Artificial Neural Networks is considered as one of the successful machine learning technique to model expert
behavioral systems and it is competent enough to minimize human efforts in manual prediction. This research anticipates the
applicability of the Adaptive Neuro-Fuzzy Modeling approach in the Dielectric Loss prediction. Prediction of Dielectric loss
parameter has wide range of applicability in electric circuits of radio, and television systems. The difficulty lies in Dielectric loss
parameter is its high dependency on the nature of dielectric material and at different frequency. Doped and undoped Terbium
Manganite is chosen as dielectric material to perform comparative analysis. The Adaptive Neuro Fuzzy Inference System, with
its inherent knowledge representation mechanism, nonlinear behavior and adaptive control property can replace almost all basic
predictor neural networks. This hybrid model establishes the superior capability in prediction over Feed forward backpropagation
networks. Finally an analysis is made statistically between Hybrid learning and back-propagation learning
mechanism in Adaptive Neuro-Fuzzy inference system to achieve best suited learning algorithm in dielectric loss prediction.
Published In:IJCSN Journal Volume 7, Issue 2
Date of Publication : April 2018
Pages : 115-125
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
Dibyajyoti Chatterjee : is a final year B.Tech student of Computer
Science and Engineering.
Jitsoma Dey : is a final year B.Tech student of Computer Science
Adaptive Neuro-Fuzzy Systems, Dielectric Loss, Doping, Tolerance, Transfer Functions, Semiconductor
This research initially aims to cater efficient applicability
of Neuro Fuzzy Networks in the diversified province of
Semiconductor in predicting Dielectric Loss parameter
with respect to different temperature at different
frequencies. The primary behavioral analysis is made on
the basis of prediction accuracy percentage, achieved by
Artificial Neural Networks with their Forward Chaining
mechanism to predict dielectric loss parameter of doped
and undoped Terbium Manganites. The research
significantly shows, enhanced performance of ANFIS
with respect to Feed-forward back propagation networks
at initial stages; later ANFIS with Hybrid learning rule is
proven as well suited dielectric loss predictor in the
domain of semiconductor doping. The comparative
analysis made for doped and undoped Terbium
Manganites at Tolerance 0.005 Hybrid Learning
Algorithm of ANFIS performs better than other two
approaches. But at tolerance level up to 0.01 both the
learning Mechanism of ANFIS performs equally; hence
the superiority of Hybrid learning rule in lower tolerance
of ANFIS is clearly visible over the statistical
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