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Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

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Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction

Hector Carreon-Ortiz¹, Fevrier Valdez¹, Patricia Melin¹

¹Tijuana Institute of Technology, TecNM, Tijuana 22379, Mexico.

|January 21, 2023

Summary

This summary is machine-generated.

This study introduces a new optimization algorithm for Nonlinear Autoregressive Neural Networks (NARNNs), reducing computational costs for time series analysis in embedded devices. The approach shows promising results for applications in robotics and sensor technology.

Keywords:

Mackey–Glass nonlinear autoregressive neural networks optimization

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Recurrent Neural Networks (RNNs) are essential for time series and sequential data but suffer from high computational and memory demands.
This limits their application in resource-constrained embedded devices.

Purpose of the Study:

To optimize Nonlinear Autoregressive Neural Network (NARNN) architecture for improved efficiency.
To evaluate the proposed optimization method using the Mackey-Glass chaotic time series.

Main Methods:

Implemented Nonlinear Autoregressive Neural Networks (NARNNs), a type of RNN.
Utilized the Discrete Mycorrhizal Optimization Algorithm (DMOA) for NARNN architecture optimization.
Tested the approach on the Mackey-Glass (MG) chaotic time series.

Main Results:

Achieved very good results in optimizing NARNNs for time series prediction.
Demonstrated competitive performance compared to Backpropagation and ANFIS methods on the MG dataset.

Conclusions:

The DMOA-optimized NARNN approach offers an efficient solution for time series analysis in embedded systems.
Potential applications span diverse fields including robotics, sensors, MEMS, and 3D printing.