Comparison of hybrid ANN models: A case study of instant noodle industry in Indonesia

Abstract

Artificial neural networks (ANNs) is the most stand popular practice to forecast the demand product since the other techniques still do not give the more accuracy. Furthermore, the hybrid method from ANN promises the best alternative to predict the customer demand. This paper proposes the hybrid model of ANN with the analytic hierarchy process (AHP), Monte Carlo (MC), and geometric random distribution to create new models to obtain the unusual methods in prediction. Those methods are substituted in the spaces of input weight and bias in the network. These hybrid methods are called AHP(iw)ANN(b) and MC(iw)ANN(b). The hybrid technique of ANN has an approach of the time series-forecasting model. ANN is implemented in the testing case after the training process has run by the system, and process of validate is compared the testing from the training dataset. The Overall process is iterating the error to produce the mean squared error (MSE). The conclusions of this study, the hybrid ANN with AHP, MC and geometric random distributions show the good result of small MSE. More specifically, the hybrid AHPANN is better than hybrid MCANN. (C) 2017 The Authors. Published by IASE. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).