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Questions and Answers
What is the main focus of soft computing?
Which type of reasoning involves drawing conclusions from general rules or facts?
In the context of neural networks, what is the function of a single neuron?
What distinguishes a fuzzy set from a crisp set?
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Which search technique is characterized by exploring the most promising path first?
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What is the fundamental aim of Genetic Algorithm?
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What are the primary components of a fuzzy system?
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Which book is NOT listed as a suggested reading for topics related to soft computing?
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What is a key concept in Genetic Algorithms related to reproduction of solutions?
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Which operator is NOT typically associated with Genetic Algorithms?
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What distinguishes Fuzzification from traditional methods like Genetic Algorithm?
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In the context of fuzzy rule base systems, what is involved in the aggregation of fuzzy rules?
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Study Notes
Course Outcomes
- Explain fundamental concepts of soft computing techniques and their applications
- Analyze various neural network architectures
- Describe fuzzy systems
- Explain fundamentals and operators of Genetic Algorithm
- Identify and select a suitable Soft Computing technology to solve problems
Introduction to Soft Computing
- Soft computing vs. Hard computing
- Various types of Soft Computing Techniques
- Applications of Soft Computing
Artificial Intelligence
- Introduction to Artificial Intelligence
- Types of production systems
- Characteristics of production systems
- Search techniques:
- Breadth First Search
- Depth First Search
- Hill Climbing
- Best First Search
- A* Algorithm
- AO* Algorithms
- Control strategies
- Knowledge representation issues
- Propositional and Predicate Logic
- Monotonic and Non-monotonic Reasoning
- Forward Reasoning and Backward Reasoning
Neural Networks
- Structure and Function of a single neuron:
- Biological Neuron
- Artificial Neuron
- Definition of ANN
- Taxonomy of Neural Net
- Difference between ANN and Human Brain
- Characteristics and Applications of ANN
- Single Layer Network
- Perceptron Training Algorithm
Fuzzy Logic
- Fuzzy set theory
- Fuzzy Set Vs Crisp Set
- Crisp relation & Fuzzy Relations
- Crisp Logic
- Fuzzy Logic
- Features of Membership Functions
- Fuzzy rule base system:
- Fuzzy Propositions
- Formation, Decomposition & Aggregation of Fuzzy Rules
- Fuzzy Reasoning
- Fuzzy Inference Systems
- Fuzzy Decision Making
- Applications of Fuzzy Logic
Genetic Algorithm
- Fundamentals and basic concepts
- Working principle
- Encoding
- Fitness function
- Reproduction
- Genetic modeling:
- Inheritance operator
- Cross over
- Inversion & deletion
- Mutation operator
- Bitwise operator
- Generational Cycle
- Convergence of GA
- Applications and advances in GA
- Fuzzification: Differences & similarities between GA & other traditional methods
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Description
Test your knowledge on the fundamental concepts of soft computing, neural network architectures, fuzzy systems, and Genetic Algorithms as covered in the EC320 Soft Computing course by Rahul Thakur, Assistant Professor at DTU.
