TY - JOUR
T1 - Application of neuro-fuzzy networks to forecast innovation performance - The example of Taiwanese manufacturing industry
AU - Chien, Shih Chien
AU - Wang, Tai Yue
AU - Lin, Su Li
N1 - Funding Information:
The technological innovation survey of Taiwanese manufacturing industry was conducted in 2001, and which was partially supported by the National Science Council of Taiwan. In this paper, we redesign the sample of 53 manufacturing firms into nine groups by means of rotation method, where only Case One has five firms, and the other eight cases have six. Furthermore, all the groups are considered to be testing set, in turn, with the other groups used as the training set. Admittedly, ninefold cross validation cases can increase the reliability of the sampling process. As a result, this is the most popular method in practice, specifically for problems where the number of samples is relatively small ( Kantardzic, 2002 ).
Funding Information:
This work was partially supported by National Science Council, Taiwan, ROC, under Grant NSC 94-2416-H-006-014.
Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010/3
Y1 - 2010/3
N2 - 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.
AB - 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.
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U2 - 10.1016/j.eswa.2009.06.107
DO - 10.1016/j.eswa.2009.06.107
M3 - Article
AN - SCOPUS:71749121378
SN - 0957-4174
VL - 37
SP - 1086
EP - 1095
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 2
ER -