Intelligent systems and control
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Follow on Instagram for UpdatesIntelligent systems and control Syllabus — Unit-wise Content
The official GTU curriculum for Intelligent systems and control (subject code ME01067021) as prescribed for IACI · Semester 1 divides the course into 7 units, covering 77 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 (ME01067021)Unit 1 INTRODUCTION
- Neural Networks
- Rationale for Using NN in Engineering
- Fuzzy Logic Control
- Rationale for Using FL in Engineering
- Evolutionary Computation
- Hybrid Systems
Unit 2 FUNDAMENTALS OF NEURAL NETWORKS
- Basic Structure of a Neuron
- Model of Biological Neurons
- Elements of Neural Networks
- Weighting Factors
- Activation Function
- Linear Separable Patterns
- Single Layer Perceptron
- Program Name: Engineering Level: PG Branch: Applied Instrumentation Course / Subject Code :ME01067021 Course / Subject Name : Intelligent systems and control w.e.f. 2024-25 http://syllabus.gtu.ac.in/ Page no. 1 of 2 General Architecture
- Linear Classification
- Perceptron Algorithm
- Multi-Layer Perceptron
- General Architecture
- Input-Output Mapping
- XOR Realization
Unit 3 NEURAL NETWORK ARCHITECTURES
- NN Classifications
- Feedforward and feedback networks
- Supervised and Unsupervised Learning Networks
- Back Propagation Algorithm
- Delta Training Rule
- Radial Basis Function Network (RBFN)
- Training of the Kohonen Network
- Examples of Self-Organization
- Hopfield Network
Unit 4 INTRODUCTION TO FUZZY SETS
- BASIC DEFINITIONS AND RELATIONS Introduction
- Classical Sets
- Classical Set Operations
- Properties of Classical Sets
- Fuzzy Sets
- Fuzzy Membership Functions
- Fuzzy Set Operations
- Properties of Fuzzy Sets
- Alpha-Cut Fuzzy Sets
- Extension Principle
- Classical Relations vs
- Fuzzy Relations
Unit 5 INTRODUCTION TO FUZZY LOGIC
- Predicate Logic
- Deductive Inferences
- Fuzzy Logic
- Approximate Reasoning
Unit 6 UZZY CONTROL AND STABILITY
- Basic Definitions
- Inference Engine
- Fuzzy Control Design
- Analysis of Fuzzy Control Systems
- Stability of Fuzzy Control Systems
- Lyapunov Stability
- Stability via Interval Matrix Method
Unit 7 INTELLIGENT AUTO TUNING OF PID CONTROLLLER
- Process Reaction Curve and Relay Methods Identification and PID Tuning
- Developing Simple Models from the Process Reaction
- Identification Algorithm for Oscillatory Step Responses
- Identification Algorithm for Non-Oscillatory Responses Without Overshoot
- Developing Simple Models from a Relay Feedback Experiment
- On- line Identification of FOPDT Models
- On-line Identification of SOPDT Models
- Examples for the On-line Relay Feedback Procedure
- Off-line Identification
- An Inverse Process Model-Based Design Procedure for PID Control
- Inverse Process Model-Based Controller Principles
- PI/PID Controller Synthesis
- Auto tuning of PID Controllers
- Assessment of PI/PID Control Performance
- Achievable Minimal IAE Cost and Rise Time
- Assessment of PI/PID Controllers 6 25% FUZZY LOGIC AND GENETIC ALGORITHM METHODS IN PID TUNING: Introduction
- Fuzzy PID Controller Design
- Fuzzy PI Controller Design
- Fuzzy D Controller Design
- Fuzzy Control Rules
- A Control Example
- Multi-Objective Optimised Genetic Algorithm Fuzzy PID Control
- Genetic Algorithm Methods Explained
- Case study A: MultiObjective Genetic Algorithm Fuzzy PID Control of a Nonlinear Plant
- Case study B: Control of Solar Plant
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