Site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems
This paper presents a new model for the propagation prediction for mobile communication network inside building. The model is based on the determination of the dominant paths between the transmitter and the receiver, diffraction at the coner and wave-guiding effect. The field strength is predicted w...
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th-mahidol.213112018-07-24T10:41:12Z Site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems S. Phaiboon P. Phokharatkul Mahidol University Engineering This paper presents a new model for the propagation prediction for mobile communication network inside building. The model is based on the determination of the dominant paths between the transmitter and the receiver, diffraction at the coner and wave-guiding effect. The field strength is predicted with adaptive neuro - fuzzy inference systems (AKFIS), trained with measurements. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network The K-means algorithm for selection of training patterns is also used. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical model are achieved. © 2004 IEEE. 2018-07-24T03:41:12Z 2018-07-24T03:41:12Z 2004-12-01 Conference Paper 2004 4th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2004. (2004), 154-157 2-s2.0-28844473270 https://repository.li.mahidol.ac.th/handle/123456789/21311 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=28844473270&origin=inward |
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Engineering S. Phaiboon P. Phokharatkul Site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems |
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This paper presents a new model for the propagation prediction for mobile communication network inside building. The model is based on the determination of the dominant paths between the transmitter and the receiver, diffraction at the coner and wave-guiding effect. The field strength is predicted with adaptive neuro - fuzzy inference systems (AKFIS), trained with measurements. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network The K-means algorithm for selection of training patterns is also used. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical model are achieved. © 2004 IEEE. |
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Mahidol University |
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Mahidol University S. Phaiboon P. Phokharatkul |
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Conference or Workshop Item |
author |
S. Phaiboon P. Phokharatkul |
author_sort |
S. Phaiboon |
title |
Site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems |
title_short |
Site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems |
title_full |
Site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems |
title_fullStr |
Site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems |
title_full_unstemmed |
Site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems |
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site-specific indoor propagation prediction using adtive neuro-fuzzy inference systems |
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2018 |
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https://repository.li.mahidol.ac.th/handle/123456789/21311 |
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1763490409813639168 |