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Seminar Neuro-computing

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  • 1. Seminar on UNDER THE GUIDANCE OF Prof. K. E. Ch. Vidyasagar PRESENTED BY Aniket R. JadhaoDr. Bhausaheb Nandurkar College of Engineering& Technology, Yavatmal. 2012-2013
  • 2. Contents Introduction Characteristics of ANN Biological neural networks Backpropogation Algorithm Advantages Applications Conclusion References
  • 3. Introduction Neurocomputing is concerned with information processing A neurocomputing approach to information processing first involves a learning process within a neural network architecture that adaptively responds to inputs according to a learning rule
  • 4. Cont... After the neural network has learned what it needs to know , the trained network can be used to perform certain tasks depending on a particular application Neural networks have the capability to learn from their environment and to adapt to it in an interactive manner.
  • 5. What do you think which is faster? ORA DIGITAL COMPUTER A HUMAN BEING?
  • 6. Here come to answer ...A human being is faster than Digital computer.But why ?How can we perform certain tasks better and fasterthan a digital computer?Do you know ?Difference between brain and a digital computer?
  • 7. Cont.. Neuron Neurons are approximately six orders ofmagnitude slower than silicon logic gates, Howeverthe brain can compensate for the relatively slowoperational speed of the neuron by processing data ina highly parallel architecture that is massivelyinterconnected. It is estimated that the human brainmust contain in the order of 10 raise to power 11neurons and approximately three orders of magnitudemore connections or synapses Therefore, the BRAIN is anadaptive, nonlinear, parallel computer that is capableof organizing neurons to perform certain tasks
  • 8. Cont…Figure: Neuron
  • 9. Cont... Characteristics of ARTIFICIAL NEURAL NETWORKSAbility to learn by example, An ARTIFICIAL NEURALNETWORK stores the knowledge that has beenlearned during the training process in the synapticweights of neurons Ability to generalise
  • 10. BACK-PROPAGATION ALGORITHIM Figure 5: Backpropagation Training
  • 11. Advantages of neurocomputing approachto solving certain problemsAdaptive learning: An ability to learn how to dotasks based on the data.Self-Organization: An ANN can create its ownorganizationReal Time Operation: ANN computations may becarried out in parallel and special hardware devicesare being designed and manufactured which takeadvantage of this capabilityFault Tolerance via Redundant Information Coding:Partial destruction of a network leads to thecorresponding degradation of performance
  • 12. Applications Voice Recognition - Transcribing spoken words intoASCII text Target Recognition - Military application which usesvideo and/or infrared image data to determine if anenemy target is present Medical Diagnosis - Assisting doctors with theirdiagnosis by analyzing the reported symptoms and/orimage data such as MRIs or X-rays Radar –signature classifier
  • 13. ConclusionThen the network is followed by the error generatorat the output, which compares the output of theneuron with the target signal for which the networkhas to be trained. Similarly, there is error generatorat the input, which updates the weights of the firstlayer taking into account the error propagated backfrom the output layer. Finally, a weight transfer unit ispresent just to pass on the values of the updatedweights to the actual weights.
  • 14. REFERNACES[1] Simon Haykin, “Neural Networks”, Second editionby, Prentice Hall of India, 2005.[2] Christos Stergiou and Dimitrios Siganos, “NeuralNetworks”, Computer Science Deptt. University ofU.K., Journal, Vol. 4, 1996.[3] Robert J Schalkoff, “Artificial Neural Networks”,McGraw-Hill International Editions, 1997.[4] Uthayakumar Gevaran, “Back Propagation”,Brandeis University, Department of ComputerScience.[5] Jordan B.Pollack, “Connectionism: Past, Presentand Future”, Computer and Information ScienceDepartment, The Ohio State University, 1998.
  • 15. So, we can see that how neural networks and neurocomputing is beneficial for us. Thank you ! Instead of seeing society as a collection of clearly defined “interest groups”, society must bereconceptualise as a complex network of groups of interacting individuals whose membership andcommunication pattern are seldom confined to one such group alone

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