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Function-based and physics-based hybrid modular neural network for radio wave propagation modeling.

by Lee Wai Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 118-121). / Abstracts in English and Chinese. / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Structure of Thesis --- p.8 / Chapter 1.3 --- Methodology --- p.8 / Chapter 2 --- BACKGROUND THEORY --- p.10 / Chapter 2.1 --- Radio Wave Propagation Modeling --- p.10 / Chapter 2.1.1 --- Basic Propagation Phenomena --- p.10 / Chapter 2.1.1.1 --- Propagation in Free Space --- p.10 / Chapter 2.1.1.2 --- Reflection and Transmission --- p.11 / Chapter 2.1.2 --- Practical Propagation Models --- p.12 / Chapter 2.1.2.1 --- Longley-Rice Model --- p.13 / Chapter 2.1.2.2 --- The Okumura Model --- p.13 / Chapter 2.1.3 --- Indoor Propagation Models --- p.14 / Chapter 2.1.3.1 --- Alexander Distance/Power Laws --- p.14 / Chapter 2.1.3.2 --- Saleh Model --- p.15 / Chapter 2.1.3.3 --- Hashemi Experiments --- p.16 / Chapter 2.1.3.4 --- Path Loss Models --- p.17 / Chapter 2.1.3.5 --- Ray Optical Models --- p.18 / Chapter 2.2 --- Ray Tracing: Brute Force approach --- p.20 / Chapter 2.2.1 --- Physical Layout --- p.20 / Chapter 2.2.2 --- Antenna Information --- p.20 / Chapter 2.2.3 --- Source Ray Directions --- p.21 / Chapter 2.2.4 --- Formulation --- p.22 / Chapter 2.2.4.1 --- Formula of Amplitude --- p.22 / Chapter 2.2.4.2 --- Power Reference E o --- p.23 / Chapter 2.2.4.3 --- Power spreading with path length 1/d --- p.23 / Chapter 2.2.4.4 --- Antenna Patterns --- p.23 / Chapter 2.2.4.5 --- Reflection and Transmission Coefficients --- p.24 / Chapter 2.2.4.6 --- Polarization --- p.26 / Chapter 2.2.5 --- Mean Received Power --- p.26 / Chapter 2.2.6 --- Effect of Thickness --- p.27 / Chapter 2.3 --- Neural Network --- p.27 / Chapter 2.3.1 --- Architecture --- p.28 / Chapter 2.3.1.1 --- Multilayer feedforward network --- p.28 / Chapter 2.3.1.2 --- Recurrent Network --- p.29 / Chapter 2.3.1.3 --- Fuzzy ARTMAP --- p.29 / Chapter 2.3.1.4 --- Self organization map --- p.30 / Chapter 2.3.1.5 --- Modular Neural network --- p.30 / Chapter 2.3.2 --- Training Method --- p.32 / Chapter 2.3.3 --- Advantages --- p.33 / Chapter 2.3.4 --- Definition --- p.34 / Chapter 2.3.5 --- Software --- p.34 / Chapter 3 --- HYBRID MODULAR NEURAL NETWORK --- p.35 / Chapter 3.1 --- Input and Output Parameters --- p.35 / Chapter 3.2 --- Architecture --- p.36 / Chapter 3.3 --- Data Preparation --- p.42 / Chapter 3.4 --- Advantages --- p.42 / Chapter 3.5 --- Limitation --- p.43 / Chapter 3.6 --- Applicable Environment --- p.43 / Chapter 4 --- INDIVIDUAL MODULES IN HYBRID MODULAR NEURAL NETWORK --- p.45 / Chapter 4.1 --- Conversion between spherical coordinate and Cartesian coordinate --- p.46 / Chapter 4.1.1 --- Architecture --- p.46 / Chapter 4.1.2 --- Input and Output Parameters --- p.47 / Chapter 4.1.3 --- Testing result --- p.48 / Chapter 4.2 --- Performing Rotation and translation transformation --- p.53 / Chapter 4.3 --- Calculating a hit point --- p.54 / Chapter 4.3.1 --- Architecture --- p.55 / Chapter 4.3.2 --- Input and Output Parameters --- p.55 / Chapter 4.3.3 --- Testing result --- p.56 / Chapter 4.4 --- Checking if an incident ray hits a Scattering Surface --- p.59 / Chapter 4.5 --- Calculating separation distance between source point and hitting point --- p.59 / Chapter 4.5.1 --- Input and Output Parameters --- p.60 / Chapter 4.5.2 --- Data Preparation --- p.60 / Chapter 4.5.3 --- Testing result --- p.61 / Chapter 4.6 --- Calculating propagation vector of secondary ray --- p.63 / Chapter 4.7 --- Calculating polarization vector of secondary ray --- p.63 / Chapter 4.7.1 --- Architecture --- p.64 / Chapter 4.1.2 --- Input and Output Parameters --- p.65 / Chapter 4.7.3 --- Testing result --- p.68 / Chapter 4.8 --- Rejecting ray from simulation --- p.72 / Chapter 4.9 --- Calculating receiver signal --- p.73 / Chapter 4.10 --- Further comment on preparing neural network --- p.74 / Chapter 4.10.1 --- Data preparation --- p.74 / Chapter 4.10.2 --- Batch training --- p.75 / Chapter 4.10.3 --- Batch size --- p.78 / Chapter 5 --- CANONICAL EVALUATION OF MODULAR NEURAL NETWORK --- p.80 / Chapter 5.1 --- Typical environment simulation compared with ray launching --- p.80 / Chapter 5.1.1 --- Free space --- p.80 / Chapter 5.1.2 --- Metal ground reflection --- p.81 / Chapter 5.1.3 --- Dielectric ground reflection --- p.84 / Chapter 5.1.4 --- Empty Hall --- p.86 / Chapter 6 --- INDOOR PROPAGATION ENVIRONMENT APPLICATION --- p.90 / Chapter 6.1 --- Introduction --- p.90 / Chapter 6.2 --- Indoor measurement on the Third Floor of Engineering Building --- p.90 / Chapter 6.3 --- Comparison between simulation and measurement result --- p.92 / Chapter 6.3.1 --- Path 1 --- p.93 / Chapter 6.3.2 --- Path 2 --- p.95 / Chapter 6.3.3 --- Path 3 --- p.97 / Chapter 6.3.4 --- Path 4 --- p.99 / Chapter 6.3.5 --- Overall Performance --- p.100 / Chapter 6.4 --- Delay Spread Analysis --- p.101 / Chapter 6.4.1 --- Location 1 --- p.103 / Chapter 6.4.2 --- Location 2 --- p.105 / Chapter 6.4.3 --- Location 3 --- p.107 / Chapter 6.4.4 --- Location 4 --- p.109 / Chapter 6.4.5 --- Location 5 --- p.111 / Chapter 6.5 --- Summary --- p.112 / Chapter 7 --- CONCLUSION --- p.I / Chapter 7.1 --- Summary --- p.113 / Chapter 7.2 --- Recommendations for Future Work --- p.115 / PUBLICATION LIST --- p.117 / BIBLIOGRAHY --- p.118

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_322766
Date January 1999
ContributorsLee, Wai Hung., Chinese University of Hong Kong Graduate School. Division of Electronic Engineering.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, vi, 121 leaves : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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