Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Microstrip Hairpin Bandpass Filter
  2.1 Design Parameters for Hairpin Filter
 3. High Dimensional Modeling
 4. Proposed High Dimensional Model for the Analysis of MicrostripHairpin Bandpass Filter
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• J. Lakshmi Narayana [ Assoc.Professor in ECE, St.Ann’s College of Engineering and Technology ]
• Dr. K. Sri Rama Krishna [ Professor & Head of ECE, V.R Siddhartha Engineering College ]
• Dr. L. Pratap Reddy [ Professor in ECE, Jawaharlal Nehru Technological University ]
• G. V. Subrahmanyam [ Research Scholar in ECE, Acharya Nagarjuna University ]
• M. Sindhu [ M.Tech Student in ECE, V.R Siddhartha Engineering College ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605