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Artificial Neural Networks - Introduction -

Artificial Neural Networks - Introduction -. Overview. Biological inspiration Artificial neurons and neural networks Application. Biological Neuron.

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Artificial Neural Networks - Introduction -

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  1. Artificial Neural Networks- Introduction -

  2. Overview • Biological inspiration • Artificial neurons and neural networks • Application

  3. Biological Neuron Animals are able to react adaptively to changes in their external and internal environment, and they use their nervous system to perform these behaviours. An appropriate model/simulation of the nervous system should be able to produce similar responses and behaviours in artificial systems.

  4. Biological Neuron The information transmission happens at the synapses.

  5. Artificial neurons Neuron

  6. Artificial neurons x1 x2 x3 … xn-1 xn w1 Output w2 Inputs y w3 . . . wn-1 wn one possible model

  7. Artificial neurons Nonlinear generalization of neuron: y is the neuron’s output, x is the vector of inputs, and w is the vector of synaptic weights. Examples: sigmoidal neuron Gaussian neuron

  8. Other Model Hopfield Retropropagation

  9. 1 1.5 AND 1 1 0.5 OR 1 NOT 0 -1 From Logical Neurons to Finite Automata

  10. Artificial neural networks Output Inputs An artificial neural network is composed of many artificial neurons that are linked together according to a specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs.

  11. Artificial neural networks • Tasks to be solved by artificial neural networks: • controlling the movements of a robot based on self-perception and other information (e.g., visual information); • deciding the category of potential food items (e.g., edible or non-edible) in an artificial world; • recognizing a visual object (e.g., a familiar face); • predicting where a moving object goes, when a robot wants to catch it.

  12. Neural network mathematics Output Inputs

  13. Neural network mathematics Neural network: input / output transformation W is the matrix of all weight vectors.

  14. Learning principle for artificial neural networks ENERGY MINIMIZATION We need an appropriate definition of energy for artificial neural networks, and having that we can use mathematical optimisation techniques to find how to change the weights of the synaptic connections between neurons. ENERGY = measure of task performance error

  15. Perceptron application + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

  16. One or more hidden layers Sigmoid activations functions Multi-Layer Perceptron Output layer 2nd hidden layer 1st hidden layer Input data

  17. A B B A A B B A A B B A Multi-Layer Perceptron Application Types of Decision Regions Result Structure Single-Layer Half Plane Bounded By Hyperplane Two-Layer Convex Open Or Closed Regions Abitrary (Complexity Limited by No. of Nodes) Three-Layer

  18. Conclusion NN have some desadvantages such as: • Preprocessing • Results interpretation by high dimension • Learning phase/Supervised/Non Supervised

  19. References • http://neuron.eng.wayne.edu/software.html Many useful example. • http://ieee.uow.edu.au/~daniel/software/libneural/BPN_tutorial/BPN_English/BPN_English/BPN_English.html • http://www.ai-junkie.com/ • http://diwww.epfl.ch/mantra/tutorial/english/

  20. Demo: OCR

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