Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition

Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition

Optimum thermal annealing process operating condition for nanostructured porous silicon (nPSi) by using radial basis function neural network (RBFNN) was proposed. The nanostructured porous silicon (nPSi) layer samples prepared by electrochemical etching process (EC) of p-type silicon wafers under di...

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Bibliographic Details
Main Authors: M. M., Yusoff, Mehdi, Qasim, Al-Dabbagh, Jinan B., Abdalla, Ahmed N., Hegde, Gurumurthy
Format: Article
Language:English
Published: scientific.net 2013
Subjects:
Q Science (General)
Online Access:http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/1/Radial_Basis_Function_Neural_Network_Model_for_Optimizing_Thermal_Annealing_Process_Operating_Condition.pdf

Internet

http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/
http://umpir.ump.edu.my/id/eprint/4705/1/Radial_Basis_Function_Neural_Network_Model_for_Optimizing_Thermal_Annealing_Process_Operating_Condition.pdf

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