Abstract
This paper presents an evolutionary method for calculating the important degree (ID) of individual input variable of well-trained neural network (NN). The importance of each input variable of neural network could be distinguished in accordance with ID value obtained. In this research, several linear and nonlinear systems’ identifications were firstly studied and simulated. From the simulation results shown, the evolutionary method proposed is quite promising and accurate for the estimation of system’s parameters. In other worlds, the method proposed could be used for data mining in the real applications. In order to verify our inference view, the evaporation process of thin film was studied either. It is a real case of industrial application. Again, the studied results show that the method proposed indeed has the superiority and potential in the area of data mining.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Intelligent Information Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
