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E-raamat: Data Fusion Mathematics: Theory and Practice

(Ramaiah Instof Tech, India)
  • Formaat: 600 pages
  • Ilmumisaeg: 27-Aug-2015
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781040071755
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Data Fusion Mathematics: Theory and PracticeSuurem pilt
  • Formaat: 600 pages
  • Ilmumisaeg: 27-Aug-2015
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781040071755
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Fills the Existing Gap of Mathematics for Data FusionData fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI). Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the monitoring of vehicles, aerospace systems, large-scale structures, and large industrial automation plants.Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to DF. The material covered can be used for evaluation of the performances of any designed and developed DF systems. It tries to answer whether unified data fusion mathematics can evolve from various disparate mathematical concepts, and highlights mathematics that can add credibility to the data fusion process.Focuses on Mathematical Tools That Use Data FusionThis text explores the use of statistical/probabilistic signal/image processing, filtering, component analysis, image algebra, decision making, and neuro-FL–GA paradigms in studying, developing and validating data fusion processes (DFP). It covers major mathematical expressions, and formulae and equations as well as, where feasible, their derivations. It also discusses SDF concepts, DF models and architectures, aspects and methods of type 1 and 2 fuzzy logics, and related practical applications. In addition, the author covers soft computing paradigms that are finding increasing applications in multisensory DF approaches and applications.This book:Explores the use of interval type 2 fuzzy logic and ANFIS in DFCovers the mathematical treatment of many types of filtering algorithms, target-tracking methods, and kinematic DF methodsPresents single and multi-sensor tracking and fusion mathematicsConsiders specific DF architectures in the context of decentralized systemsDiscusses information filtering, Bayesian approaches, several DF rules, image algebra and image fusion, decision fusion, and wireless sensor network (WSN) multimodality fusionData Fusion Mathematics: Theory and Practice incorporates concepts, processes, methods, and approaches in data fusion that can help you with integrating DF mathematics and achieving higher levels of fusion activity, and clarity of performance. This text is geared toward researchers, scientists, teachers and practicing engineers interested and working in the multisensor data fusion area.

Arvustused

"An application's guide to sensor fusion - Raol's comprehensive yet succinct handling of the mathematical fundamentals of sensor fusion make this a reference source for every practitioner." Ajith K. Gopal, The Council for Scientific and Industrial Research in South Africa

" comprehensively presents tools for data fusions. Initial two chapters cover basic of data fusion and state estimations, especially Bayesian framework. The rest of chapters deal with advance topics that include fuzzy-logic based design, centralized and decentralized strategies, and image fusion. I feel the content of the book will useful both academia and industry." Dr. Mangal Kothari, Indian Institute of Technology Kanpur

Preface xvii
Acknowledgements xxi
Introduction xxiii
Author xxix
1 Introduction to Data Fusion Process 1(12)
1.1 Data Fusion Aspects
1(2)
1.2 Data Fusion Models
3(5)
1.2.1 Joint Directors of Laboratories Model
3(1)
1.2.2 Modified Waterfall Fusion Model
3(1)
1.2.3 The Intelligence Cycle-Based Model
4(1)
1.2.4 Boyd Model
5(1)
1.2.5 Omnibus Model
5(1)
1.2.6 Dasarathy Model
5(3)
1.3 Sensor Data Fusion Configurations
8(1)
1.3.1 Complementary
8(1)
1.3.2 Competitive
9(1)
1.3.3 Cooperative
9(1)
1.4 Sensor Data Fusion Architectures
9(2)
1.4.1 Centralised Fusion
9(1)
1.4.2 Distributed Fusion
10(1)
1.4.3 Hybrid Fusion
11(1)
1.5 Data Fusion Process
11(1)
Exercises
12(1)
References
12(1)
2 Statistics, Probability Models and Reliability: Towards Probabilistic Data Fusion 13(44)
2.1 Introduction
13(1)
2.2 Statistics
14(4)
2.2.1 Mathematical Expectation
15(1)
2.2.2 Variance, Covariance and STD
16(1)
2.2.3 Correlations and Autocorrelation Function
17(1)
2.3 Probability Models
18(2)
2.4 Probabilistic Methods for DF
20(5)
2.4.1 Bayesian Formula
20(2)
2.4.2 DF Based on Bayesian Rule
22(2)
2.4.3 Distributed DF Based on Bayesian Rule
24(1)
2.4.4 LLs-Based DF
25(1)
2.5 Reliability in DF
25(6)
2.5.1 Bayesian Method
26(1)
2.5.1.1 Weighted Average Method
27(1)
2.5.2 Evidential Method
27(1)
2.5.3 FL-Based Approach
28(1)
2.5.4 Markov Models for Reliability Evaluation
28(2)
2.5.5 Reliability in Least-Squares Estimation
30(1)
2.6 Information Methods
31(4)
2.6.1 Entropy and Information
31(2)
2.6.2 Fisher Information
33(1)
2.6.3 Information Pooling Methods
34(1)
2.6.3.1 Linear Opinion Pool
34(1)
2.6.3.2 Independent Opinion Pool
34(1)
2.6.3.3 Independent Likelihood Pool
34(1)
2.7 Probability Concepts for Expert System and DF
35(5)
2.7.1 Probabilistic Rules and Evidence
36(1)
2.7.2 Propagation of Confidence Limits
36(3)
2.7.3 Combining-Fusion of Multiple Reports
39(1)
2.8 Probabilistic Methods for DF: Theoretical Examples
40(12)
2.8.1 Maximum Entropy Method
40(1)
2.8.2 Maximum Likelihood Method
41(1)
2.8.3 ML and Incomplete Data
42(1)
2.8.4 Bayesian Approach
43(3)
2.8.5 DF Aspects/Examples
46(2)
2.8.5.1 Sensors with No Noise
46(1)
2.8.5.2 Fusion of Homogeneous Sensor Data
47(1)
2.8.6 Some Realistic DF Problems
48(4)
2.9 Bayesian Formula and Sensor/DF: Illustrative Example
52(3)
Exercises
55(1)
References
56(1)
3 Fuzzy Logic and Possibility Theory-Based Fusion 57(92)
3.1 Introduction
57(3)
3.2 Fuzzy Logic Type 1
60(9)
3.2.1 MFs for Fuzzification
61(1)
3.2.2 FS Operations
62(1)
3.2.3 Fuzzy Inference System
63(2)
3.2.3.1 Steps of Fuzzy Inference Process
63(2)
3.2.4 Triangular-Norm
65(1)
3.2.5 s-Norm
66(1)
3.2.6 Defuzzification
67(1)
3.2.7 Fuzzy Implication Functions
68(1)
3.3 Adaptive Neuro-Fuzzy Inference System
69(3)
3.4 Fuzzy Logic Type 2
72(13)
3.4.1 Type 2 and Interval Type 2 Fuzzy Sets
73(1)
3.4.2 IT2FL Mathematics
74(5)
3.4.3 The Set Theoretic Operations for IT2FS
79(1)
3.4.4 Further Operations on IT2FS
79(6)
3.5 Fuzzy Intelligent Sensor Fusion
85(1)
3.6 FL-Based Procedure for Generating the Weights for a DF Rule
86(2)
3.7 FL-ANFIS for Parameter Estimation and Generation of DF Weights: Illustrative Examples
88(6)
3.7.1 ANIFS-Based Parameter Estimation
88(2)
3.7.1.1 Parameter Estimation from an Algebraic Model
89(1)
3.7.2 ANIFS for Deciphering a Linear DF Rule for Images
90(4)
3.7.2.1 Determination of DF Rule Using ANFIS and Random Image Data
90(2)
3.7.2.2 Determination of DF Rule Using ANFIS and Real-Blurred Images
92(2)
3.8 Possibility Theory
94(7)
3.8.1 Possibility Distribution
94(1)
3.8.2 Possibility Set Functions
94(2)
3.8.3 Joint Possibility Distribution, Specificity and Non-Interactivity
96(2)
3.8.4 Possibility and Necessity of Fuzzy Events
98(1)
3.8.5 Conditional Possibility
99(2)
3.9 Fusion of Long-Wave IR and EOT Images Using Type 1 and Type 2 Fuzzy Logics: Illustrative Examples
101(22)
3.9.1 FL Systems: Takagi-Sugeno-Kang Inference Method
101(6)
3.9.2 IT2FS Operations and Inference
107(3)
3.9.3 FSs-Type Reduction
110(2)
3.9.3.1 Centre of Sets (CoS)
110(1)
3.9.3.2 Karnik Mendel Algorithm (KMA)
111(1)
3.9.3.3 Defuzzification
112(1)
3.9.4 Implementation of Image Fusion Using MATLAB FLS Toolbox
112(2)
3.9.5 Results and Discussion
114(9)
3.9.5.1 Qualitative Analysis
115(4)
3.9.5.2 Analytical Evaluation
119(4)
3.10 DF Using Dempster-Shafer and Possibility Theory: Illustrative Example
123(14)
3.10.1 Information Fusion for Close-Range Mine Detection
127(2)
3.10.1.1 Belief Function Fusion
127(2)
3.10.1.2 Fuzzy and Possibility Fusion
129(1)
3.10.2 Close-Range Mine Detection Measures
129(4)
3.10.2.1 IR Measures
129(2)
3.10.2.2 IMD Measures
131(1)
3.10.2.3 GPR Measures
131(2)
3.10.3 Fusion Combination Evaluation
133(1)
3.10.4 Comparison and Decision Results
134(3)
Appendix 3A: Type 1-Triangular MF-MATLAB Code
137(2)
Appendix 3B: Type 2-Gaussian MF-MATLAB Code
139(2)
Appendix 3C: Fuzzy Inference Calculations - MATLAB Code
141(4)
Exercises
145(1)
References
146(3)
4 Filtering, Target Tracking and Kinematic Data Fusion 149(60)
4.1 Introduction
149(1)
4.2 The Kalman Filter
150(14)
4.2.1 State and Sensor Models
151(1)
4.2.2 The Kalman Filter Algorithm
152(4)
4.2.2.1 Time Propagation/Time Update Algorithm
152(1)
4.2.2.2 Measurement/Data-Update Algorithm
153(3)
4.2.3 The Innovations: Kalman Filter Residuals
156(1)
4.2.4 Steady-State Filters
157(1)
4.2.5 Asynchronous, Delayed and A-Sequent Measurements
158(2)
4.2.6 The Extended Kalman Filter
160(2)
4.2.6.1 Time Propagation of States/Covariance Matrix
161(1)
4.2.6.2 Measurement Update
162(1)
4.2.7 Kalman Filter: A Natural Data-Level Fuser
162(2)
4.2.7.1 Fusion-Measurement Update Algorithm
162(2)
4.3 The Multi-Sensor Data Fusion and Kalman Filter
164(3)
4.3.1 Measurement Models
164(1)
4.3.2 Group-Sensor Method
165(1)
4.3.3 Sequential-Sensor Method
166(1)
4.3.4 Inverse-Covariance Form
166(1)
4.3.5 Track-to-Track Fusion
167(1)
4.4 Non-Linear Data Fusion Methods
167(3)
4.4.1 Likelihood Estimation Methods
168(1)
4.4.2 Derivative-Free Filtering and Fusion
168(1)
4.4.3 Other Non-Linear Tracking Filters
169(1)
4.5 Data Association in MS Systems
170(6)
4.5.1 Nearest-Neighbour Standard Filter
172(1)
4.5.2 Probabilistic Data Association Filter
173(2)
4.5.3 Multiple-Hypothesis Filter
175(1)
4.6 Information Filtering
176(3)
4.6.1 Square Root Information Filtering
177(1)
4.6.2 DF Based on Square Root Information Filtering
178(1)
4.7 HI Filtering-Based DF
179(5)
4.7.1 HI Posterior Filter
180(1)
4.7.2 Risk-Sensitive HI Filter
181(1)
4.7.3 Global H1 Filter for DF
182(1)
4.7.4 Hybrid H2 and HI Filter
183(1)
4.8 Optimal Filtering for Data Fusion with Missing Measurements
184(11)
4.8.1 Basic Filter for Missing Measurements: SVF
186(1)
4.8.2 Optimal Filter for Missing Measurements: Measurement Level Fusion
187(2)
4.8.3 Optimal Filter in Two Parts for SVF
189(1)
4.8.4 Optimal Filter in Two Parts for MLF
189(1)
4.8.5 Performance Evaluation of the Filters for Handling Missing Data: Illustrative Examples
190(5)
4.9 Factorisation Filtering and Sensor DF: Illustrative Example
195(12)
4.9.1 Kalman UD Factorisation Filter
197(5)
4.9.2 UD Factorisation Filter for Correlated Process Noise and Bias Parameters
202(1)
4.9.3 Sensor Fusion Scheme
203(2)
4.9.4 Performance Evaluation of UD and UDCB Filters for Tracking and Fusion
205(2)
Acknowledgement
207(1)
Exercises
207(1)
References
208(1)
5 Decentralised Data Fusion Systems 209(26)
5.1 Introduction
209(1)
5.2 Data Fusion Architectures
210(3)
5.2.1 Hierarchical Data Fusion Architectures
210(2)
5.2.2 Distributed DF Architectures
212(1)
5.2.3 Decentralised Data Fusion Architectures
212(1)
5.3 Decentralised Estimation and Fusion
213(8)
5.3.1 Information Filter
213(2)
5.3.2 Information Filter and Bayesian Theorem
215(1)
5.3.3 Information Filter in Multi-Sensor Estimation
215(1)
5.3.4 Hierarchical Information Filter
216(1)
5.3.5 Decentralised Information Filter
217(2)
5.3.5.1 Square Root Information Filter and Fusion
219(2)
5.4 Decentralised Multi-Target Tracking
221(1)
5.4.1 Decentralised Data Association
221(1)
5.4.2 Decentralised Identification and Bayesian Theorem
222(1)
5.5 Millman's Formulae in Sensor Data Fusion
222(7)
5.5.1 Generalised Millman's Formula
223(2)
5.5.2 Millman's Fusion Formulae in Filtering Algorithms
225(1)
5.5.3 Millman's Fusion Formulae in Smoothing Algorithms
226(1)
5.5.4 Generalised Millman's Formula in State Estimation
226(11)
5.5.4.1 Optimal Centralised Fusion
226(1)
5.5.4.2 Multi-sensory Fusion
227(1)
5.5.4.3 Hierarchical Data Fusion
228(1)
5.6 SRIF for Data Fusion in Decentralised Network with Four Sensor Nodes: Illustrative Example
229(4)
Exercises
233(1)
References
234(1)
6 Component Analysis and Data Fusion 235(44)
6.1 Introduction
235(2)
6.2 Independent Component Analysis
237(8)
6.2.1 Independence
239(1)
6.2.2 NG Property
240(1)
6.2.3 Determination of NG Property
240(2)
6.2.3.1 Kurtosis
241(1)
6.2.3.2 Neg-Entropy
241(1)
6.2.4 Determination of ICs Based on Information Theory
242(1)
6.2.5 Maximum Likelihood Estimation
243(1)
6.2.6 Demonstration of FastICA Code: Illustrative Example
244(1)
6.3 An Approach to Image Fusion Using ICA Bases
245(10)
6.3.1 Fusion Preliminaries
246(1)
6.3.2 Major Fusion Strategies
247(1)
6.3.2.1 Pixel-Based Fusion
247(1)
6.3.2.2 Region-Based Fusion
248(1)
6.3.3 ICA and TICA Bases
248(3)
6.3.3.1 Bases
248(1)
6.3.3.2 ICA Bases
249(1)
6.3.3.3 Topographic ICA: TICA Bases
250(1)
6.3.4 Training and Properties of ICA Bases
251(1)
6.3.4.1 Training
251(1)
6.3.4.2 Properties
252(1)
6.3.5 Image Fusion Using ICA Bases
252(1)
6.3.6 Pixel- and Region-Based Fusion Rules Using ICA Bases
253(2)
6.3.6.1 WC Pixel-Based Method
253(1)
6.3.6.2 Region-Based Image Fusion Using ICA Bases
253(1)
6.3.6.3 Performance Evaluation Metrics
254(1)
6.4 Principal Component Analysis
255(3)
6.4.1 Image Fusion Using PCA Coefficients
257(1)
6.4.2 Image Fusion of Blurred Aircraft Images Using PCA Coefficients: Illustrative Example
257(1)
6.5 Discrete-Cosine Transform
258(3)
6.5.1 Multi-Resolution DCT
259(1)
6.5.2 Multi-Sensor Image Fusion
260(1)
6.6 WT: A Brief Theory
261(6)
6.6.1 Image Analysis for Image Fusion by WT
263(1)
6.6.2 Image Fusion of Blurred Aircraft Images Using WT Coefficients: Illustrative Example
264(3)
6.7 An Approach to Image Fusion Using ICA and Wavelets
267(1)
6.8 Non-Linear ICA and PCA
268(1)
6.8.1 Non-Linear ICA
268(1)
6.8.2 Non-Linear PCA
268(1)
6.9 Curvelet Transform for Image Fusion
269(2)
6.10 Image Fusion Using MR Singular Value Decomposition
271(5)
6.10.1 Multi-Resolution SVD
271(2)
6.10.2 Image Fusion Using MRSVD: Illustrative Example
273(3)
Exercises
276(1)
References
277(2)
7 Image Algebra and Image Fusion 279(80)
S. Sethu Selvi
7.1 Introduction
279(3)
7.1.1 A Digital Image
279(1)
7.1.2 Needs of Image Fusion
280(2)
7.2 Image Algebra
282(12)
7.2.1 Point and Value Sets
283(2)
7.2.2 Images and Templates
285(4)
7.2.3 Recursive Templates
289(1)
7.2.4 Neighbourhoods and the p-Product: Illustrative Examples
290(4)
7.3 Pixels and Features of an Image
294(2)
7.4 Inverse Image
296(1)
7.5 Red, Green and Blue, Grey Images and Histograms
297(1)
7.6 Image Segmentation
298(3)
7.6.1 Thresholding
299(1)
7.6.2 Edge-Based Segmentation
300(1)
7.6.3 Region-Based Segmentation
300(1)
7.7 Noise Processes in an Observed/Acquired Image
301(4)
7.7.1 Salt and Pepper Noise
302(1)
7.7.2 Gaussian Noise
303(1)
7.7.3 Speckle Noise
304(1)
7.7.4 Quantisation and Uniform Noise
304(1)
7.7.5 Photon Counting Noise
304(1)
7.7.6 Photographic Grain Noise
305(1)
7.7.7 Periodic Noise
305(1)
7.8 Image Feature Extraction Methods
305(8)
7.9 Image Transformation and Filtering Approaches
313(9)
7.9.1 Linear Filtering
314(1)
7.9.2 Median Filtering
315(1)
7.9.3 2D Transforms
315(2)
7.9.4 Wavelet Transform
317(3)
7.9.5 Multi-Scale Image Decomposition
320(2)
7.10 Image Fusion Mathematics
322(10)
7.10.1 Pixel-Level Fusion
322(6)
7.10.2 Feature Level Fusion
328(2)
7.10.3 Region-Based Image Fusion
330(2)
7.11 Image Fusion Algorithms
332(8)
7.12 Performance Evaluation
340(7)
7.13 Multimodal Biometric Systems and Fusion: Illustrative Examples
347(6)
7.13.1 Multimodal Biometric System Based on Feature Vector Fusion
347(3)
7.13.2 Character Recognition System Based on Score Fusion
350(3)
Exercises
353(2)
References
355(4)
8 Decision Theory and Fusion 359(52)
8.1 Introduction
359(4)
8.2 Loss and Utility Functions
363(1)
8.3 Bayesian DT
364(1)
8.4 Decision Making with Multiple Information Sources
365(2)
8.4.1 Super Bayesian
365(1)
8.4.2 Multiple Bayesians
366(1)
8.4.3 Product Solution
367(1)
8.5 Fuzzy Modelling Approach for Decision Analysis/Fusion
367(2)
8.6 Fuzzy-Evolutive Integral Approach
369(1)
8.7 Decision Making Based on Voting
370(1)
8.7.1 A General Framework for Voting
371(1)
8.8 DeF Using FL for Aviation Scenarios
371(6)
8.8.1 Decision Level Fusion, FL and FIS
372(1)
8.8.2 Performance Evaluation: Illustrative Examples
373(4)
8.8.2.1 DeF 1: Formation Flight
373(1)
8.8.2.2 DeF 2: Air Lane
374(3)
8.9 DeF Strategies
377(9)
8.9.1 Classifier Systems
378(1)
8.9.2 Classifier Fusion and Selection
379(7)
8.9.2.1 Combining Class Labels: Crisp Outputs
380(2)
8.9.2.2 Class Ranking
382(2)
8.9.2.3 Combining Soft Outputs
384(2)
8.9.3 Selection of the Classifiers
386(1)
8.10 SA with FL and DeF for Aviation Scenarios: Illustrative Examples
386(23)
8.10.1 SA and Decision-Level Fusion
387(1)
8.10.2 A MATLAB/GUI Tool for Evaluation of FIFs
387(1)
8.10.3 FL and DeF
388(1)
8.10.4 Performance of FL-Based Decision Systems
388(20)
8.10.4.1 Scenario I: Formation Flight
389(5)
8.10.4.2 Scenario II: Attack
394(4)
8.10.4.3 Scenario III: Threat Assessment
398(3)
8.10.4.4 Study of Effect of Noise on FL-Based Decision Fusion Systems
401(7)
8.10.5 Discussion of the Results
408(1)
Exercises
409(1)
References
409(2)
9 Wireless Sensor Networks and Multimodal Data Fusion 411(26)
9.1 Introduction
411(1)
9.2 Communication Networks and Their Topologies in WSNs
412(1)
9.2.1 Star Network
413(1)
9.2.2 Mesh Network
413(1)
9.2.3 Hybrid Star: Mesh Network
413(1)
9.3 Sensor/Wireless Sensor Networks
413(2)
9.3.1 Need in WSNs
414(1)
9.3.2 WSN Challenges
414(1)
9.4 Wireless Sensor Networks and Architectures
415(1)
9.4.1 Distributed Kalman Filtering
416(1)
9.5 Sensor Data Fusion in WSN
416(4)
9.5.1 Reporting
417(1)
9.5.2 Decision of Fusion
417(1)
9.5.3 Cluster-Based Data Fusion
417(1)
9.5.4 Synchronisation among Nodes
418(1)
9.5.5 Resistance against Attacks
419(1)
9.6 Multimodality Sensor Fusion
420(6)
9.6.1 Multimodal Sensor Data Management
421(1)
9.6.2 Multimodal Sensory Data Interpretation
421(1)
9.6.3 Human-Sensor Data Interaction
422(1)
9.6.4 Real-World System Development and Deployment
422(1)
9.6.5 Multimodal Fusion Methodology
422(4)
9.6.5.1 Data Fusion Levels
423(1)
9.6.5.2 Techniques for Multimodal Fusion
423(3)
9.7 Decision Fusion Rules in WSN
426(2)
9.7.1 System Model
426(1)
9.7.2 Fusion Rules
427(1)
9.8 Data Aggregation in WSN
428(1)
9.9 Hybrid Data and Decision Fusion in WSN
429(3)
9.9.1 Identification of Parameters of the Partial Differential Equation
430(1)
9.9.2 Hybrid Data/Decision Fusion Approach: Illustrative Example
430(2)
9.10 Optimal Decision Fusion in WSN
432(2)
9.10.1 Complementary Optimal Decision Fusion
432(2)
Exercises
434(1)
References
435(2)
10 Soft Computing Approaches to Data Fusion 437(58)
10.1 Introduction
437(2)
10.2 Artificial Neural Networks
439(5)
10.2.1 Feed-Forward Neural Networks
440(4)
10.2.1.1 Back Propagation Algorithm for Training
440(2)
10.2.1.2 Recursive Least Squares Filtering Algorithms
442(2)
10.3 Radial Basis Function Neural Network
444(3)
10.3.1 NW Structure
445(1)
10.3.2 RBF Properties
446(1)
10.3.3 RBF Training Algorithm
447(1)
10.4 Recurrent Neural Networks
447(3)
10.4.1 RNN-S/Hopfield Neural Network
448(1)
10.4.2 RNN-Forcing Input
449(1)
10.4.3 RNN-Weighted States
449(1)
10.4.4 RNN-Error
450(1)
10.5 FL and Systems as SC Paradigm
450(6)
10.5.1 Traditional Logic
450(1)
10.5.2 Fuzzy Sets and Uncertainty
451(1)
10.5.3 Production Rules
452(1)
10.5.4 Linguistic Variables
453(1)
10.5.5 Fuzzy Logic and Functions
453(1)
10.5.6 Fuzzy Hedges
453(2)
10.5.7 FIS/FLC Developmental Process
455(1)
10.6 FL in Kalman Filter for Image-Centroid Tracking: A Type of Fusion
456(10)
10.6.1 Segmentation/Centroid Detection Method
457(1)
10.6.2 FL-Based KF
458(1)
10.6.3 Realisation of Fuzzy Error Mapping Using FIS
459(1)
10.6.4 Simulation of Synthetic Image Data
459(2)
10.6.4.1 State Model
460(1)
10.6.4.2 Measurement Model
460(1)
10.6.5 Gating and NN Data Association
461(1)
10.6.6 Simulation of FL-CDTA: Illustrative Example
462(4)
10.7 Genetic Algorithms
466(3)
10.7.1 Biological Basis
466(1)
10.7.2 Components and Operations in GA
466(2)
10.7.2.1 Chromosomes
466(1)
10.7.2.2 Population and Fitness
467(1)
10.7.2.3 Initialisation and Reproduction
467(1)
10.7.2.4 Crossover
467(1)
10.7.2.5 Mutation
467(1)
10.7.2.6 Generation
467(1)
10.7.2.7 Survival of the Fittest
468(1)
10.7.2.8 Cost Function, Decision Variables and Search Space
468(1)
10.7.3 Steps in Genetic Algorithm
468(1)
10.7.4 Other Aspects of GAs
469(1)
10.8 SDF Approaches Using SC Methods: Illustrative Examples
469(11)
10.8.1 DF for Parameter Estimation Using RNN
470(4)
10.8.1.1 DF: Parameter Vector Fusion
473(1)
10.8.2 DF for Parameter Estimation Using GA
474(2)
10.8.3 Multiple Neural NWs Using DF Method for Non-Linear Process Modelling
476(3)
10.8.3.1 Aggregation of ANNs Using DF Method: Illustrative Example
476(3)
10.8.4 DF Using Neural NWs: Illustrative Example
479(1)
10.9 Machine Learning
480(5)
10.9.1 Taxonomy of the Machine Learning
481(1)
10.9.2 Machine Learning Diversity Metric for DF
482(2)
10.9.2.1 Preliminary Results Using E-Measure: Illustrative Example
482(2)
10.9.3 Machine Learning Algorithm and DF for Estimating Energy Expenditure
484(1)
10.10 Neural-Fuzzy-Genetic Algorithm Fusion
485(5)
10.10.1 ANN-Based Approach
485(2)
10.10.2 Fuzzy Logic-Based Approach
487(2)
10.10.3 GA-Based Approach
489(1)
10.10.4 GA-FL Hybrid Approach
489(1)
10.11 Image Analysis Using ANFIS: Illustrative Example
490(2)
Acknowledgement
492(1)
Exercises
492(1)
References
493(2)
Appendix A: Some Algorithms and/or Their Derivations 495(12)
Appendix B: Other Methods of DF and Fusion Performance Evaluation Metrics 507(12)
Appendix C: Automatic Data Fusion 519(4)
Appendix D: Notes and Information on Data Fusion Software Tools 523(10)
Appendix E: Definitions of Sensor DF in Literature 533(6)
Appendix F: Some Current Research Topics in DF 539(2)
Index 541
Jitendra R. Raol received a BE and ME in electrical engineering from the MS University of Baroda, Vadodara in 1971 and 1973, respectively, and a PhD (in electrical and computer engineering) from McMaster University, Hamilton, Canada in 1986. He taught for two years at the MS University of Baroda before joining the National Aeronautical Laboratory in 1975. He retired in 2007 as Scientist G and head, flight mechanics and control division at CSIR-NAL. His main research interests are DF, system identification, state/parameter estimation, flight mechanicsflight data analysis, H-infinity filtering, ANNs, fuzzy systems, genetic algorithms, and soft technologies for robotics.
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