Version 1
: Received: 4 May 2023 / Approved: 5 May 2023 / Online: 5 May 2023 (08:59:10 CEST)
How to cite:
Hinojosa-Meza, R.; Montes Rivera, M.; Vacas-Jacques, P.; Escalante-Garcia, N.; Dena-Aguilar, J. A.; Becerra Sanchez, A.; Olvera-Gonzalez, E. Comparative Analysis of RNN Versus IIR Digital Filtering to Optimize Resilience to Dynamic Perturbations in pH Sensing for Vertical Farming. Preprints2023, 2023050334. https://doi.org/10.20944/preprints202305.0334.v1
Hinojosa-Meza, R.; Montes Rivera, M.; Vacas-Jacques, P.; Escalante-Garcia, N.; Dena-Aguilar, J. A.; Becerra Sanchez, A.; Olvera-Gonzalez, E. Comparative Analysis of RNN Versus IIR Digital Filtering to Optimize Resilience to Dynamic Perturbations in pH Sensing for Vertical Farming. Preprints 2023, 2023050334. https://doi.org/10.20944/preprints202305.0334.v1
Hinojosa-Meza, R.; Montes Rivera, M.; Vacas-Jacques, P.; Escalante-Garcia, N.; Dena-Aguilar, J. A.; Becerra Sanchez, A.; Olvera-Gonzalez, E. Comparative Analysis of RNN Versus IIR Digital Filtering to Optimize Resilience to Dynamic Perturbations in pH Sensing for Vertical Farming. Preprints2023, 2023050334. https://doi.org/10.20944/preprints202305.0334.v1
APA Style
Hinojosa-Meza, R., Montes Rivera, M., Vacas-Jacques, P., Escalante-Garcia, N., Dena-Aguilar, J. A., Becerra Sanchez, A., & Olvera-Gonzalez, E. (2023). Comparative Analysis of RNN Versus IIR Digital Filtering to Optimize Resilience to Dynamic Perturbations in pH Sensing for Vertical Farming. Preprints. https://doi.org/10.20944/preprints202305.0334.v1
Chicago/Turabian Style
Hinojosa-Meza, R., Aldonso Becerra Sanchez and Ernesto Olvera-Gonzalez. 2023 "Comparative Analysis of RNN Versus IIR Digital Filtering to Optimize Resilience to Dynamic Perturbations in pH Sensing for Vertical Farming" Preprints. https://doi.org/10.20944/preprints202305.0334.v1
Abstract
We propose an advanced filtering scheme based on Recurrent Neural Networks (RNNs) and Deep Learning to enable efficient control strategies for Vertical Farming (VF) applications. We demonstrate that the best RNN model incorporates five neuron layers, with the first and second containing ninety Long Short-Term Memory neurons. The third layer implements one Gated Recurrent Units neuron. The fourth segment incorporates one RNN network, while the output layer is designed by using a single neuron exhibiting a rectified linear activation function. By utilizing this RNN digital filter, we introduce two variations: (1) A scaled RNN model to tune the filter to the signal of interest, and (2) A moving average filter to eliminate harmonic oscillations of the output waveforms. The RNN models are contrasted with conventional digital Butterworth, Chebyshev I, Chebyshev II, and Elliptic Infinite Impulse Response (IIR) configurations. The RNN digital filtering schemes avoid introducing unwanted oscillations, which makes them more suitable for VF than their IIR counterparts. Finally, by utilizing the advanced features of scaling of the RNN model, we demonstrate that the RNN digital filter can be pH selective, as opposed to conventional IIR filters.
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.