Use of a Hysteresis Loop Activation Function to Enable an Analog Perceptron to Gain Memory
Science Journal of Circuits, Systems and Signal Processing
Volume 7, Issue 2, June 2018, Pages: 68-73
Received: May 17, 2018; Accepted: Jun. 14, 2018; Published: Jul. 10, 2018
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William Brickner, Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, USA
Muhammad Sana Ullah, Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, USA
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With the advent of memristors, analog artificial neural networks are closer than ever. Neural computing is growing as a topic of research. In the context of analog artificial neural networks, the purpose of this research is to verify that a perceptron could gain a discrete memory from implementing a hysteresis loop in the activation function. The discrete memory is represented by the difference path of the hysteresis activation function that took from logic 1 to logic 0. To write to the memory, the input to the hysteresis loop would have to exceed threshold. To read the stored value, the input would have to be between the thresholds of the hysteresis function. In order to verify the perceptron’s memory, a network with manually chosen weights is selected which acts as a shift register. The components of this network are assembled in a circuit simulation program. Functionally, the network receives two inputs: a data signal and an enable signal. The output of the network is a time-shifted version of previous input signals. A system whose output is a time-shifted version of the previous inputs is considered to have memory.
Artificial Neural Network, Recurrent Neural Network, Memristors, Hysteresis Loop Activation Function, Analog Computing, Neural Computing, Long Short-Term Memory
To cite this article
William Brickner, Muhammad Sana Ullah, Use of a Hysteresis Loop Activation Function to Enable an Analog Perceptron to Gain Memory, Science Journal of Circuits, Systems and Signal Processing. Vol. 7, No. 2, 2018, pp. 68-73. doi: 10.11648/j.cssp.20180702.14
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