Fuzzy Logic and Neural Network
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Follow on Instagram for UpdatesFuzzy Logic and Neural Network Syllabus — Unit-wise Content
The official GTU curriculum for Fuzzy Logic and Neural Network (subject code ME02062071) as prescribed for IOT · Semester 2 divides the course into 6 units, covering 30 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME02062071)Unit 1 Introduction to Fuzzy Sets: Crisp sets – notations,
- Fuzzy Set – Representation
- difference between crisp set and fuzzy sets
- definitions in fuzzy sets
- operations and properties
- Fuzzy Reasoning and Clustering: Introduction
- fuzzy logic controller – two major forms
- sensitivity analysis
- advantages and disadvantages. fuzzy clustering – fuzzy C-means clustering
- entropy based fuzzy clustering
Unit 2 Fundamentals of Neural Networks: Introduction –
- biological neuron
- artificial neuron
- a layer of neurons
- multiple layers of neurons
- Static Vs dynamic neural networks
- training of neural networks – supervised learning
- unsupervised learning
- incremental training
- batch training
Unit 3 Some Examples of Neural Networks: Introduction, Multi-
- layer feed-forward neural network
- Radial basis function network
- Self-organizing map, recurrent neural networks
Unit 4 Combined Generic Algorithm – Fuzzy Logic
- Fuzzy-Genetic Algorithm
- Genetic-Fuzzy System – A brief literature review
- working principle of Genetic-Fuzzy Systems
Unit 5 Combined Genetic Algorithms – Neural Networks
- Working Principle of a Genetic-Neural system – forward calculation
- A Hand Calculation
Unit 6 Combined Neural Networks: Fuzzy Logic: Introduction,
- Neuro-Fuzzy Working based on Mamdani Approach – Tuning of the Neuro-Fuzzy system using a back-propagation algorithm
- tuning of the Neuro-Fuzzy system using a Genetic algorithm
- A numerical example
- Neuro-Fuzzy system based on Takagi and Sugeno’s Approach – tuning of the ANFIS using a Generic Algorithm
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