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Intelligent Control: A Hybrid Approach Based on Fuzzy Logic, Neural Networks and Genetic Algorithms 2014 ed. [Kietas viršelis]

  • Formatas: Hardback, 282 pages, aukštis x plotis: 235x155 mm, weight: 6282 g, 55 Illustrations, color; 103 Illustrations, black and white; XVII, 282 p. 158 illus., 55 illus. in color., 1 Hardback
  • Serija: Studies in Computational Intelligence 517
  • Išleidimo metai: 16-Dec-2013
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319021346
  • ISBN-13: 9783319021348
Kitos knygos pagal šią temą:
Intelligent Control: A Hybrid Approach Based on Fuzzy Logic, Neural Networks and Genetic Algorithms 2014 ed.Didesnis paveikslėlis
  • Formatas: Hardback, 282 pages, aukštis x plotis: 235x155 mm, weight: 6282 g, 55 Illustrations, color; 103 Illustrations, black and white; XVII, 282 p. 158 illus., 55 illus. in color., 1 Hardback
  • Serija: Studies in Computational Intelligence 517
  • Išleidimo metai: 16-Dec-2013
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319021346
  • ISBN-13: 9783319021348
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783319021348.html
Raktažodžiai:

Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of the fuzzy controller is then described and finally an evolutionary algorithm is applied to the neurally-tuned-fuzzy controller in which the sigmoidal function shape of the neural network is determined.

The important issue of stability is addressed and the text demonstrates empirically that the developed controller was stable within the operating range. The text concludes with ideas for future research to show the reader the potential for further study in this area.

Intelligent Control will be of interest to researchers from engineering and computer science backgrounds working in the intelligent and adaptive control.



Recenzijos

From the book reviews:

This research monograph offers a concise introduction to the contemporary controllers based on computational intelligence and revolves around the constructs of fuzzy controllers whose development is supported by various mechanisms of neurocomputing and evolutionary optimization. The references are representative, carefully selected to serve well the purpose to support the essential subject matters covered in the book. this book can appeal to a broad readership of those interested in fuzzy control, intelligent systems, robotics . (Witold Pedrycz, zbMATH 1307.93004, 2015)

1 Introduction
1(10)
1.1 Intelligent Control
1(3)
1.2 Intelligent Control Architecture
4(1)
1.3 Approaches to Intelligent Control
5(1)
1.4 Experimental Rig of Flexible Arm
6(1)
1.5 Overview of the Book
7(4)
References
8(3)
2 Dynamical Systems
11(28)
2.1 Introduction
11(1)
2.2 Dynamics of Robot Manipulator
12(1)
2.3 Dynamics of Flexible-Arm
13(10)
2.3.1 Strength and Stiffness
14(2)
2.3.2 Safety Factor
16(1)
2.3.3 Experimental Flexible Arm
17(1)
2.3.4 Printed Armature Motor
18(2)
2.3.5 Motor Drive Amplifier
20(1)
2.3.6 Acceleromeler
21(1)
2.3.7 Computer Interfacing
22(1)
2.3.8 Operating Characteristics
22(1)
2.4 Previous Research and Developments
23(3)
2.5 Dynamic Equations of Flexible Robotic Arm
26(7)
2.5.1 Development of the Simulation Algorithm
28(1)
2.5.2 Hub Displacement
29(1)
2.5.3 End-Point Displacement
30(1)
2.5.4 Matrix Formulation
31(1)
2.5.5 State-Space Formulation
32(1)
2.6 Some Simulation Results
33(3)
2.6.1 Bang-Bang Signal
34(2)
2.7 Summary
36(3)
References
36(3)
3 Control Systems
39(18)
3.1 Introduction
39(2)
3.2 Control Systems
41(3)
3.3 Control of Flexible Arm
44(3)
3.4 Open-Loop Control
47(1)
3.5 Closed-Loop Control
47(4)
3.5.1 Joint Based Collocated Controller
49(1)
3.5.2 Hybrid Collocated and Non-Collocated Controller
50(1)
3.6 Alternative Control Approaches
51(2)
3.6.1 Intelligent Control Approaches
52(1)
3.7 Summary
53(4)
References
53(4)
4 Mathematics of Fuzzy Control
57(38)
4.1 Fuzzy Logic
57(1)
4.2 Fuzzy Sets
57(1)
4.3 Membership Functions
58(9)
4.3.1 Piecewise Linear MF
59(1)
4.3.2 Nonlinear Smooth MF
60(1)
4.3.3 Sigmoidal MF
61(2)
4.3.4 Polynomial or Spline-Based Functions
63(2)
4.3.5 Irregular Shaped MF
65(2)
4.4 Linguistic Variables
67(1)
4.5 Features of Linguistic Variables
68(2)
4.6 Linguistic Hedges
70(2)
4.7 Fuzzy If-then Rules
72(5)
4.7.1 Fuzzy Proposition
72(1)
4.7.2 Methods for Construction of Rule-Base
73(3)
4.7.3 Properties of Fuzzy Rules
76(1)
4.8 Fuzzification
77(1)
4.9 Inference Mechanism
78(4)
4.9.1 Mamdani Fuzzy Inference
79(1)
4.9.2 Sugeno Fuzzy Inference
80(1)
4.9.3 Tsukamoto Fuzzy Inference
81(1)
4.10 Defuzzification
82(8)
4.10.1 Defuzzification Methods
82(6)
4.10.2 Properties of Defuzzification
88(1)
4.10.3 Analysis of Defuzzification Methods
89(1)
4.11 Summary
90(5)
References
90(5)
5 Fuzzy Control
95(42)
5.1 Introduction
95(6)
5.1.1 Fuzzification for Control
96(1)
5.1.2 Inference Mechanism for Control
97(1)
5.1.3 Rule-Base for Control
98(2)
5.1.4 Defuzzification for Control
100(1)
5.2 Theoretical Analysis of Fuzzy Controllers
101(7)
5.2.1 Consideration of Process Variables
102(2)
5.2.2 Types of Fuzzy Controllers
104(4)
5.3 Fuzzy Controller for Flexible Arm
108(3)
5.3.1 Input-Output Selection
110(1)
5.4 PD-Like Fuzzy Logic Controller
111(7)
5.4.1 PD-Like Fuzzy Controller with Error and Change of Error
111(4)
5.4.2 PD-Like Fuzzy Controller with Error and Velocity
115(3)
5.5 PI-Like Fuzzy Controller
118(4)
5.6 Integral Windup Action
122(1)
5.7 PID-Like Fuzzy Controller
123(2)
5.8 PD-PI-Type-like Fuzzy Controller
125(4)
5.9 Some Experimental Results on PD-PI FLC
129(2)
5.10 Choice of Scaling Factors
131(1)
5.11 Summary
132(5)
References
133(4)
6 Evolutionary-Fuzzy Control
137(42)
6.1 Introduction
137(5)
6.2 Overview of Evolutionary Algorithms
142(5)
6.2.1 Evolutionary Programming
143(1)
6.2.2 Evolution Strategies
143(1)
6.2.3 Genetic Programming
144(1)
6.2.4 Differential Evolution
144(1)
6.2.5 Cultural Algorithm
145(1)
6.2.6 Genetic Algorithm
145(2)
6.3 Evolutionary Fuzzy Control
147(3)
6.4 Merging MFs and Rule-Bases of PD-PI FLC
150(5)
6.5 Optimising FLC Parameters Using GA
155(12)
6.5.1 Encoding Scheme
157(1)
6.5.2 Chromosome Representation for MFs
157(2)
6.5.3 Chromosome Representation for Rule-Base
159(1)
6.5.4 Objective Function
159(2)
6.5.5 Dynamic Crossover
161(1)
6.5.6 Dynamic Mutation
162(3)
6.5.7 Selection
165(1)
6.5.8 Initialisation
166(1)
6.5.9 Evaluation
166(1)
6.6 Some Experimental Results
167(6)
6.7 Summary
173(6)
References
173(6)
7 Neuro-Fuzzy Control
179(38)
7.1 Introduction
179(1)
7.2 Neural Networks and Architectures
180(3)
7.3 Combinations of Neural Networks and Fuzzy Controllers
183(6)
7.3.1 NN for Correcting FLC
185(1)
7.3.2 NN for Learning Rules
185(1)
7.3.3 NN for Determining MFs
186(2)
7.3.4 NN for Learning/Tuning Scaling Parameters
188(1)
7.4 Scaling Parameters of PD-PI Fuzzy Controller
189(2)
7.5 Reducing the Number of Scaling Parameters
191(1)
7.6 Neural Network for Tuning Scaling Factors
192(6)
7.6.1 Backpropagation Learning with LinearActivation Function
193(3)
7.6.2 Learning with Non-Linear Activation Function
196(2)
7.7 Multi-Resolution Learning
198(4)
7.7.1 Adaptive Neural Activation Functions
200(2)
7.8 Some Experimental Results
202(10)
7.9 Summary
212(5)
References
213(4)
8 Evolutionary-Neuro-Fuzzy Control
217(26)
8.1 Introduction
217(2)
8.2 Integration of Fuzzy Systems, Neural Networks and Evolutionary Algorithms
219(7)
8.3 EA-NN Cooperative Combination
226(6)
8.3.1 EA for Weight Learning
226(3)
8.3.2 EA for Weights and Activation Functions Learning
229(3)
8.4 Optimal Sigmoid Function Shape Learning
232(1)
8.5 Evolutionary-Neuro-Fuzzy PD-PI-like Controller
233(3)
8.5.1 GA-Based Neuro-Fuzzy Controller
234(2)
8.6 Some Experimental Results
236(4)
8.7 Summary
240(3)
References
240(3)
9 Stability Analysis of Intelligent Controllers
243(26)
9.1 Introduction
243(1)
9.2 Mathematical Preliminaries
244(8)
9.3 Qualitative Stability Analysis of Fuzzy Controllers
252(6)
9.4 Passivity Approach to Stability Analysis of Fuzzy Controllers
258(2)
9.5 Stability Analysis of PD-PI-like Fuzzy Controller
260(2)
9.6 Summary
262(7)
References
264(5)
10 Future Work
269(12)
10.1 Epilogue
269(1)
10.2 Future Research Directions
270(1)
10.3 Adaptive Neural Network Control
271(8)
10.3.1 Adaptive Neuro-Fuzzy Controller
271(3)
10.3.2 B-Spline Neural Network
274(1)
10.3.3 CMAC Network
274(2)
10.3.4 Binary Neural Network-Based Fuzzy Controller
276(3)
10.4 Summary
279(2)
References
279(2)
Index 281
Nazmul H. Siddique graduated from Dresden University of Technology, Germany in Cybernetics and Automation Engineering in 1989. He obtained M. Sc. Eng. in Computer Science and Engineering from Bangladesh University of Engineering and Technology (BUET) in 1995. He received his PhD in intelligent control from the Department of Automatic Control and Systems Engineering, University of Sheffield, England in 2003. He has been a Lecturer in the School of Computing and Intelligent Systems, University of Ulster at Magee, UK since 2001. Dr. Siddiques research interests relate to intelligent systems, computational intelligence, stochastic systems, Markov models, and complex systems. Dr. Siddique has published over 110 journal/refereed conference papers including 7 book chapters and co-authored two books (to be published by John Wiley and Springer verlag in 2012). He guest edited 5 special issues of reputed journals. He co-edited seven conference proceedings. He has served as committee members and chairs of a number of national and international conferences. He is an editor of the Journal of Behavioural Robotics, associate editor of Journal of Engineering Letters and member of the editorial advisory board of International Journal of Neural Systems. He is a senior member of IEEE and is on the executive committee of the IEEE SMC UK-RI Chapter.