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Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2005 / Cilt: 13 - Sayı: 1

"Soft Computing" Methods in Microwave Active Device Modeling

Yavuz CENGİZ
Filiz GÜNEŞ and Mehmet Fatih ÇAĞLAR
Filiz GÜNEŞ
Sayfa
137–148
Yayın Tarihi
2005-01-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Performances of the FLS and ANN models are compared and conclusions are drawn.

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Amaç & Hipotez:In this work, the signal and noise behaviors of a microwave transistor within its operation domain (CT,VDS, IDS, f) are modeled by the Artificial Neural Network (ANN) and Fuzzy Logic System (FLS) without using any information on the microwave circuit theory .
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Temel Bulgular & Veri:A worked example is presented where the same data is employed for both models selected from the manufacturer's data sheets.
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Sonuç & Katkı:Performances of the FLS and ANN models are compared and conclusions are drawn.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Fuzzy Logic System (FLS) · Artificial Neural Network (ANN) model · Signal and Noise parameters

Özet

In this work, the signal and noise behaviors of a microwave transistor within its operation domain (CT,VDS, IDS, f) are modeled by the Artificial Neural Network (ANN) and Fuzzy Logic System (FLS) without using any information on the microwave circuit theory . A worked example is presented where the same data is employed for both models selected from the manufacturer's data sheets. Performances of the FLS and ANN models are compared and conclusions are drawn.

Abstract

In this work, the signal and noise behaviors of a microwave transistor within its operation domain (CT,VDS, IDS, f) are modeled by the Artificial Neural Network (ANN) and Fuzzy Logic System (FLS) without using any information on the microwave circuit theory . A worked example is presented where the same data is employed for both models selected from the manufacturer's data sheets. Performances of the FLS and ANN models are compared and conclusions are drawn.

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Bu Makaleye Atıf Yap

CENGİZ, Y., ÇAĞLAR, F. G. A. M. F., GÜNEŞ, F. (2005). "Soft Computing" Methods in Microwave Active Device Modeling. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 13 - Sayı: 1, 137–148. https://ajindex.com/articles/184226

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Kaynakça (8)

  1. Z.R. Hu, Z.M. Yang, V.F. Fusco, J.A.C. Stewart, “Unified Small-Signal-Noise Model for Active Microwave Devices” IEE Proc.-G, Vol. 140, No 1, February 1993 pp.55-60.
  2. F. Lin, G. Kompa, “FET Model Parameter Extraction Based on Optimization with Multiplane Data-Fitting and Bidirectional Search-A New Concept” IEEE Trans. on Microwave Theory and Techniques, Vol. 42, No 7, July 1994 pp.1114-1121.
  3. J.P. Roux, L. Escotte, R. Plana, J. Graffeuil, S.L. Delage, H. Blanck, “Small –Signal and Noise Model Extraction Technique for Heterojunction Bipolar Transistor at Microwave Frequencies” IEEE Trans.on Microwave Theory and Techniques, Vol. 43, No 2, February 1995 pp. 293-298.
  4. K. Shirakawa, H. Oikawa, T. Shimura, Y. Kawasaki, Y. Ohashi, T.Saito, Y. Daido, “An Approach to Determining an Equivalent Circuit for HEMT’S”, IEEE Trans.on Microwave Theory and Techniques, Vol. 43, No 3, March pp.499-503. F. G¨une¸s, F. G¨urgen and H. Torpi, “Signal-Noise Neural Network Model for Active Microwave Device”, IEE Proc-Circuits Devices and Systems, Vol 143, No 1, 1996 pp. 1-8. F. G¨une¸s, H. Torpi, F. G¨urgen,“A Multidimensional Signal-Noise Neural Model for Microwave Transistor”, IEE Proc-Circuits Devices and Systems, Vol 145, No 2, 1998 pp 111-117. F. G¨une¸s, Y. Cengiz, “Optimization of a Microwave Amplifier using Neural Performance Data Sheets with Genetic Algorithms”, International Conference on Artificial Neural Networks (ICANN) 2003, 26-29 June 2003,
  5. Istanbul. V. Miraftab, R.R. Mansour, “Computer-aided Tuning of Microwave Filters Using Fuzzy Logic”, IEEE Trans. Microwave Theory Tech., December 2002.
  6. V. Miraftab, R.R. Mansour, “EM-Based Design Tools For Microwave Circuits Using Fuzzy Logic Techniques”, IEEE MTT-S Digest, pp. 169-172, 2003. L.A. Zadeh, “Fuzzy Sets ”, Information and Control, Vol. 8, 338-353, 1965. I.H. Alta¸s, “Bulanık Mantık Denetimli Bir G¨u¸c Uyartım Sistemi”, Elektrik-Elektronik Bilgisayar M¨uhendisli˘gi Ulusal Kongresi, Gaziantep (1999).
  7. W. McCulloch, W. Pitts, “A logical calculus of ideas imminent in nervous activity”, Bulletin of Mathematical Biophysics, vol. 5, pp. 115–133, 1943. F. Rosenblatt, “The perceptron: a probabilistic model for information storage and organization in the brain”, Psychological Review, vol. 65, pp. 386–408, 1958.
  8. J.Q. Zhang, K.C. Gupta, Neural Networks for RF and Microwave Design, Norwood, Artech House, 2000.