Learning Flexible Neural Networks for Pattern Recognition
Learning the gradient of neuron's activity function
like the weight of links causes a new specification which is
flexibility. In flexible neural networks because of supervising and
controlling the operation of neurons, all the burden of the learning is
not dedicated to the weight of links, therefore in each period of
learning of each neuron, in fact the gradient of their activity function,
cooperate in order to achieve the goal of learning thus the number of
learning will be decreased considerably.
Furthermore, learning neurons parameters immunes them against
changing in their inputs and factors which cause such changing.
Likewise initial selecting of weights, type of activity function,
selecting the initial gradient of activity function and selecting a fixed
amount which is multiplied by gradient of error to calculate the
weight changes and gradient of activity function, has a direct affect
in convergence of network for learning.
[1] A. Mirzaaghazadeh, H. Motameni, "Using Neural Network in Pattern
Recognition", Proceeding of Iran Computer Conference, 2002.
[2] Kamarthi S.V., Pittner S. , Accelerating neural network traning using
weight extrapolation, Neural networks, 9, 1999, pp. 1285-1299.
[3] A. Burak Goktepe, "Role of Learning Algorithm in Neural Network-
Based Back calculation of Flexible Pavements", Journal of Computing
in Civil Engineering, Volume 20, Issue 5, pp. 370-373
(September/October 2006).
[4] Manfred M Fisher, "Neural Networks: A General Framework for Non-
Linear Function Approximation", Transactions in GIS, Volume 10 Page
521 - july 2006, doi:10.1111/j.1467-9671.2006.01010.x, Volume 10
Issue 4.
[5] V. Maiorov, "Approximation by neural networks and learning theory",
Journal of Complexity, Volume 22, Issue 1, Februery 2006, Pages 102-
117.
[6] Salvatore Cavalieri, "A novel learning algorithm which improves the
partial fault tolerance of multilayer neural networks", Neural Networks,
Volume 12, Issue 1, January 1999, Pages 91-106.
[7] Mohammad Teshnehlab and Keigo Watanabe (Eds.), "Intelligent
control based on flexible neural networks", Kluwer Academic
Publishers, Dordrecht, The Netherlands, 1999, ISBN 0-7923 -5683-7,
Automatica, Volume 38, Issue 3, March 2002, Pages 564-565.
[8] Edgar Rinast, HansDieter Weiss, "Neural network approach
computer=assisted interpretation of ultrasound images of the
gallbladder", Europen Journal of Radiology, Volume 17, Issue 3,
November 1993, Pages 175-178.
[9] K. Economou and D. Lymberopoulos, "A new perspective in learning
pattern generation for teaching neural networks", Neural Networks,
Volume 12, Issue 4-5, June 1999, Pages 767-775.
[10] Eiji Mizutani and James W. Demmel, "On structure-exploiting trustregion
regularized nonlinear least squares algorithms for neural-network
learning", Neural Networks, Volume 16, Issue 5-6, June-July 2003,
pages 745-753.
[11] R.Vicente Ruiz de angulo and Carme Torras, "Neural learning methods
yielding functional invaiance", Theoretical Computer Science, Volume
320, Issue 1, 12 June 2004, Pages 111-121.
[12] Solanki Gautam, "Neural network and its application in pattern
recognition", Seminar Report of Department of Computer Science and
Engg. Indian Institue of Technology, Bombay, November 5, 2004.
[13] Alexander J. Faaborg, "Using neural networks to create an adaptive
character recognition system", March 2002,
http://web.media.mit.edu/~faaborg/research/cornell/hci_neuralnetwork_f
inalpaper.pdf
[14] O. Lezray, D. Fournier and H. Cardot, "Neural network induction graph
for pattern recognition", Neurocomputing 57 (2004) 257-274.
[1] A. Mirzaaghazadeh, H. Motameni, "Using Neural Network in Pattern
Recognition", Proceeding of Iran Computer Conference, 2002.
[2] Kamarthi S.V., Pittner S. , Accelerating neural network traning using
weight extrapolation, Neural networks, 9, 1999, pp. 1285-1299.
[3] A. Burak Goktepe, "Role of Learning Algorithm in Neural Network-
Based Back calculation of Flexible Pavements", Journal of Computing
in Civil Engineering, Volume 20, Issue 5, pp. 370-373
(September/October 2006).
[4] Manfred M Fisher, "Neural Networks: A General Framework for Non-
Linear Function Approximation", Transactions in GIS, Volume 10 Page
521 - july 2006, doi:10.1111/j.1467-9671.2006.01010.x, Volume 10
Issue 4.
[5] V. Maiorov, "Approximation by neural networks and learning theory",
Journal of Complexity, Volume 22, Issue 1, Februery 2006, Pages 102-
117.
[6] Salvatore Cavalieri, "A novel learning algorithm which improves the
partial fault tolerance of multilayer neural networks", Neural Networks,
Volume 12, Issue 1, January 1999, Pages 91-106.
[7] Mohammad Teshnehlab and Keigo Watanabe (Eds.), "Intelligent
control based on flexible neural networks", Kluwer Academic
Publishers, Dordrecht, The Netherlands, 1999, ISBN 0-7923 -5683-7,
Automatica, Volume 38, Issue 3, March 2002, Pages 564-565.
[8] Edgar Rinast, HansDieter Weiss, "Neural network approach
computer=assisted interpretation of ultrasound images of the
gallbladder", Europen Journal of Radiology, Volume 17, Issue 3,
November 1993, Pages 175-178.
[9] K. Economou and D. Lymberopoulos, "A new perspective in learning
pattern generation for teaching neural networks", Neural Networks,
Volume 12, Issue 4-5, June 1999, Pages 767-775.
[10] Eiji Mizutani and James W. Demmel, "On structure-exploiting trustregion
regularized nonlinear least squares algorithms for neural-network
learning", Neural Networks, Volume 16, Issue 5-6, June-July 2003,
pages 745-753.
[11] R.Vicente Ruiz de angulo and Carme Torras, "Neural learning methods
yielding functional invaiance", Theoretical Computer Science, Volume
320, Issue 1, 12 June 2004, Pages 111-121.
[12] Solanki Gautam, "Neural network and its application in pattern
recognition", Seminar Report of Department of Computer Science and
Engg. Indian Institue of Technology, Bombay, November 5, 2004.
[13] Alexander J. Faaborg, "Using neural networks to create an adaptive
character recognition system", March 2002,
http://web.media.mit.edu/~faaborg/research/cornell/hci_neuralnetwork_f
inalpaper.pdf
[14] O. Lezray, D. Fournier and H. Cardot, "Neural network induction graph
for pattern recognition", Neurocomputing 57 (2004) 257-274.
@article{"International Journal of Information, Control and Computer Sciences:65004", author = "A. Mirzaaghazadeh and H. Motameni and M. Karshenas and H. Nematzadeh", title = "Learning Flexible Neural Networks for Pattern Recognition", abstract = "Learning the gradient of neuron's activity function
like the weight of links causes a new specification which is
flexibility. In flexible neural networks because of supervising and
controlling the operation of neurons, all the burden of the learning is
not dedicated to the weight of links, therefore in each period of
learning of each neuron, in fact the gradient of their activity function,
cooperate in order to achieve the goal of learning thus the number of
learning will be decreased considerably.
Furthermore, learning neurons parameters immunes them against
changing in their inputs and factors which cause such changing.
Likewise initial selecting of weights, type of activity function,
selecting the initial gradient of activity function and selecting a fixed
amount which is multiplied by gradient of error to calculate the
weight changes and gradient of activity function, has a direct affect
in convergence of network for learning.", keywords = "Back propagation, Flexible, Gradient, Learning,
Neural network, Pattern recognition.", volume = "1", number = "9", pages = "2892-4", }
