Artificial neural network modeling studies to predict the amount of carried weight by iran khodro transportation system

This paper investigates the use of three artificial neural network (ANNs) algorithms, namely, incremental back propagation algorithm (IBP), genetic algorithm (GA) and Levenberg–Marquardt algorithm (LM) for predicting Carried weight, with an automobile industry namely, Iran Khodro Company (IKCO) used as the study case. These algorithms belong to three classes: gradient descent backpropagation algorithm, genetic algorithm and LevenbergMarquardt algorithm. The above algorithms were compared according to their prediction ability, prediction accuracy, as well as degree of generalization. The network structure was trained with the algorithms by using some numerical measures as the training set. Those algorithms were then compared according to their performances in training and prediction accuracy in testing based on root mean square error (RMSE) and correlation coefficient (R). The results indicate that incremental back propagation performs better than the other algorithms in training and has higher prediction accuracy during the learning period. [Mohd Nizam Ab. Rahman, Saeid Jafarzadeh-Ghoushchi, Dzuraidah Abd. Wahab, Majid Jafarzadeh-Ghoushji. Artificial Neural Network Modeling Studies to Predict the Amount of Carried Weight by Iran Khodro Transportation System. Life Sci J 2014; 11(2s):146-154]. (ISSN: 1097-8135). http://www.lifesciencesite.com. 25

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