Comparison of Multilayer Perceptron and Radial Basis Function Neural Networks in Predicting the Success of New Product Development
Abstract
Given that the new product failure in practice entails huge costs for organizations, the need for competitive planning has led organizations to apply appropriate approaches; one of these approaches is to predict new product success before market entry. Accordingly, this study predicts NPD success by comparing two techniques, the Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) in the clothing industry of Tabriz. In order to collect data, a questionnaire with good validity and reliability was distributed among the population. MLP and RBF were used to analyze data. Based on MSE, RMSE and R2, data analysis showed that MLP had lower error than RBF in predicting NPD success.
Keywords:
new product development, success prediction, artificial neural networkDownloads
References
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