NEURAL NETWORKS
Theory
Biological Aspect
Artificial Neuron
Neuron Networks
Neuron Network Learning
Supervised Learning
Unsupervised Learning
Learning using the Rule of Perceptron
The
Rule
Generalized
Rule
Linear Function
Nonlinear Continuous Function
Weights Modification
Not Supervised Learning
Traditional Algorithm
Example of Choice of Initial Vectors
Learning Order
The Conterpropagation
The Fuzzy Neurons
Fuzzy Neural Network
Neural Networks for Knowledge Representation
Overview of the System
Knowledge Representation
The Learning Step
The System Operation
Applications
Potentiality Maps for Irrigation
Palni Application
Conclusion
Concluding Remarks
IRIT-UPS
Rule