期刊名称：International Journal of Future Generation Communication and Networking
印刷版ISSN：2233-7857
出版年度：2012
卷号：5
期号：1
出版社：SERSC
摘要：Conventional neural network modeling techniques are not suitable for developing models that have many input variables because data generation and model training become too expensive. In this paper, an efficient neural network modeling technique for microstrip hairpin band pass filter that have many input variables is proposed. The decomposition approach is used to simplify the overall high dimensional neural network modeling problem into a set of low dimensional sub neural network problems. A method to combine the sub models with a filter empirical/equivalent model is developed. An additional neural network mapping model is formulated with the neural network sub models and empirical/ equivalent model to produce the final overall filter model. Even, with a limited amount of data, the proposed model can produce much more accurate results compared to the conventional neural network model and the resulting model is much faster than an EM model.
关键词：Computer-aided design (CAD); high dimensional modeling; Microstrip hairpin band pass filter; neural network; optimization; simulation
