June 19, 2024
Gholam Hossein Roshani

Gholam Hossein Roshani

Academic rank: Associate professor
Education: Ph.D in Nuclear Engineering
Faculty: Faculty ofٍٍ Electrical Engineering


a comparison optimization methods of response surface methodology (Rsm) radial basis function (Rbf) network and multi-layer perceptron (Mlp) for bending property of wool fabric
Type Article
Researchers Kiamars Fathi، Mohammad Bameni Moghadam، Gholam Hossein Roshani، mohamad ataee، Esmaeel Jafarpanah، Elnaz Eftekhari، Marzieh Ahmadi Marzdashti، Ali Nazari


The computational intelligences such as artificial neural network (ANN) and fuzzy inference system (FIS) along with Statistical Methods are strong tools for prediction, classification and optimization in engineering applications. In this paper, radial basis function (RBF) network, Multi-layer Perceptron (MLP) and Response Surface Methodology (RSM) are used and compared for prediction of bending length of wool fabric. For developing of the proposed models, the input parameters are SHP (%), BTCA (%) and Nano ZnO (%) and the output is bending length (cm). Results show that there are no significant differences among the methods.