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

• Other Authors: Lee, Wai Hung.
• Format: Others
• Language: English; Chinese
• Published: 1999
• Subjects: Neural networks (Computer science); Radio wave propagation > Mathematical models; Mobile communication systems
• Online Access: http://library.cuhk.edu.hk/record=b5890075; http://repository.lib.cuhk.edu.hk/en/item/cuhk-322766

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