Efficient Nonlinear Autoregressive Neural Network Architecture for Real-Time Biomedical Applications

Olney, Brooks; Mahmud, Shakil; Karam, Robert

doi:10.1109/aicas54282.2022.9869935

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The current literature provides a history of very extensive research on the use of NARNNs in the following areas: The use of NARNN in medical devices such as continuous glucose monitors and drug delivery pumps that are often combined with closed-loop systems to treat chronic diseases, for error detection and correction due to their predictive capabilities [ 42 ]. The use of NARNNs as Chinese e-commerce sales forecasting to develop purchasing and inventory strategies for EC companies [ 43 ], to support management decisions [ 44 ], the effects of air pollution on respiratory morbidity and mortality [ 45 ], the relationship between time series in the economy [ 46 ], to model and forecast the prevalence of COVID-19 in Egypt.…”

Section: Nonlinear Autoregressive Neural Networkmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Nonlinear Autoregressive Neural Networkmentioning

confidence: 99%

“…The use of NARNN in medical devices such as continuous glucose monitors and drug delivery pumps that are often combined with closed-loop systems to treat chronic diseases, for error detection and correction due to their predictive capabilities [ 42 ].…”

Section: Nonlinear Autoregressive Neural Networkmentioning

confidence: 99%

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

et al. 2023

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“…In the neural network training process, the network weight and neuron bias are adjusted iteratively to optimise the accuracy of future value prediction (Sarkar et al, 2019). According to Olney et al (2022), the number r of neurons in the hidden layer defines the configuration of the NARNN model by carrying a weight term for each delay state and a bias term. A number of hidden neurons also accounts for the complexity of a neural network, where a network system with more neurons would be more complex.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia

ABD RAHMAN

2023

JOSMA

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A nonlinear autoregressive neural network (NARNN) model is a feedforward neural network for handling complex nonlinear time series problem. In this study, the tangent sigmoid (tansig) activation function with different number of past values and different number of hidden neurons for NARNN model is determined. The COVID-19 daily confirmed cases in Malaysia are collected with different amount of sample used which are 100, 500 and 900. Therefore, data of 100, 500 and 900 days prior to 21 September 2022 are extracted for the NARNN model training, validation and testing procedure. The lowest average mean squared error (MSE) are considered as the best combination. Result shown that the past value 1:10 and number of neurons of 10 when the sample size is 100. At sample size 500, past values of 1:10 and number of neurons of 8 enables the model to perform at its best. Whereas for sample size 900, network setting of 1:5 past value and 5 hidden neurons gives the least MSE. Multi-step ahead time series forecasting is conducted to forecast the number of COVID-19 confirmed cases in 7 days which is from 22 to 28 September 2022. The result shown for 7-days-ahead confirmed cases forecasting Malaysia datasets, the best forecasting outcome occurs when 900 samples are inputted.

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Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity

Qian,

Cichiwskyj,

Ling

et al. 2024

Lecture Notes in Computer Science

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Efficient Nonlinear Autoregressive Neural Network Architecture for Real-Time Biomedical Applications

Cited by 4 publications

References 10 publications

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

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

Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia

Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity

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