1. Identification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network

Gh. Ahmadi; M. Teshnelab

Volume 8, Issue 3 , Summer 2020, , Pages 417-425

http://dx.doi.org/10.22044/jadm.2020.8865.2021

Abstract
  Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism ...  Read More

H.6.2.2. Fuzzy set
2. Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System

N. Moradkhani; M. Teshnehlab

Volume 7, Issue 3 , Summer 2019, , Pages 367-375

http://dx.doi.org/10.22044/jadm.2018.5295.1638

Abstract
  Cement rotary kiln is the main part of cement production process that have always attracted many researchers’ attention. But this complex nonlinear system has not been modeled efficiently which can make an appropriate performance specially in noisy condition. In this paper Takagi-Sugeno neuro-fuzzy ...  Read More