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

Recurrent Neural Networks (RNN) are basically used for applications with time series and sequential data and are currently being used in embedded devices. However, one of their drawbacks is that RNNs have a high computational cost and require the use of a significant amount of memory space. Therefore, computer equipment with a large processing capacity and memory is required. In this article, we experiment with Nonlinear Autoregressive Neural Networks (NARNN), which are a type of RNN, and we use the Discrete Mycorrhizal Optimization Algorithm (DMOA) in the optimization of the NARNN architecture. We used the Mackey-Glass chaotic time series (MG) to test the proposed approach, and very good results were obtained. In addition, some comparisons were made with other methods that used the MG and other types of Neural Networks such as Backpropagation and ANFIS, also obtaining good results. The proposed algorithm can be applied to robots, microsystems, sensors, devices, MEMS, microfluidics, piezoelectricity, motors, biosensors, 3D printing, etc.

Automated summary

Recurrent Neural Networks (RNN) are commonly used for time series and sequential data applications and are currently used in embedded devices. However, RNNs have the drawbacks of high computational cost and memory requirements. This article explores the use of Nonlinear Autoregressive Neural Networks (NARNN), a type of RNN, and applies the Discrete Mycorrhizal Optimization Algorithm (DMOA) to optimize the NARNN architecture. The proposed approach achieves good results when tested with the Mackey-Glass chaotic time series (MG), and comparisons with other methods like Backpropagation and ANFIS also yield positive outcomes. This algorithm has potential applications in various fields including robotics, microsystems, sensors, and 3D printing.