Genetic Algorithm with Solved Example(Selection,Crossover,Mutation)
#geneticAlgorithm #neuralNetworks #dataMining
What is Genetic Algorithm?
Flow Chart for the Algorithm
Genetic Operators-Selection, Crossover, Mutation
Solved Example
Introduction:1.1 Biological neurons, McCulloch and Pitts models of neuron, Types
of activation function, Network architectures, Knowledge representation, Hebb net
1.2 Learning processes: Supervised learning, Unsupervised learning and
Reinforcement learning
1.3 Learning Rules : Hebbian Learning Rule, Perceptron Learning Rule, Delta
Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, WinnerTake-All Learning Rule
1.4 Applications and scope of Neural Networks
10
2
Supervised Learning Networks :
2.1 Perception Networks – continuous & discrete, Perceptron convergence theorem,
Adaline, Madaline, Method of steepest descent, – least mean square algorithm,
Linear & non-linear separable classes & Pattern classes,
2.2 Back Propagation Network,
2.3 Radial Basis Function Network.
12
3
Unsupervised learning network:
3.1 Fixed weights competitive nets,
3.2 Kohonen Self-organizing Feature Maps, Learning Vector Quantization,
3.3 Adaptive Resonance Theory – 1
06
4
Associative memory networks:
4.1 Introduction, Training algorithms for Pattern Association,
4.2 Auto-associative Memory Network, Hetero-associative Memory Network,
Bidirectional Associative Memory,
4.3 Discrete Hopfield Networks.
08
5
Fuzzy Logic:
5.1 Fuzzy Sets, Fuzzy Relations and Tolerance and Equivalence
5.2 Fuzzification and Defuzzification
5.3 Fuzzy Controllers
Видео Genetic Algorithm with Solved Example(Selection,Crossover,Mutation) канала btech tutorial
What is Genetic Algorithm?
Flow Chart for the Algorithm
Genetic Operators-Selection, Crossover, Mutation
Solved Example
Introduction:1.1 Biological neurons, McCulloch and Pitts models of neuron, Types
of activation function, Network architectures, Knowledge representation, Hebb net
1.2 Learning processes: Supervised learning, Unsupervised learning and
Reinforcement learning
1.3 Learning Rules : Hebbian Learning Rule, Perceptron Learning Rule, Delta
Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, WinnerTake-All Learning Rule
1.4 Applications and scope of Neural Networks
10
2
Supervised Learning Networks :
2.1 Perception Networks – continuous & discrete, Perceptron convergence theorem,
Adaline, Madaline, Method of steepest descent, – least mean square algorithm,
Linear & non-linear separable classes & Pattern classes,
2.2 Back Propagation Network,
2.3 Radial Basis Function Network.
12
3
Unsupervised learning network:
3.1 Fixed weights competitive nets,
3.2 Kohonen Self-organizing Feature Maps, Learning Vector Quantization,
3.3 Adaptive Resonance Theory – 1
06
4
Associative memory networks:
4.1 Introduction, Training algorithms for Pattern Association,
4.2 Auto-associative Memory Network, Hetero-associative Memory Network,
Bidirectional Associative Memory,
4.3 Discrete Hopfield Networks.
08
5
Fuzzy Logic:
5.1 Fuzzy Sets, Fuzzy Relations and Tolerance and Equivalence
5.2 Fuzzification and Defuzzification
5.3 Fuzzy Controllers
Видео Genetic Algorithm with Solved Example(Selection,Crossover,Mutation) канала btech tutorial
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