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- E Pasha, R Farnoosh, A Fatemi
- 2006

In this paper a new cost function is introduced by using the fuzzy entropy to choose a threshold value in image denoising problem. The results are explained with pilot this cost function on the some images.

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will… (More)

We propose modeling a nearly regular point pattern by a generalized Neyman-Scott process in which the offspring are Gaussian perturbations from a regular mean configuration. The mean configuration of interest is an equilateral grid, but our results can be used for any stationary regular grid. The case of uniformly distributed points is first studied as a… (More)

- Rahman Farnoosh, Mohamadtaghi Rahimi, Pranesh Kumar
- FUZZ-IEEE
- 2016

- R Farnoosh, H Rezazadeh, A Sobhani, D Ebrahimibagha
- 2014

In this paper, we present the numerical solution of ordinary differential equations (or SDEs), from each order especially second-order with time-varying and Gaussian random coefficients. We indicate a complete analysis for second-order equations in special case of scalar linear second-order equations (damped harmonic oscillators with additive or… (More)

- R Farnoosh, A Hajrajabi
- 2014

In this article, the discrete time state space model with first-order autoregressive dependent process noise is considered and the recursive method for filtering, prediction and smoothing of the hidden state from the noisy observation is designed. The explicit solution is obtained for the hidden state estimation problem. Finally, in a simulation study, the… (More)

- R Farnoosh, J Ghasemian, O Solaymani Fard
- 2012

In this paper, we deal with the ridge-type estimator for fuzzy nonlinear regression models using fuzzy numbers and Gaussian basis functions. Shrinkage regularization methods are used in linear and nonlinear regression models to yield consistent estimators. Here, we propose a weighted ridge penalty on a fuzzy nonlinear regression model, then select the… (More)