Contents & References of Using soft computing methods in the design of intelligent controllers
List:
Title
Page number
Presentation A
Acknowledgment B
Abstract T
List of contents C
List of figures H
List of tables H
1: Introduction of basics and main concepts 1
1-1- Introduction. 2
1-2- Definition of soft computing (SC) 3
1-3- Objectives of soft computing. 4
1-4- The importance of soft computing. 5
2: Fuzzy computing, neural computing and algorithms based on genetics and particle swarm algorithm 6
2-1- Fuzzy logic. 7
2-1-1- The difference between fuzzy sets and classical sets. 8
2-1-2- Dry and non-dry sets. 9
2-1-3- description of fuzzy sets. 10
2-1-4- The process of using fuzzy logic. 11
2-1-5- Fuzzy logic and its connection with artificial intelligence. 13
2-2- neural networks. 14
2-2-1- An introduction to artificial neural networks. 14
2-2-2- similarity with the brain. 14
2-2-3- Artificial neural networks. 17
2-2-4- artificial nerve cell. 18
2-2-5- Structure of artificial neural networks and their function 19
2-2-6- Division of neural networks based on structure. 21
2-2-7- Division of neural networks based on learning algorithm. 22
2-2-8- A general view on network education. 23
2-3- evolutionary optimization algorithms. 25
2-4- Genetic algorithm. 26
2-4-1- Introduction. 26
2-4-2- Chromosome display. 29
2-4-3- Encoding maps. 31
2-4-4- Population initialization. 32
2-4-5- Proportion function. 33
2-4-6- genetic operators. 34
2-4-7- selection methods. 38
2-5- Particle Swarm Algorithm (PSO) 40
3: Application of fuzzy logic in mobile robots 44
3-1- History. 45
3-2- Introduction. 45
3-3- Reasons for using fuzzy controllers. 46
3-4- The structure of a fuzzy controller. 47
3-5- Fuzzy methods used in robots 49
3-5-1- Position control in moving robots 50
4: Controller design based on soft computing 56
4-1- Soft computing techniques. 57
4-2- Feedback control proportional to derivative and acceleration. 60
4-3- Multivariable fuzzy logic controllers. 62
4-4- Fuzzy neural control systems (HFNC) 63
4-4-1-Radial basis function neural model training (RBFNN). 65
References: 69
List of figures
Title
Page number
Figure 2-1: The difference of sets of values ??in (a) fuzzy and (b) non-fuzzy logic. 10
Figure 2-2: Continuous mapping of membership function values. 11
Figure 2-3: The amount of temperature dependence determined by an expert to regulate the temperature of the environment. 12
Figure 2-4: The degree of influence of a value of the corresponding belonging functions. 13
Figure 2-5: The main characteristics of a biological neuron. 16
Figure 2-6: The structure of the biological synapse. 17
Figure 2-7: Network with one neuron. 19
Figure 2-8: Artificial neural network and different layers in it. 19
Figure 2-9: Conceptual representation of the optimization process with a genetic algorithm. 29
Figure 2-10: Repeating GA in successive populations. 32
Figure 11-2: An example single-point intersection in binary representation. 36
Figure 2-12: Mutation operation in binary representation. 38
Figure 2-13: Description of PSO velocity and position of Xi particle in two-dimensional search space. 42
Figure 2-14: Particle swarm optimization flowchart. 43
Figure 3-1: Block diagram of a fuzzy controller system. 45
Figure 3-2: Classification of controller design methods 46
Figure 3-3: Block diagram to show the components of a fuzzy controller. 47
Figure 3-4: Schematic of applying the angular command to the robot. 50
Figure 3-5: Block diagram for generating the rotation angle of the robot. 50
Figure 6-3: Diagram of the angle applied to the robot and the angle returned from the robot. 51
Figure 4-1: Proportional structure of derived controller and acceleration. 60
Figure 4-2: Structure of coupled MIMO fuzzy logic controllers. 63
Figure 4-3: The structure of a fuzzy-neural hybrid controller. 64
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