An examination on the effect of CVNN parameters while classifying the real-valued balanced and unbalanced data

Acar, Yunus Emre; Ceylan, Murat; Yaldiz, Ercan

doi:10.1109/idap.2018.8620907

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Therefore the real valued input data must be converted to complex domain. This conversion can be done with sample angle-based coding using the equation given below [22].…”

Section: Symmetry Detection Problemmentioning

confidence: 99%

New Complex Valued Activationfunctions: Complex Modifiedswish, Complex E-Swish Andcomplex Flatten-Tswish

Çelebi

Ceylan

2020

ijarcs

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Complex valued neural network (CVNN) has been developed to process complex valued data directly. In CVNN, one of the most important factors is selecting the node's activation function. Choosing the right activation function for each layer is also crucial and may have a significant impact on metric scores and the training speed of the model. This paper introduces three new activation functions for CVNNs which is closely related to the activation function complex swish. These new activation functions are complex modified swish, complex E-swish and complex Flatten-T swish. In order to verify the validity and practicability of the proposed three new activation functions are tested and compared with complex swish activation function on complex valued four bit XOR problem, three inputs symmetry detection and the fading equalization problems. We show that complex E-swish ( β=1.4) has the best overall performance when compared to other networks using complex swish, complex modified swish and complex Flatten-T swish activation functions on the considered tasks.

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“…Therefore the real valued input data must be converted to complex domain. This conversion can be done with sample angle-based coding using the equation given below [22].…”

Section: Symmetry Detection Problemmentioning

confidence: 99%

New Complex Valued Activationfunctions: Complex Modifiedswish, Complex E-Swish Andcomplex Flatten-Tswish

Çelebi

Ceylan

2020

ijarcs

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The New Activation Function for Complex Valued Neural Networks: Complex Swish Function

Çelebi¹,

Ceylan²

2019

4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings

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A Survey of Complex-Valued Neural Networks

Bassey¹,

Qian²,

Li³

2021

Preprint

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Artificial neural networks (ANNs) based machine learning models and especially deep learning models have been widely applied in computer vision, signal processing, wireless communications, and many other domains, where complex numbers occur either naturally or by design. However, most of the current implementations of ANNs and machine learning frameworks are using real numbers rather than complex numbers. There are growing interests in building ANNs using complex numbers, and exploring the potential advantages of the so called complex-valued neural networks (CVNNs) over their real-valued counterparts. In this paper, we discuss the recent development of CVNNs by performing a survey of the works on CVNNs in the literature. Specifically, detailed review of various CVNNs in terms of activation function, learning and optimization, input and output representations, and their applications in tasks such as signal processing and computer vision are provided, followed by a discussion on some pertinent challenges and future research directions.

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An examination on the effect of CVNN parameters while classifying the real-valued balanced and unbalanced data

Cited by 3 publications

References 14 publications

New Complex Valued Activationfunctions: Complex Modifiedswish, Complex E-Swish Andcomplex Flatten-Tswish

New Complex Valued Activationfunctions: Complex Modifiedswish, Complex E-Swish Andcomplex Flatten-Tswish

The New Activation Function for Complex Valued Neural Networks: Complex Swish Function

A Survey of Complex-Valued Neural Networks

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