标题：Robust Prediction Capability of Feed Forward Back Propagation Network over Adaptive Neuro Fuzzy Inference System on Optical Characteristics of Ironbased Superconductor Glass materials
期刊名称：International Journal of Computer Science and Network
印刷版ISSN：2277-5420
出版年度：2018
卷号：7
期号：3
页码：166-181
出版社：IJCSN publisher
摘要：This research intends to establish the dominant prediction potentiality of Feed Forward Back Propagation Networks
over the Adaptive Neuro Fuzzy Inference Systems. The elementary step contains the analysis on the different transfer functions of
Feed Forward Back Propagation Network in order to identify the most convenient transfer function for estimating optical parameters
of Fe doped Glass material: real parts of Absorption Co-efficient and Refractive Index over Wavelength. The motive behind Fe
doping is that it converts Glass material which acts as a Semiconductor to behave as a Superconductor which eventualizes its high
applicability in the field of Telecommunication, Optoelectronics, Laser Based Manufacturing, Biomedical Engineering and more.
Further, Adaptive Neuro Fuzzy Inference System with different membership functions are examined to reach best probable outcome.
The optical properties in the basic experimental dataset are studied using Spin coater and Ultraviolet Spectrometer ranging in
between 29.3 µg/cm2
to 2623 µg/cm2
, whereas, only two samples are considered in this research paper among the given five
samples.
