Abstract

We present a new type of activation functions for a complex-valued neuralnetwork (CVNN). A proposed activation function is constructed such that itfixes a given ellipse. We obtain an application to a complex-valued Hopfieldneural network (CVHNN) using a special form of the introduced complexfunctions as an activation function. Considering the interesting geometricproperties of the plane curve ellipse such as focusing property, weemphasize that these properties may have possible applications in variousneural networks.

Highlights

  • Complex-valued neural networks (CVNN) have been used in various fields such as optoelectronics, imaging, signal processing, quantum neural devices and artificial neural information processing by many researchers

  • We propose a new type of complex-valued functions as an activation function for a complexvalued Hopfield neural network (CVHNN)

  • We propose this function as an activation function for a CVHNN

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Summary

Introduction

Complex-valued neural networks (CVNN) have been used in various fields such as optoelectronics, imaging, signal processing, quantum neural devices and artificial neural information processing by many researchers (see [1,2,3,4,5,6,7,8] for more details). We propose a new type of complex-valued functions as an activation function for a complexvalued Hopfield neural network (CVHNN). These functions fix a given ellipse on the complex plane. It is a well-known fact from geometry that a light ray which leaves a focus c1 of an ellipse will be reflected to other focus c2 (see [10] and [11] for more details) Using this interesting property and the following proposition, Frantz proposed an application to the open problem about trapped reflections described in [12]. We expect that our study will help to generate some new researches and applications on complex-valued neural networks

Complex Functions That Fix an Ellipse
An Application to Complex-Valued Hopfield Neural Networks
Remarks and Conclusion
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