Fault Diagnosis in Mixed-Mode Circuit By using Artificial Neural Network Method
S. Ramya1, M.Meenaa Kumari2, R. Hema3
1S.Ramya, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2M.Meenaa Kumari, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
3R.Hema, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 14 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 10 October 2019 | PP: 415-418 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F11170886S219/19©BEIESP | DOI: 10.35940/ijeat.F1117.0886S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In these work it is said that artificial systems are connected to finding of calamitous imperfections in the advanced piece of a nonlinear blended mode circuit. The methodology is exhibited on the case of a moderately mind boggling sigma-delta modulator. A lot of shortcomings are chosen first. At that point, issue lexicon is made, by reproduction, utilizing the reaction of the loop path to an info incline flag. This spoken to type of a carry-into table. Counterfeit neural system is then prepared for displaying (retaining) the look-into table. The conclusion is carried out so the artificial neural network is energized by broken reactions so as to introduce the deficiency codes at its yield. There were no blunders in recognizing the shortcomings amid conclusion.
Keywords: Fault Diagnosis, Artificial Neural Network.
Scope of the Article: Artificial Intelligence and Machine Learning