear regression model one must solve the problem of their comparison. Usually as a simultaneous measure of covariance and bias, the ...
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Studies on amyloglucosidase from rhizopus delemar
... The standard error of a mean is obtained by dividing the square root of tho error variance standard deviation by the square root of the number of experimental points#... variance Is comp[r] ...
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Sentiment Analysis of Social Media Text Data using Back Propagation in Artificial Neural Networks
... It can be concluded from the mathematical background and subsequent results that the proposed system yields high accuracy for text mining data. The data used here are in the form of tweets. The training algorithm used is ...
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