Artificial neural networks

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Artificial neural networks

  1. 1. Artificial Neural Networks http://www.studygalaxy.com/06/12/12
  2. 2. Overview1. Introduction2. Biological inspiration3. Artificial neurons and neural networks4. Learning processes5. Learning with artificial neural networks 06/12/12
  3. 3. Introduction• What is an (artificial) neural network  A set of nodes (units, neurons, processing elements)  Each node has input and output  Each node performs a simple computation by its node function  Weighted connections between nodes called Synaptic Weight.  Connectivity gives the structure/architecture of the net  What can be computed by a NN is primarily determined by the connections and their weights  A very much simplified version of networks of neurons in animal nerve systems 06/12/12
  4. 4. STRUCTURE OF NEURON Each neuron has a body, an axon, and many dendrites Can be in one of the two states: firing and rest. Neuron fires if the total incoming stimulus exceeds the threshold Synapse: thin gap between axon of one neuron and dendrite of another. Signal exchange Synaptic strength/efficiency 06/12/12
  5. 5. Structure of Neuron 06/12/12 BY:RAVI
  6. 6. COMPARISON ANN Bio NN Nodes Cell body input signal from other output neurons firing frequency node function firing mechanism Synapses Connections synaptic strength connection strength06/12/12
  7. 7. Neural Network Architecture Single Layer Feed forward Network Multi Layer Feed forward Network Recurrent Network 06/12/12
  8. 8. Single Layer Feed forward Network Input Layer of source node that projects on to an output layer of nuerons(computation nodes) but not vice-versa. This is refers as a single layer network because processing or computation takes place only on output layer. 06/12/12
  9. 9. Multi Layer Feed Forward NetworkOne or More Hidden Layer are there.Whose Computation nodes are called Hidden neurons orHidden nodes.Function of Hidden neurons is to intermean b/w the externalinput and network output. 06/12/12 BY:RAVI
  10. 10. Recurrent Network At least one feedback loop. Consist of a single layer of neurons. Each neuron feeding its output signal back as inputs of others neurons except itself. May or may not have Hidden neurons. 06/12/12
  11. 11. Biological inspirationAnimals are able to react adaptively to changes in theirexternal and internal environment, and they use their nervoussystem to perform these behaviours.An appropriate model/simulation of the nervous systemshould be able to produce similar responses and behaviours inartificial systems.The nervous system is build by relatively simple units, theneurons, so copying their behavior and functionality should bethe solution. 06/12/12
  12. 12. Biological inspirationDendrites Soma (cell body) Axon 06/12/12
  13. 13. Biological inspiration dendrites axon synapsesThe information transmission happens at the synapses. 06/12/12
  14. 14. Biological inspirationThe spikes travelling along the axon of the pre-synapticneuron trigger the release of neurotransmitter substancesat the synapse.The neurotransmitters cause excitation or inhibition in thedendrite of the post-synaptic neuron.The integration of the excitatory and inhibitory signalsmay produce spikes in the post-synaptic neuron.The contribution of the signals depends on the strength ofthe synaptic connection. 06/12/12
  15. 15. Artificial neural networks OutputInputs 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. 06/12/12
  16. 16. Artificial neural networksTasks 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. 06/12/12
  17. 17. ANN Business ApplicationsEvaluation of personnel and job candidatesResource allocationData miningForeign exchange rateStock, bond, and commodities selection and tradingSignature validationTax fraudLoan applications evaluationSolvency predictionNew product analysisAirline fare managementPrediction 06/12/12

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