Modeling of moisture content in tomato drying procces by ANN-GA technique

Author

Javadikia, P. ; Rafiee, Shakiba ; Garavand, A.T. ; Keyhani, Ali

Author_Institution

Dept. of Agric. Machinery Eng., Razi Univ., Kermanshah, Iran

fYear

2011

fDate

13-14 Oct. 2011

Firstpage

162

Lastpage

165

Abstract

A Feed Forward Neural Network (FFNN) is designed to estimate the moisture content of dried tomato. The experiment is done by a dryer that it was capable of providing any desired drying air temperature, relative humidity and velocity. After getting the practical data, a general FFNN is designed and optimized with Genetic Algorithm (GA) through MATLAB software. Result showed that the configuration of FFNN and GA is very powerful and it is able to model any set of data. Finally, the result of best network by GA was a network with only one hidden layer and 11 neurons and this network could predict moisture content of dried tomato with correlation coefficient of 0.99.

Keywords

agricultural machinery; agricultural products; agriculture; drying; feedforward neural nets; genetic algorithms; moisture; production engineering computing; ANN-GA technique; FFNN; dryer; drying air temperature; feedforward neural network; genetic algorithm; moisture content; relative humidity; tomato drying procces; velocity; Artificial neural networks; Biological neural networks; Genetic algorithms; Moisture; Neurons; Optimization; Artifical Neural network; Drying; Optimaization; Tomato;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Knowledge Engineering (ICCKE), 2011 1st International eConference on

Conference_Location

Mashhad

Print_ISBN

978-1-4673-5712-8

Type

conf

DOI

10.1109/ICCKE.2011.6413344

Filename

6413344