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Computing Gradient Vector and Jacobian Matrix in Arbitrarily Connected Neural Networks. Author : Bogdan M. Wilamowski , Fellow, IEEE, Nicholas J. Cotton, Okyay Kaynak , Fellow, IEEE, and Günhan Dündar Source : IEEE INDUSTRIAL ELECTRONICS MAGAZINE Date : 2012/3/28 Presenter : 林哲緯. - PowerPoint PPT Presentation
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Computing Gradient Vector and Jacobian Matrix inArbitrarily Connected Neural Networks
Author : Bogdan M. Wilamowski, Fellow, IEEE, Nicholas J. Cotton, Okyay Kaynak, Fellow, IEEE, and Günhan DündarSource : IEEE INDUSTRIAL ELECTRONICS MAGAZINEDate : 2012/3/28Presenter : 林哲緯
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Outline
• Numerical Analysis Method• Neuron Network Architectures• NBN Algorithm
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Minimization problem
Newton's method
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Minimization problem
http://www.nd.com/NSBook/NEURAL%20AND%20ADAPTIVE%20SYSTEMS14_Adaptive_Linear_Systems.html
Steepest descent method
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Least square problem
Gauss–Newton algorithm
http://en.wikipedia.org/wiki/Gauss%E2%80%93Newton_algorithm
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Levenberg–Marquardt algorithm
• Levenberg–Marquardt algorithm– Combine the advantages of Gauss–Newton
algorithm and Steepest descent method– far off the minimum like Steepest descent method– Close to the minimum like Newton algorithm– It’s find local minimum not global minimum
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Levenberg–Marquardt algorithm
• Advantage– Linear– First-order differential
• Disadvantage– inverting is not used at all
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Outline
• Numerical Analysis Method• Neuron Network Architectures• NBN Algorithm
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Weight updating ruleSecond-order algorithmFirst-order algorithm
α : learning constantg : gradient vector
J : Jacobian matrixμ : learning parameterI : identity matrixe : error vector
MLP ACNFCN
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Forward & Backward Computation
Forward : 12345, 21345, 12435, or 21435Backward : 54321, 54312, 53421, or 53412
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Jacobian matrix
Row : pattern(input)*outputColumn : weightp = input numberno = output number
Row = 2*1 = 2Column = 8Jacobin size = 2*8
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Jacobian matrix
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Outline
• Numerical Analysis Method• Neuron Network Architectures• NBN Algorithm
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Direct Computation of Quasi-Hessian Matrix and Gradient Vector
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Conclusion
• memory requirement for quasi-Hessian matrix and gradient vector computation is decreased by(P × M) times
• can be used arbitrarily connected neural networks
• two procedures– Backpropagation process(single output)– Without backpropagation process(multiple outputs)
