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Decoding Natural Behavior from Neuroethological Embedding

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Adaptive complex-valued stepsize based fast learning of complex-valued neural networks.

Yongliang Zhang¹, He Huang¹

¹School of Electronics and Information Engineering, Soochow University, Suzhou 215006, PR China.

Neural Networks : the Official Journal of the International Neural Network Society

|February 5, 2020

Summary

This summary is machine-generated.

This study introduces an adaptive complex-valued stepsize for training complex-valued neural networks. The method enhances convergence and accuracy by enabling faster escape from saddle points.

Keywords:

Adaptive complex-valued stepsize Complex-valued neural networks Fast learning Rotation factors Saddle points Scaling factors

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Area of Science:

Artificial Intelligence
Machine Learning
Neural Networks

Background:

Complex-valued gradient descent is crucial for complex-valued neural networks.
Determining an appropriate learning stepsize remains a significant challenge.

Purpose of the Study:

To propose an adaptive complex-valued stepsize design method.
To improve the training efficiency and performance of complex-valued neural networks.

Main Methods:

Generalizing the adaptable learning rate tree technique to the complex domain.
Introducing scaling and rotation factors to adjust stepsize amplitude and phase.
Analyzing algorithm dynamics near saddle points.

Main Results:

Expanded search range from half line to half plane for better search direction.
Demonstrated ease of escaping saddle points for fast convergence.
Achieved high accuracy in function approximation and pattern classification tasks.

Conclusions:

The proposed adaptive complex-valued stepsize method offers significant advantages.
It improves convergence speed and accuracy in complex-valued neural network training.
The method shows superior performance compared to existing algorithms.