Sharif Digital Repository

A flexible meta modelling approach is presented to predictive control of a drying process using Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Partial Least Squares (PLS) analysis. In the proposed approach, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modelling with aim at predicting the granule particle size and executing by ANFIS and ANN. ANN hold the promise of being capable of producing non-linear models, being able to work under noise conditions and being fault tolerant to the loss of neurons or connections. Also, the... 

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car... 

This paper models car ownership in Iran based on the data in a period of years 1980 to 2007 by artificial neural network (ANN) and Fuzzy Linear Regression (FLR). The car ownership is mainly affected by purchasing power of the customers, social and demographic factors; the car ownership model has a multi variable form. To explain the effect of these factors, ANN and FLR models are applied. The major reason for applying fuzzy concept and ANN is to overcome the inter-correlation problem associated with the independent variables. In this study, average family size; total population; urban population; urbanization rate; gross national product per capita; gasoline price; total length of road are... 

This paper presents a neuro-based approach for annual transport energy demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the transport energy demand, gross domestic product (GDP), population and total number of vehicles are selected. This approach is structured as a hierarchical artificial neural networks (ANNs) model based on the supervised multi-layer perceptron (MLP), trained with the back-propagation (BP) algorithm. This hierarchical ANNs model is designed properly. The input variables are transport energy demand in the last year, GDP, population and total number of vehicles. The output variable is the energy...