Muutke küpsiste eelistusi

E-raamat: Fuzzy Decision Making In Modeling And Control

3.67/5 (6 hinnangut Goodreads-ist)
Edited by (Erasmus Univ Rotterdam, The Netherlands), Edited by (Technical Univ Of Lisbon, Portugal)
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 43,29 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele
Fuzzy Decision Making In Modeling And ControlSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Decision making and control are two fields with distinct methods for solving problems, and yet they are closely related. This book bridges the gap between decision making and control in the field of fuzzy decisions and fuzzy control, and discusses various ways in which fuzzy decision making methods can be applied to systems modeling and control.Fuzzy decision making is a powerful paradigm for dealing with human expert knowledge when one is designing fuzzy model-based controllers. The combination of fuzzy decision making and fuzzy control in this book can lead to novel control schemes that improve the existing controllers in various ways. The following applications of fuzzy decision making methods for designing control systems are considered: Fuzzy decision making for enhancing fuzzy modeling. The values of important parameters in fuzzy modeling algorithms are selected by using fuzzy decision making. Fuzzy decision making for designing signal-based fuzzy controllers. The controller mappings and the defuzzification steps can be obtained by decision making methods. Fuzzy design and performance specifications in model-based control. Fuzzy constraints and fuzzy goals are used. Design of model-based controllers combined with fuzzy decision modules. Human operator experience is incorporated for the performance specification in model-based control.The advantages of bringing together fuzzy control and fuzzy decision making are shown with multiple examples from real and simulated control systems.
Foreword vii
Preface ix
Acknowledgments xi
Introduction
1(16)
Control systems
1(3)
Advanced control systems
4(3)
Fuzzy control and decision making
7(6)
Fuzzy logic control
7(3)
Fuzzy model-based control
10(2)
Fuzzy decisions for control
12(1)
Chapter outline
13(4)
Fuzzy Decision Making
17(20)
Classification of decision making methods
18(2)
General formulation of decision making
20(3)
Fuzzy decisions
23(4)
Fuzzy multiattribute decision making
27(9)
Alternatives
28(1)
Decision criteria
29(1)
Membership values
29(2)
Weight factors
31(1)
Aggregation function
31(1)
Ranking
32(1)
Overview of the decision model
33(2)
Relationship to other decision methods
35(1)
Summary and concluding remarks
36(1)
Fuzzy Decision Functions
37(28)
Main types of aggregation
37(3)
Triangular norms and conorms
40(3)
Triangular norms
40(2)
Triangular co-norms
42(1)
Averaging and compensatory operators
43(10)
Averaging operators
43(4)
Compensatory operators
47(2)
Associative compensatory operators
49(2)
Ordered weighted averaging operators
51(2)
Generalized operators
53(4)
Monotonic identity commutative aggregation operators
53(1)
Fuzzy integrals
54(1)
Rule-based mappings
55(1)
Hierarchies of operators
56(1)
Weighted aggregation
57(6)
Weighted counterparts of t-norms
59(3)
Weighted counterparts of t-conorms
62(1)
Weighted averaging operators
63(1)
Summary and concluding remarks
63(2)
Fuzzy Aggregated Membership Control
65(28)
Decision making and control
65(2)
Conventional fuzzy controllers
67(7)
Basic elements of a fuzzy controller
68(1)
Fuzzy inference mechanism
69(3)
Nonlinearity in fuzzy controllers
72(2)
Nonlinear controllers using decision functions
74(13)
Fuzzy aggregated membership controllers
75(3)
Decomposability of control surface
78(5)
Relation to rule-based systems
83(3)
Function approximation capability
86(1)
Examples of fuzzy aggregated membership control
87(4)
Parameter estimation of nonlinear parity equations in aircraft
87(2)
Nonlinear PID control of a laboratory propeller setup
89(2)
Summary and concluding remarks
91(2)
Modeling and Identification
93(16)
Formulation of the modeling problem
94(2)
Fuzzy modeling
96(5)
Linguistic fuzzy models
97(2)
Singleton fuzzy model
99(1)
Takagi-Sugeno fuzzy models
100(1)
Fuzzy identification
101(2)
Identification by product-space fuzzy clustering
103(5)
Structure identification
103(1)
Parameter estimation
104(2)
Number of clusters
106(1)
Antecedent membership functions
107(1)
Consequent parameters
107(1)
Summary and concluding remarks
108(1)
Fuzzy Decision Making for Modeling
109(28)
Fuzzy decisions in fuzzy modeling
110(15)
Fuzzy models from clustering
110(2)
Compatible cluster merging
112(1)
The decision making algorithm
113(2)
Merging clusters
115(2)
Heuristic step
117(2)
Selection of the decision function
119(1)
Compatible cluster merging algorithm
120(2)
Influence of the heuristic step
122(1)
Example
122(1)
Similarity and rule base simplification
123(2)
Defuzzification as a fuzzy decision
125(5)
Sensitivity of defuzzification to domain elements
128(2)
A defuzzification method with unequal sensitivity
130(1)
Application example: fuzzy security assessment
130(4)
Security class determination
131(2)
Defuzzification for fuzzy security assessment
133(1)
Summary and concluding remarks
134(3)
Fuzzy Model-Based Control
137(40)
Inversion of fuzzy models
138(5)
Problem definition
140(1)
Inversion methods
141(2)
Inversion of a singleton fuzzy model
143(8)
Linguistic fuzzy models with singleton consequents
143(2)
Inversion of the singleton model
145(6)
Inversion of an affine Takagi-Sugeno fuzzy model
151(4)
TS fuzzy model
151(2)
Inversion of the TS fuzzy model
153(2)
On-line adaptation of feedforward fuzzy models
155(2)
Predictive control using the inversion of a fuzzy model
157(2)
Pressure control of a fermentation tank
159(9)
Fuzzy modeling
160(1)
Singleton fuzzy model
161(2)
Affine Takagi-Sugeno model
163(1)
Predictive control based on the singleton fuzzy model
164(2)
Adaptive control
166(1)
Predictive control based on the affine TS fuzzy model
167(1)
Fuzzy compensation of steady-state errors
168(6)
Derivation of fuzzy compensation
169(3)
Application to a system with dead-zone
172(2)
Summary and concluding remarks
174(3)
Performance Criteria
177(18)
Design specifications
178(4)
Design specifications for linear systems
179(1)
Optimal control problem
179(1)
Weighted-Sum Objective
180(1)
Weighted-Max Objective
181(1)
Design specifications for nonlinear systems
181(1)
Classical performance specifications
182(4)
I/O specifications
183(2)
Regulation specifications
185(1)
Actuator effort
185(1)
Classical performance criteria
186(4)
Norms and semi-norms of signals
186(1)
1-norm
187(1)
2-norm
187(1)
∞-norm
187(1)
ITAE norm
188(1)
Root-Mean-Square
188(1)
Average-Absolute Value
188(1)
Norms of systems
189(1)
Fuzzy performance criteria
190(2)
Summary and concluding remarks
192(3)
Model-Based Control with Fuzzy Decision Functions
195(36)
Fuzzy decision making in predictive control
196(3)
Fuzzy model-based predictive control
199(6)
Fuzzy goals and constraints in the control environment
200(2)
Aggregation of criteria in the control environment
202(2)
Fuzzy criteria in model-based predictive control
204(1)
Fuzzy criteria for decision making in control
205(7)
Aggregation operators for FDM in control
205(3)
Control criteria and decision functions
208(1)
Classical objective functions
208(2)
Fuzzy objective functions
210(2)
Application examples
212(11)
Description of the simulated systems
213(1)
Linear system
213(1)
Air conditioning system
213(1)
Application of aggregation operators to the linear system
214(4)
Fuzzy vs. conventional objective functions
218(1)
Linear system
219(1)
Air-conditioning system
220(3)
Design of decision functions from expert knowledge
223(6)
System description
225(1)
Expert control
226(1)
Design of objective function
226(2)
Simulation experiments
228(1)
Summary and concluding remarks
229(2)
Derivative-Free Optimization
231(32)
Branch-and-bound optimization for predictive control
232(8)
B&B in predictive control
233(5)
Application of the B&B method to nonlinear control
238(1)
Evaluation of the B&B method applied to MBPC
239(1)
Branch-and-bound optimization for fuzzy predictive control
240(4)
Application example for fuzzy branch-and-bound
244(2)
Genetic algorithms for optimization in predictive control
246(12)
Genetic algorithms
248(1)
Basic elements of genetic algorithms
248(3)
Implementation of constraints
251(1)
Fitness function
252(1)
Encoding control variables and implementing constraints
252(2)
Genetic operators
254(1)
Population structure
255(2)
Termination conditions
257(1)
Application example with genetic algorithms
258(3)
Summary and concluding remarks
261(2)
Advanced Optimization Issues
263(18)
Convex optimization in fuzzy predictive control
264(4)
Application example with convex fuzzy optimization
268(2)
Fuzzy predictive filters
270(4)
Basic principles
270(1)
Adaptive control alternatives
271(1)
Gain filter
272(2)
Application example for fuzzy predictive filters
274(3)
Summary and concluding remarks
277(4)
Application Example
281(20)
Air-conditioning systems
282(1)
Fan-coil systems
283(2)
Fuzzy models of the air-conditioning system
285(4)
TS fuzzy model of the air-conditioning system
285(3)
Affine TS model of the air-conditioning system
288(1)
Controllers applied to the air-conditioning system
289(10)
PID control of the air-conditioning system
291(1)
Inverse control based on affine TS fuzzy model
291(1)
Predictive control based on classical cost functions
292(4)
Predictive control based on fuzzy cost functions
296(3)
Summary and concluding remarks
299(2)
Future Developments
301(6)
Theoretical analysis of FAME controllers
301(1)
Decision support for fuzzy modeling
302(1)
Cooperative control systems
302(1)
Control with approximate models
302(2)
Relation to robust control
304(1)
Hierarchical fuzzy goals in control applications
304(1)
B&B for MIMO systems
304(3)
Appendix A Model-Based Predictive Control
307(8)
A.1 Basic definitions
308(3)
A.1.1 Control and prediction horizons
308(1)
A.1.2 Objective function
308(2)
A.1.3 Reference trajectory
310(1)
A.1.4 Receding horizon principle
310(1)
A.1.5 Classical MBPC scheme
311(1)
A.2 Modeling in MBPC
311(1)
A.3 Optimization problems
312(1)
A.4 Compensation of model-plant mismatch and disturbances
313(2)
Appendix B Nonlinear Internal Model Control
315(4)
B.1 Classical internal model control
315(2)
B.2 MBPC in an internal model control scheme
317(2)
Bibliography 319(12)
Index 331


Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97898144892632e.html