Artificial Neural Networks are well known for their prediction capability that can simulate human expertise efficiently.
Generally Hybrid Neural networks are popular for their greater performance over other popular neural networks. On the other hand
each and every Neural Networks has high dependency on the behavior of prediction dataset. This research tries to find out the
constraint application of Neuro-Fuzzy Networks. A Comparative analysis is made to envision, the prediction capability of Artificial
Neural Networks on dielectric properties of TbMnO3 ceramics doped with Bi and Fe ions in Dielectric Constant prediction where
feed forward back propagation networks outperforms the capability of Neuro-Fuzzy Networks.
Published In:IJCSN Journal Volume 7, Issue 2
Date of Publication : April 2018
Pages : 126-134
Tables : 06
Anindita Das Bhattacharjee : started her career in industry as a
trainee software developer for a year. She has done M. Tech in
Computer Science from National Institute of Technology (NIT),
Durgapur. She secured a position of First Class Second in M.
Tech. Currently she is working in Swami Vivekananda Institute of
Science and Technology. She has been teaching for about 10
years in Computer Science. She is an author of a book “Artificial
Intelligence and Soft Computing for Beginners” published in 2013
and author of book chapters in the book “Intelligent Analysis for
Multimedia Information” published by IGI Global and indexed in
Ananya Ray : is a final year B.Tech student of Computer
Science and Engineering and she was a topper of 3rd
Year Computer Science Department.
Ishita Sengupta : is a final year B.Tech student of
Computer Science and Engineering.
This research finally concludes on the basis of
statistical analysis that there exist no such particular
neural networks that can be highlighted as universal
best predictor. The ANFIS model with its superior
reasoning capability is best suited for dealing with
uncertain or imprecise data. In predicting Dielectric
constant FFBPN proves to be a most suitable predictor.
The analytical survey finds that each and every neural
network and their performance on prediction is truly
dependent on the nature, type, domain of the datasets.
The dielectric constant of Terbium Manganite is one
of the crucial dataset in which Neuro-Fuzzy neural
networks performs less accurately than feed forward
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