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  A Constraint in Neuro Fuzzy Approach on Prediction of Dielectric Constant of Doped and Undoped TbMnO3  
  Authors : Anindita Das Bhattacharjee; Ananya Ray; Ishita Sengupta
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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

Figures :16

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 Scopus.

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.


Adaptive Neuro Fuzzy Systems, Dielectric Constant, Doping, Tolerance, Transfer Functions, Semiconductor

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 back propagation.


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