Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) on thickness auto adjustment of instant noodle dough on roll press machine

The proposed paper is an auto adjustment of instant noodle dough thickness on a roll press machine. In this plan will be implemented two artificial intelligent methods of Neural Network Back propagation (BP-NN) and Extreme Learning Machine (ELM). The method is intended to improve the performance of the controls on this machine. The input variables are the height and thickness of the dough by using the sharp sensor. While the output plan is two, dc motor and stepper motor. From the results of experiments that have been done, it is concluded that the use of extreme learning machine method is better than the Neural Network method. This is evidenced from the comparison of the average error output value on the ELM of 0.002440673 for training data and 0.6337302 for validation data. While the average error of output value on NN is 0.006638958 for training data and 0.568553072 for validation data. The training time of ELM method is 0.0072812 second and NN method is 0.501274 sec.