A Schmitt Trigger Based Oscillatory Neural Network for Reservoir Computing
Journal of Electrical and Electronic Engineering
Volume 8, Issue 1, February 2020, Pages: 1-9
Received: Nov. 15, 2019; Accepted: Dec. 2, 2019; Published: Jan. 4, 2020
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Authors
Ting Zhang, Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, the United States
Mohammad Rafiqul Haider, Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, the United States
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Abstract
With the increase in communication bandwidth and frequency, the development level of communication technology is also constantly developing. The scale of the Internet of Things (IoT) has shifted from single point-to-point communication to mesh communication between sensors. However, the large sensors serving the infrastructure place a burden on real-time monitoring, data transmission, and even data analysis. The information processing method is experimentally demonstrated with a non-linear Schmitt trigger oscillator. A neuronally inspired concept called reservoir computing has been implemented. The synchronization frequency prediction tasks are utilized as benchmarks to reduce the computational load. The oscillator's oscillation frequency is affected by the sensor input, further affecting the storage pattern of the oscillatory neural network. This paper proposes a method of information processing by training and modulating the weights of the intrinsic electronic neural network to achieve the next step prediction. The effects on the frequency of a single oscillator in a coupled oscillatory neural network are studied under asynchronous and synchronization modes. Principle Component Analysis (PCA) is used to reduce the data dimension, and Support Vector Machine (SVM) is used to classify the synchronous and asynchronous data. We define that oscillator with stronger coupling weight (lower coupling resistance) as a leader oscillator. From the spice simulation, when OSC1 and OSC2 work as leader oscillator, the ONN almost always achieve synchronization; and the synchronization frequency is close to the average value of the leader oscillators. By training the emerging synchronous and asynchronous data, we can predict the synchronization status of an unknown dataset. Weight retrieval can be achieved by adjusting the slope and bias of the separation boundary.
Keywords
Schmitt Trigger Oscillator, Reservoir Computing, Coupling Weight, Synchronization Frequency
To cite this article
Ting Zhang, Mohammad Rafiqul Haider, A Schmitt Trigger Based Oscillatory Neural Network for Reservoir Computing, Journal of Electrical and Electronic Engineering. Vol. 8, No. 1, 2020, pp. 1-9. doi: 10.11648/j.jeee.20200801.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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