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E-raamat: Computational Intelligence Assisted Design: In Industrial Revolution 4.0

(Industry 4.0 Artificial Intelligence Laboratory, Dongguan University of Technology, China), (Industry 4.0 Artificial Intelligence Laboratory, Dongguan University of Technology, China; Faculty of Engineering, University of Strathclyde, )
  • Formaat: 526 pages
  • Ilmumisaeg: 19-Jun-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781498760676
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Computational Intelligence Assisted Design: In Industrial Revolution 4.0Suurem pilt
  • Formaat: 526 pages
  • Ilmumisaeg: 19-Jun-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781498760676
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Computational Intelligence Assisted Design framework mobilises computational resources, makes use of multiple Computational Intelligence (CI) algorithms and reduces computational costs. This book provides examples of real-world applications of technology. Case studies have been used to show the integration of services, cloud, big data technology and space missions. It focuses on computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation.

This book provides readers with wide-scale information on CI paradigms and algorithms, inviting readers to implement and problem solve real-world, complex problems within the CI development framework. This implementation framework will enable readers to tackle new problems without difficulty through a few tested MATLAB source codes
Dedication iii
Foreword v
Preface vii
Acknowledgements ix
Acronyms xxi
1 Introduction 1(24)
1.1 Introduction
1(1)
1.2 History of Computational Intelligence
1(12)
1.3 On the Way to Industry 4.0
13(2)
1.4 Need for Computational Intelligence in Design and Engineering
15(1)
1.5 Terms and Definitions
16(2)
1.6 Specialized and Application Areas
18(1)
1.7 Information Sources
19(2)
1.8 How to Use This Book
21(4)
Part I: Hands-on Learning of Computational Intelligence
2 Global Optimization and Evolutionary Search
25(32)
2.1 Mimicking Natural Evolution
25(2)
2.1.1 Breading Engineering Solutions
26(1)
2.1.2 Conventional Computers
27(1)
2.1.3 Genetic Evolution-A Way to Solve Complex Optimisation Problems
27(1)
2.2 Nondeterministic Methods for Optimization and Machine Learning
27(8)
2.2.1 Nondeterministic Hill-Climbing
28(4)
2.2.2 Simulated Annealing
32(2)
2.2.3 Parallelism C-An Essential Step Towards Global Search
34(1)
2.3 The Simple Genetic Algorithm
35(10)
2.3.1 Mutation and Crossover
35(1)
2.3.2 Coding, Genes and Chromosomes
36(1)
2.3.3 The Working Mechanism
37(1)
2.3.4 GAs Transform Exponential Problems to NP-Complete Problems
38(7)
2.4 Micro Genetic Algorithm
45(2)
2.5 Genetic Algorithm Using Mendel's Principles
47(6)
2.6 Characteristics of Evolutionary Design Tools
53(1)
2.6.1 Advantages of Evolutionary Techniques
53(1)
2.6.2 Preparation and Conditions of Use
53(1)
2.7 Tutorials and Coursework
54(1)
2.8 Summary
55(2)
3 Artificial Neural Networks and Learning Systems
57(26)
3.1 Human Brain and Artificial Neural Networks
57(9)
3.1.1 Central Nervous System and Conventional Computer
57(1)
3.1.2 'Reproduce' the Human Brain by Artificial Neural Networks
58(2)
3.1.3 Mathematical Models and Types of ANNs
60(6)
3.2 ANN Design and Learning
66(6)
3.2.1 Three Steps in ANN Design
66(1)
3.2.2 Knowledge Representation
66(1)
3.2.3 Learning Process
67(1)
3.2.4 Learning Methods
68(1)
3.2.5 Learning Paradigms
68(4)
3.3 Learning Algorithms
72(8)
3.3.1 Hebbian Learning
72(1)
3.3.2 Error-Correction Learning
73(6)
3.3.3 Competitive Learning
79(1)
3.3.4 Darwinian Selective Learning and Darwin Machine
79(1)
3.4 Tutorials and Coursework
80(3)
4 Fuzzy Logic and Fuzzy Systems
83(16)
4.1 Human Inference and Fuzzy Logic
83(3)
4.1.1 Human Inference and Fuzzy Systems
83(1)
4.1.2 Fuzzy Sets
84(1)
4.1.3 Membership Functions
85(1)
4.2 Fuzzy Logic and Decision Making
86(8)
4.2.1 Formation of Fuzzy Decision Signal
88(1)
4.2.2 Fuzzy Rule Base
89(2)
4.2.3 Measurements with Fuzzification and Scaling
91(1)
4.2.4 Defuzzification and Output Signals
92(2)
4.3 Tutorial and Coursework
94(5)
Part II: CIAD and Advanced Computational Intelligence Tools
5 CIAD-Computational Intelligence Assisted Design
99(16)
5.1 Introduction
99(2)
5.2 Optimization, Design and Intelligent Design Automation
101(5)
5.2.1 Optimal Engineering Design
101(2)
5.2.2 Difficulties with Conventional Optimization Methods
103(1)
5.2.3 Converting a Design Problem into a Simulation Problem
104(1)
5.2.4 Manual Design through Trial and Error
105(1)
5.2.5 Automate Design by Exhaustive Search?
105(1)
5.2.6 Further Requirements on a CAD Environment
105(1)
5.3 Linking Intelligent Design with Manufacture
106(9)
5.4 Computational Intelligence Integrated Solver
108(1)
5.5 CIAD
109(1)
5.6 CIAE
110(1)
5.7 Intelligent Virtual Prototypes
111(1)
5.8 Physical Prototyping
112(1)
5.9 CIAM
112(1)
5.10 System Integration
113(1)
5.11 Applications
113(1)
5.12 Cyber-Physical Design Integration for Industry 4.0
113(2)
6 Extra-Numerical Multi-Objective Optimization
115(10)
6.1 Introduction
115(2)
6.2 History of Multi-Objective Optimization
117(4)
6.2.1 Aggregating Approaches
117(1)
6.2.2 Population-Based Approaches
118(1)
6.2.3 Pareto-Based Approaches
118(3)
6.3 Theory and Applications
121(1)
6.4 Multi-Objective Genetic Algorithm
122(3)
7 Swarm Intelligence
125(26)
7.1 Introduction
125(1)
7.2 Particle Swarm Optimization
125(2)
7.3 Ant Colony Optimization
127(3)
7.4 Swarm Fish Algorithm
130(8)
7.4.1 Swarm Fish Algorithm with Variable Population
130(3)
7.4.2 Multi-Objective Artificial Swarm Fish Algorithm
133(2)
7.4.3 Case Study
135(2)
7.4.4 Conclusions
137(1)
7.5 Swarm Bat Algorithm
138(2)
7.6 Firefly Algorithm
140(2)
7.7 Artificial Dolphin Swarm Algorithm
142(9)
7.7.1 Introduction
142(1)
7.7.2 Dynamic Behaviors of Dolphins
143(4)
7.7.3 k-Nearest Neighbor Classification
147(1)
7.7.4 Swarm Dolphin Algorithm
148(3)
8 Evolving Artificial Neural Networks in a Closed Loop
151(20)
8.1 Introduction
151(1)
8.2 Directly Evolving a Neural Network in a Closed Loop
152(3)
8.2.1 Existing Architectures for Neural Control
152(1)
8.2.2 Architecture of a Neural Network Controller
152(2)
8.2.3 The Problem of Neurocontroller Design
154(1)
8.3 Globally Optimized Design Through a Genetic Algorithm
155(3)
8.3.1 Difficulties with Conventional Neural Network Training Methods
155(2)
8.3.2 Training with a Genetic Algorithm
157(1)
8.4 Neural Network Control for Linear and Nonlinear System Control
158(11)
8.4.1 GA-based Design for Neural Control of a Linear Plant
158(6)
Training to Cope with Transport Delay
161(1)
Evolving the Architecture
162(2)
8.4.2 GA-Based Design for Neural Control of a Nonlinear Plant
164(8)
Evolving the Architecture
167(2)
8.5 Conclusions
169(2)
9 Evolving Fuzzy Decision-Making Systems
171(10)
9.1 Introduction
171(1)
9.2 Formulation of a Fuzzy Decision-Making System
172(3)
9.2.1 PI-Type Fuzzy Decision Making
172(1)
9.2.2 PID-Type Decision Making
173(2)
9.3 Decision-Making Parameters
175(2)
9.3.1 Membership Functions
175(1)
9.3.2 Fuzzy Rule Base
176(1)
9.3.3 Scaling Factors
177(1)
9.4 Design Example for a Nonlinear System to Control
177(3)
9.5 Conclusion
180(1)
10 Performance Assessment and Metric Indices
181(36)
10.1 Introduction
181(1)
10.2 Metric Indices
182(2)
10.3 Measure of Fitness of Fitting-Coefficients of Determination
184(2)
10.4 Measure of Error Heterogeneity-Relative Gini Index
186(1)
10.5 Measure of Trend-Trend Indices
187(5)
10.5.1 The Mean Variables
187(1)
10.5.2 The Moving Mean Variables
188(1)
10.5.3 Pareto Reliability Index
189(1)
10.5.4 Pareto Risk Index
190(1)
10.5.5 Pareto Sensitivity Indices
191(1)
10.6 Fast Approach to Pareto-Optimal Solution Recommendation
192(2)
10.6.1 Normalization
192(1)
10.6.2 FPR Steps
193(1)
10.7 Fitness Functions
194(1)
10.8 Test Functions
195(22)
Part III: CIAD for Science and Technology
11 Adaptive Bathtub-Shaped Curve
217(12)
11.1 Introduction
217(1)
11.2 Parameterization Method via Radial Basis Functions
218(2)
11.3 Adaptive Bathtub-Shaped Failure Rate Function
220(3)
11.4 Fitness Function Definition
223(1)
11.5 Simulations and Discussion
223(3)
11.6 Conclusions and Future Work
226(3)
12 Terahertz Spectroscopic Analysis
229(16)
12.1 Introduction
229(1)
12.2 THz-TDS Experimental Setup Sketch
230(1)
12.3 Statement of Mixture Component Determination
231(2)
12.4 Fitness Function Definition
233(1)
12.5 Uncertainty Studies
234(2)
12.6 Empirical Studies and Discussion
236(7)
12.7 Conclusions and Future Work
243(2)
13 Evolving a Sliding Robust Fuzzy System
245(20)
13.1 Introduction
245(1)
13.2 Application of Fuzzy Logic to Sliding Mode Control
245(6)
13.2.1 Fuzzy Switching Element for the SMC System
246(4)
13.2.2 Fuzzy Gain Scheduling for the Switching Element in the SMC System
250(1)
13.2.3 Fuzzy PD SMC System with Integral Equivalent Control
250(1)
13.3 Fuzzy SMC System Designs Using a GA
251(11)
13.3.1 FSMC-I System with a Fuzzy Switching Element
252(3)
13.3.2 FSMC-II System with Fuzzy Gain-Scheduling
255(3)
13.3.3 Fuzzy PD SMC System with Integral Equivalent Control
258(4)
13.4 Conclusion
262(3)
14 Space Tether for Payload Orbital Transfer
265(16)
14.1 Introduction
265(2)
14.2 Motorized Momentum Exchange Tether
267(2)
14.3 Payload Transfer
269(2)
14.4 Tether Strength Criterion
271(1)
14.5 Payload Transfer Objective Definition
272(1)
14.6 Simulations
273(5)
14.7 Conclusion and Future Work
278(3)
15 Structural Design for Heat Sinks
281(16)
15.1 Introduction
281(1)
15.2 Structural Modeling
282(4)
15.2.1 Total Thermal Resistance
283(2)
15.2.2 Pressure Drop
285(1)
15.3 Experimental Setup
286(2)
15.4 Optimal Design
288(1)
15.5 Fitness Functions
288(2)
15.6 Empirical Results
290(5)
15.6.1 Simulations
290(3)
15.6.2 Verification
293(2)
15.7 Conclusions and Future Work
295(2)
16 Battery Capacity Prediction
297(12)
16.1 Introduction
297(1)
16.2 Adaptive Bathtub-Shaped Functions
298(1)
16.3 Battery Capacity Prediction
299(3)
16.4 Fitness Function
302(1)
16.5 Simulation Results and Discussion
303(4)
16.6 Conclusion and Future Work
307(2)
17 Parameter Determination for Fuel Cells
309(12)
17.1 Introduction
309(2)
17.2 Analytical Modeling
311(3)
17.3 Fitness Function
314(1)
17.4 Empirical Results and Discussion
314(5)
17.5 Conclusion and Future Work
319(2)
18 CIAD Towards the Invention of a Microwave-Ignition Engine
321(18)
18.1 Introduction
321(1)
18.2 HCMI Design Evaluation and Virtual Prototyping Through Simulation
322(3)
18.2.1 Models of the Emitter and Cylinder
322(2)
18.2.2 Coupled Constraint Optimization Problem
324(1)
18.3 Heuristic Methods and Improved GA Search
325(3)
18.3.1 Existing Heuristic Methods Tested
325(1)
18.3.2 Improved GA Search
326(2)
18.4 Case Studies
328(2)
Case 1: Coupled Resonant Frequencies and Emitter Lengths
328(2)
Case 2: Coupled Frequencies, Emitter Lengths, Emitter Heights and Emitter Widths
330(1)
18.5 Virtual Prototyping Results and Comparison
330(6)
18.5.1 Virtual Prototyping for Case 1 with a Default Emitter
330(3)
Search Results of the NM Method
330(1)
Search Results of the Generic GA
331(1)
Search Results of the PGA
332(1)
18.5.2 Virtual Prototyping for Case 2 with an Extended Emitter
333(18)
Search Results of the NM Simplex
333(1)
Search Results of the GA
334(1)
Search Results of the PGA
335(1)
18.6 Conclusion
336(3)
19 Control for Semi-Active Vehicle Suspension System
339(36)
19.1 Introduction
339(3)
19.2 Two-Degree-of-Freedom Semi-Active Suspension System
342(3)
19.3 Sliding Mode Control with Skyhook Surface Scheme
345(2)
19.4 Fuzzy Logic Control
347(4)
19.5 Fuzzy Sliding Mode Control with Switching Factor α-FαSMC
351(1)
19.6 Polynomial Function Supervising FαSMC-An Improvement
351(6)
19.6.1 Multi-objective Micro-GA for the Offline Step
352(3)
19.6.2 Offline Step
355(1)
19.6.3 Online Step
356(1)
19.7 Road Surface Profile-Modeling of the Source of Uncertainty
357(1)
19.8 Uncertainty Studies
358(2)
19.9 Simulations
360(11)
19.10 Conclusion and Future Work
371(4)
Part IV: CIAD for Social Sciences
20 Exchange Rate Modeling and Decision Support
375(12)
20.1 Introduction
375(1)
20.2 Exchange Rate Determination Model
376(1)
20.3 Fitness Function of Regression Modeling
377(2)
20.4 Empirical Results and Discussion
379(6)
20.5 Conclusions and Future Work
385(2)
21 Quantitative Modeling of Electricity Consumption
387(16)
21.1 Introduction
387(4)
21.2 Quantitative Modeling of National Electricity Consumption
391(1)
21.3 Fitness Function
392(1)
21.4 Numerical Results
393(4)
21.5 Social, Economic and Environmental Impacts
397(4)
21.6 Conclusions and Future Work
401(2)
22 CIAD Gaming Support for Electricity Trading Decisions
403(18)
22.1 Introduction
403(1)
22.2 Modelling Intelligent Market Behaviors
404(4)
22.2.1 NETA Market Price Formulation
404(1)
22.2.2 Generator Gaming Strategies
405(3)
22.2.3 Supplier Gaming Strategies
408(1)
22.3 Intelligent Agents and Modeling
408(3)
22.4 Model Analysis and Verification
411(4)
22.4.1 Small-scale Model Simulation
412(2)
22.4.2 Large-scale Model Simulation
414(1)
22.5 Applications of the Model
415(4)
22.5.1 Competitive Strategy
415(1)
22.5.2 Cooperative Strategy
416(3)
22.6 Conclusions
419(2)
23 Dynamic Behavior of Rural Regions with CO2 Emission Estimation
421(14)
23.1 Introduction
421(1)
23.2 CO2 Emission Estimation of Productive Activity
422(4)
23.3 Hybrid Modeling of the Functional Region
426(1)
23.4 Fitness Function
427(2)
23.5 Empirical Results and Discussion
429(4)
23.6 Conclusions and Future Work
433(2)
24 Spatial Analysis of Functional Region of Suburban-Rural Areas
435(10)
24.1 Introduction
435(1)
24.2 Spatial Modeling of the Functional Regions
436(1)
24.3 Sensitive Analysis to Functional Distance
437(1)
24.4 Fitness Function
437(1)
24.5 Empirical Results and Discussion
438(5)
24.6 Conclusions and Future Work
443(2)
25 CIAD for Industry 4.0 Predictive Customization
445(16)
25.1 Introduction
445(1)
25.2 Customization in Industry 4.0
446(5)
25.2.1 CPS with Data Analytics Framework for Smart Manufacturing
447(1)
25.2.2 Smart Products and Product Lifecycle for Industry 4.0
448(1)
25.2.3 Computational Intelligence for Customized Production
449(2)
25.3 Methodology and CIAD Approaches
451(4)
25.3.1 Fuzzy c-Means Approach
452(1)
25.3.2 Framework for Predicting Potential Customer Needs and Wants
453(2)
25.4 Case Study
455(5)
25.5 Discussion and Conclusion
460(1)
References 461(40)
Glossary 501(2)
Index 503
Dr. Yi Chen is a lecturer in Dynamics and Control at the School of Engineering and Built Environment at Glasgow Caledonian University.
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