Metaheuristic Optimization Techniques
This course explores intelligent systems and evolutionary algorithms, focusing on nature-inspired computational methods.
Neural Networks: Covers supervised/unsupervised learning, feedforward/backpropagation, Hopfield networks, associative memories, LVQ, and RBF networks.
Evolutionary Algorithms: Introduces genetic algorithms, optimization techniques, and self-adaptive learning.
Fuzzy Logic & Hybrid Systems: Explores fuzzy controllers, neuro-fuzzy networks, and fuzzy ARMAP.
Swarm Intelligence: Examines ant colony optimization, particle swarm intelligence, and collective behavior modeling.
Students develop skills in AI-driven problem-solving and optimization, preparing them for applications in robotics, automation, and intelligent systems.
G1: Translate mechatronics systems and robots into mathematical models and simulations.
G3: Perform Multiphysics modeling and simulation for Mechatronics, Robotics, Control, and Embedded systems.
G4: Develop, test, and integrate robotics hardware and software into fully functional systems.
G5: Design, develop, and interface Embedded Systems, ensuring efficient coding and integration.