s. danesh

In this paper, three methods of nonparametric fuzzy regression with crisp input and asymmetric trapezoidal fuzzy output, are compared. It analyzes the three nonparametric techniques in statistics, namely local linear smoothing (L-L-S), K- nearest neighbor Smoothing (K-NN) and kernel smoothing (K-S) with trapezoidal fuzzy data to obtain the best smoothing parameters. In addition, it makes an analysis on three real-world datasets and calculates the goodness of fit to illustrate the application of the proposed method.In this paper, we propose to analyze the three nonparametric regression techniques in statistical regression, namely local linear smoothing (L-L-S), the K- nearest neighbor smoothing (K-NN) and the kernel smoothing techniques (K-S) with trapezoidal fuzzy data.This article is organized as follows: In section 2, we have some preliminaries about fuzzy nonparametric regression and trapezoidal fuzzy data. In section 3, smoothing methods for trapezoidal fuzzy numbers are proposed and in section 4, two numerical examples are solved.

This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where the variation around the mean is not determinate. However, to provide unbiased and precise estimations, the predictions are required to be correct on average and the spread of date be specified. To address this issue, we proposed a method based on the fuzzy inference system, and genetic and linear programming algorithms. We consider the crisp inputs and the symmetrical triangular fuzzy output. The proposed algorithm is applied to fit the fuzzy regression model. In addition, we apply a simulation example and a practical example in the field of machining process to assess the performance of the proposed method in dealing with practical problems in which the output variables have the nature of uncertainty and impression. Finally, we compare the performance of the suggested method with other methods. Based on the examples, the proposed method is verified for prediction. The results show that the proposed method reduces the error values to a minimum level and is more accurate than the Linear Programming (LP) and fuzzy weights with linear programming (FWLP) methods.

The most common method of increasing implant stability in the posterior maxilla comprises the reinforcement of bone height using bone grafts in sinus lift surgery. The purpose of the present study was to compare autogenous and allogeneic bone grafts in implant stability after open sinus lift surgery. This split-mouth clinical trial compared the implant stability in 10 patients who needed bilateral open sinus lifts, including 8 men and 2 women. Each side of each patient's jaw was assigned to either case or control groups. Open sinus lift was performed on both sides of the jaw: autogenous bone graft was used on the side considered as the control, while allogeneic bone graft was used on the side assigned to the case group. After four months, the implant stability was evaluated and recorded in each group using the Periotest® system. The mean value related to implant stability was -2.78±2.31 in the control group and -3.19±2.51 in the case group. The values below zero (negative values) indicate an acceptable stability. According to Mann-U-Whitney test, there were no statistically significant differences between the two groups (P>0.05); however, the intragroup analysis using Wilcoxon test showed statistically significant results with regard to implant stability in each group (P<0.05). Based on the results, autogenous and allogeneic bone grafts have similar effects on implant stability after open sinus lift surgery, and both bone grafts provide a suitable implant durability.