Title :
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
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;
Conference_Titel :
Computer and Knowledge Engineering (ICCKE), 2011 1st International eConference on
Conference_Location :
Mashhad
Print_ISBN :
978-1-4673-5712-8
DOI :
10.1109/ICCKE.2011.6413344
