scholarly journals A Criterion for the Fuzzy Set Estimation of the Regression Function

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Jesús A. Fajardo
Keyword(s):  
Convergence Rate ◽  
Mean Square Error ◽  
Fuzzy Set ◽  
Kernel Estimation ◽  
Regression Function ◽  
Optimal Scaling ◽  
Scaling Factor ◽  
Mean Square ◽  
Set Estimation ◽  
Kernel Estimations

We propose a criterion to estimate the regression function by means of a nonparametric and fuzzy set estimator of the Nadaraya-Watson type, for independent pairs of data, obtaining a reduction of the integrated mean square error of the fuzzy set estimator regarding the integrated mean square error of the classic kernel estimators. This reduction shows that the fuzzy set estimator has better performance than the kernel estimations. Also, the convergence rate of the optimal scaling factor is computed, which coincides with the convergence rate in classic kernel estimation. Finally, these theoretical findings are illustrated using a numerical example.

scholarly journals Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

2020 ◽  
Vol 30 (1) ◽  
pp. 240-257
Author(s):  
Akula Suneetha ◽  
E. Srinivasa Reddy
Keyword(s):  
Image Quality ◽  
Mean Square Error ◽  
Fuzzy Set ◽  
Digital Image ◽  
Gaussian Noise ◽  
Digital Images ◽  
Structural Similarity ◽  
Fuzzy Rules ◽  
Mean Square ◽  
Image Edges

Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

scholarly journals تقدير دالة الأنحدار اللامعلمي باستخدام بعض الطرائق اللامعلمية الرتيبة

2008 ◽  
Vol 14 (50) ◽  
pp. 304
Author(s):  
ياسمين عبد الرحمن محمد ◽  
دجلة ابراهيم مهدي
Keyword(s):  
Mean Square Error ◽  
Monotone Function ◽  
Minimum Mean Square Error ◽  
Kernel Estimator ◽  
Regression Function ◽  
Least Square ◽  
Maximum Efficiency ◽  
Mean Square ◽  
Statistical Measures ◽  
Monotone Methods

This research was concerning to study monotone nonparametric methods for estimating the nonparametric regression function (i.e treatment outlier) to achieve a monotone function (increasing or decreasing). So we will use the monotone methods to treatment outlier but after estimate the regression function with use kernel estimator (Nadarya - Watson) these methods are:- 1- Mukerjee method takes averages of maximums and minimum of subsets of the data was used to adjust the initial kernel regression estimates and use the researcher special case when . 2- Algorithm least square isotonic regression. In the experimental aspect comparison was done of which is the best methods through the simulation procedure using Mote Carlo method using five models. While in the application aspect practical application was done on data represent the measurements for blood pressure patients. In both aspects we use two of the important statistical measures which are Mean square error (MSE) and efficiency. We find through the application that the best method is Mukerjee method for general case as it has minimum Mean square error and maximum efficiency.  

New Concurrent Fractionally-Spaced Blind Equalization

2010 ◽  
Vol 108-111 ◽  
pp. 363-368 ◽  
Author(s):  
Wei Rao ◽  
Ye Cai Guo ◽  
Min Chen ◽  
Wen Qun Tan ◽  
Jian Bing Liu ◽  
...  
Keyword(s):  
Steady State ◽  
Convergence Rate ◽  
Mean Square Error ◽  
Blind Equalization ◽  
Mean Square ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Underwater Acoustic ◽  
Underwater Acoustic Channels ◽  
Simulation Results

The paper proposes a concurrent constant modulus algorithm (CMA) and decision-directed (DD) scheme for fractionally-spaced blind equalization. The proposed algorithm makes full use of the advantages of CMA and DD algorithm. A novel rule to control the adjustment of DD’s tap weights vector is proposed which avoids the hard switch between CMA and DD in practice. Simulations with underwater acoustic channels are used to compare the proposed algorithm with the famous CMA. And the simulation results show that the proposed algorithm has faster convergence rate and lower steady state mean square error.

New FSE Based on MCMA and PLL

2013 ◽  
Vol 760-762 ◽  
pp. 467-471
Author(s):  
Yan Liu ◽  
Yuan Min Li
Keyword(s):  
Computer Simulation ◽  
Steady State ◽  
Convergence Rate ◽  
Mean Square Error ◽  
Carrier Phase ◽  
Digital Transmission ◽  
Mean Square ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Phase Rotation

In the digital transmission system, an efficient fractionally-spaced equalizer (FSE) for blind equalization based on modified constant modulus algorithm (MCMA) and phase locked loop (PLL) is proposed. Comparing with conventional FSE based on famous CMA, the proposed algorithm has not only lower steady-state mean square error and faster convergence rate but also the ability to recover carrier phase rotation. The efficiency of the method is proved by computer simulation.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

2005 ◽  
Vol 10 (4) ◽  
pp. 333-342
Author(s):  
V. Chadyšas ◽  
D. Krapavickaitė
Keyword(s):  
Mean Square Error ◽  
Taylor Series ◽  
Random Sampling ◽  
Finite Population ◽  
Taylor Series Expansion ◽  
Simple Random Sampling ◽  
Population Parameter ◽  
Mean Square ◽  
Parameter Ratio ◽  
Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

Performance analysis of global local mean square error criterion of stochastic linearization for nonlinear oscillator

2019 ◽  
Author(s):  
Nguyen Cao Thang ◽  
Luu Xuan Hung
Keyword(s):  
Performance Analysis ◽  
Mean Square Error ◽  
Nonlinear Oscillators ◽  
Degree Of Freedom ◽  
Equivalent Linearization ◽  
Mean Square ◽  
Error Criterion ◽  
Stochastic Linearization ◽  
Local Mean ◽  
Mean Square Error Criterion

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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