Analog Circuits Based Fault Diagnosis using ANN and SVM
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.2.19Keywords:
Artificial Neural Networks, Kernel Principal Component Analysis, Support Vector Machine.Dimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In this study, we provide a technique for identifying analog errors using a neural network and an SVM (SVM). The study's major objective is to produce a trustworthy diagnostic based on a technique that reduces testing durations by resolving the problem of component tolerances.The suggested strategy uses an artificial neural network and a backward propagation mechanism. The impact of methods like Principal Component Analysis on feature extraction is discussed in this work. The simulation results show that the technique is effective and efficient for fault identification in tolerant mixed-signal circuits.Abstract
How to Cite
Downloads
Similar Articles
- Jhankar Moolchandani, Kulvinder Singh, English language analysis using pattern recognition and machine learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- G. Hemamalini, V. Maniraj, Enhanced otpmization based support vector machine classification approach for the detection of knee arthritis , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- S. Vanaja, Hari Ganesh S, Application of data mining and machine learning approaches in the prediction of heart disease – A literature survey , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Mohamed Azharudheen A, Vijayalakshmi V, Improvement of data analysis and protection using novel privacy-preserving methods for big data application , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Pallavi M. Shimpi, Nitin N. Pise, Comparative Analysis of Machine Learning Algorithms for Malware Detection in Android Ecosystems , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- M. Jayakandan, A. Chandrabose, An ensemble-based approach for sentiment analysis of covid-19 Twitter data using machine learning and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Surendra Singh Bisht, Saurabh Charaya, Rachna Mehta, A Comparative and Hybrid Machine Learning Framework for IoT-Based Predictive Maintenance of Rotating Machinery , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- S. Udhaya Priya, M. Parveen, ETPPDMRL: A novel approach for prescriptive analytics of customer reviews via enhanced text parsing and reinforcement learning , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- A. Tamilmani, K. Muthuramalingam, An enhanced support vector machine bbased multiclass classification method for crop prediction , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Olivia C. Gold, Jayasimman Lawrence, Ensemble of CatBoost and neural networks with hybrid feature selection for enhanced heart disease prediction , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
