Application of neuro-fuzzy networks to forecast innovation performance - The example of Taiwanese manufacturing industry

Authors:
Shih-Chien Chien;Tai-Yue Wang;Su-Li Lin
Affiliations:
Department of International Business and Trade, Shu-Te University, 59 Hun Shan Rd., Hun Shan Village, Yen Chau, Kaohsiung County, Taiwan, ROC;Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan, ROC;Department of International Business and Trade, Shu-Te University, 59 Hun Shan Rd., Hun Shan Village, Yen Chau, Kaohsiung County, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

In this paper, we elaborate a neural network model to predict innovation performance with fuzzy rules, as well as implement an adaptive neuro-fuzzy inference systems (ANFIS) to measure the innovation performance through technical information resource and innovation objective. Building on the findings from fuzzy neural network approach, using Sugeno ANFIS, we also compared the artificial neural network with statistical techniques. We found strong support for ANFIS method has better results than the neural network and statistical techniques with regards to forecast performance. Finally, on the basis of our analysis, our results hold an important lesson for decision makers who may clearly picture the rules and adjust the resource allocation to meet their innovation objectives.