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- Aritifical Intelligence and Fuzzy Logic
Curriculum
- 1 Section
- 32 Lessons
- 10 Weeks
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- Aritifical Intelligence and Fuzzy Logic32
- 2.1Lecture 1: Introduction
- 2.2Lecture 2: Linear Neural networks
- 2.3Lecture 3: Multi layered neural networks
- 2.4Lecture 4: Back Propagation Algorithm revisited
- 2.5Lecture 5: Non linear system analysis – I
- 2.6Lecture 6: Non linear system analysis – II
- 2.7Lecture 7: Radial Basis function networks
- 2.8Lecture 8: Adaptive learning rate
- 2.9Lecture 9: Weight update rules
- 2.10Lecture 10: Recurrent networks Back propagation through time
- 2.11Lecture 11: Recurrent networks Real time recurrent learning
- 2.12Lecture 12: Self organizing Map – Multidimensional networks
- 2.13Lecture 13: Fuzzy sets – A Primer
- 2.14Lecture 14: Fuzzy Relations
- 2.15Lecture 15: Fuzzy Rule base and Approximate Reasoning
- 2.16Lecture 16: Introduction to Fuzzy Logic Control
- 2.17Lecture 17: Neural Control A review
- 2.18Lecture 18: Network inversion and Control
- 2.19Lecture 19: Neural Model of a Robot manipulator
- 2.20Lecture 20: Indirect Adaptive Control of a Robot manipulator
- 2.21Lecture 21: Adaptive neural control for Affine Systems SISO
- 2.22Lecture 22: Adaptive neural control for Affine systems MIMO
- 2.23Lecture 23: Visual Motor Coordination with KSOM
- 2.24Lecture 24: Visual Motor coordination – quantum clustering
- 2.25Lecture 25: Direct Adaptive control of Manipulators – Introduction
- 2.26Lecture 26: NN based back stepping control
- 2.27Lecture 27: Fuzzy Control – a Review
- 2.28Lecture 28: Mamdani type flc and parameter optimization
- 2.29Lecture 29: Fuzzy Control of a pH reactor
- 2.30Lecture 30: Fuzzy Lyapunov controller – Computing with words
- 2.31Lecture 31: Controller Design for a T-S Fuzzy model
- 2.32Lecture 32: Linear controllers using T-S fuzzy model