The program is broken down as follows:
1. Fundamentals of fuzzy or blurred logic.
1.1. Basics of fuzzy logic. Imprecision and uncertainty.
1.2. fuzzy sets.
1.3. Membership functions.
1.4. Operations on fuzzy sets.
1.5. fuzzy relations.
1.6 Operations with fuzzy relations.
1.7. Approximate reasoning. linguistic variables.
1.8. fuzzy propositions.
1.9. Operations with fuzzy propositions.
1.10. Fuzzy if-then rules.
1.11. Operators involvement. fuzzy inference.
1.12. Controller design based on fuzzy logic rules.
1.13. Models Takagi-Sugeno Mandani and-Kang.
2. Modeling and identification systems using fuzzy techniques.
2.1. fuzzy function approximation.
2.2. Fuzzy modeling systems.
2.3. Model types.
2.4. Fuzzy model state of a dynamic system.
2.5. Models Takagi-Sugeno Mandani and-Kang.
2.6. Mandani and TSK fuzzy models equivalent of a classic controller.
2.7. Identification of fuzzy models. Methods.
2.8. Identification of the structure.
2.9. Parameter estimation.
3. Design of fuzzy controllers.
3.1. Design of fuzzy controllers without model.
3.2. PID fuzzy controllers.
3.3. Design of fuzzy model based controllers. Adaptive Methods. methods
direct synthesis. Optimization methods online.
3.4. Fuzzy controller design with matlab.
4. Fundamentals of neural networks.
4.1. Concept artificial neuron. Layers of neurons. Concept of neural network.
4.2. multilayer networks. recurrent networks.
4.3. basic neural networks. Network linear flow: Perceptron and Adaline. Recurrent networks:
Hopfield and Hamming. Learning methods.
4.4. feedforward networks. Learning backpropagation.
4.5. Radial basis functions. Probabilistic networks and networks generalized regression.
4.6. matlab neural networks.
5. Identification of neural network systems
5.1. Function approximation with neural networks.
5.2. Types of system models.
5.3. Modeling systems with neural networks. NN-FIR. NN-ARX. NN-ARMAX, OE-NN, NN-SSIF.
5.4. Types of networks used in modeling. Networks with delay in inner layers. backpropagation
in dynamic systems. 5.1. 5.5. Identification of dynamic systems.
6. Control systems with neural networks.
6.1. Direct control schemes. reverse direct control. Internal model control.
Feedback linearization. feedforward control.
6.2. Indirect control schemes.
7. Fundamentals of optimization and evolutionary algorithms.
7.1 Methods single point optimization.
7.2 Methods based on the derivative: maximum slope, Newton-Raphson, Quasi-Newton,
7.3 non-derivative methods: brute force, random walk, Hooke-Jeeves, Simulated Annealing-.
7.4 multipoint optimization methods.
7.5 Derivative Methods: MultiStart and clustering.
7.6 non-derivative methods: Nelder-Mead, CRS, Genetic Algorithms, Differential Evolution, PSO