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Neural Networks for Eye Height and Eye Width Prediction with an Improved Adaptive Sampling Algorithm

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Modeling, Design and Simulation of Systems (AsiaSim 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 751))

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Abstract

This paper discusses the application of artificial neural networks (ANN) in terms of eye diagram modeling, where ANN models are trained to predict the eye height and eye width, which are useful information for signal integrity inspection. This paper also presents an improved version of the adaptive sampling method which is used in the data collection process. The proposed adaptive sampling manages to reduce the number of training and testing samples needed to train the neural model, thus reducing the time needed to simulate the data. In addition, the proposed adaptive sampling can generate training and testing samples more evenly across the whole design space, reducing the risk of oversampling and undersampling.

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Acknowledgments

This work is supported by the Universiti Sains Malaysia under the Research University Grant (RUI) grant no. 1001/PELECT/8014011.

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Correspondence to Patrick Goh .

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© 2017 Springer Nature Singapore Pte Ltd.

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Goay, C.H., Goh, P. (2017). Neural Networks for Eye Height and Eye Width Prediction with an Improved Adaptive Sampling Algorithm. In: Mohamed Ali, M., Wahid, H., Mohd Subha, N., Sahlan, S., Md. Yunus, M., Wahap, A. (eds) Modeling, Design and Simulation of Systems. AsiaSim 2017. Communications in Computer and Information Science, vol 751. Springer, Singapore. https://doi.org/10.1007/978-981-10-6463-0_17

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  • DOI: https://doi.org/10.1007/978-981-10-6463-0_17

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  • Online ISBN: 978-981-10-6463-0

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