ControlSystems.jl: A Control Toolbox in Julia

Carlson, Fredrik Bagge; Fält, Mattias; Heimerson, Albin; Troeng, Olof

doi:10.1109/cdc45484.2021.9683403

Cited by 9 publications

(5 citation statements)

References 7 publications

(6 reference statements)

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“…Second, we run ModalSTEL on each resulting forest. In particular, ModalSTEL operates a hyperparameter search, thanks to Hyperopt.jl [8], on the first forest, with an internal two-split cross-validation on the training set only, and then used the obtained parametrization on all 10 forests. The evolutionary part was realized by instantiating NSGA-II to this task, implemented in Evolutionary.jl [46].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Evolutionary Explainable Rule Extraction from (Modal) Random Forests

Ghiotti,

Manzella,

Pagliarini

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Symbolic learning is the subfield of machine learning concerned with learning predictive models with knowledge represented in logical form, such as decision tree and decision list models. Ensemble learning methods, such as random forests, are usually deployed to improve the performance of decision trees; unfortunately, interpreting tree ensembles is challenging. In order to deal with unstructured (e.g., temporal or spatial) data, moreover, decision trees and random forests have been recently generalized to the use of modal logics, which are harder to interpret than their propositional counterpart. Recently, a methodology for extracting simple rules from propositional random forests, based on a sequence of optimization steps, was proposed. In this work, we generalize this approach along two directions: from propositional to modal logic and from a sequence of optimization steps to a single multi-objective optimization problem. Even if confined to the temporal domain, our experimental results, based on open-source implementations and public data, show that our method is robust and able to extract small, accurate, and informative decision lists even for complex classification problems.

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Section: Experiments and Resultsmentioning

confidence: 99%

Evolutionary Explainable Rule Extraction from (Modal) Random Forests

Ghiotti,

Manzella,

Pagliarini

et al. 2023

Frontiers in Artificial Intelligence and Applications

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show abstract

“…The model coding, training, testing, and optimization were conducted in the Julia programing language. More specifically, the “Flux.jl” machine learning framework 35 was employed for NN implementation, and “Hyperopt.jl” 36 package for BOHB implementation. The training was conducted on a machine with a hardware configuration of an Intel Core i7-7700HQ CPU @ 2.80GHz Ã— 8, 16 GB RAM, and an NVIDIA GeForce GTX 1050 for GPU acceleration.…”

Section: Proposed Methodologymentioning

confidence: 99%

Deep long short-term memory neural networks as virtual sensors for marine diesel engine NOx prediction at transient conditions

Karystinos,

Papalambrou

2023

International Journal of Engine Research

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Virtual Sensors based on deep-learning models for predicting the NOx emissions of a Diesel Engine under transient conditions were developed and verified. Raw data from laboratory experimental measurements, under marine transient loading cycles, were used for training and evaluation of the developed models. NOx prediction under transient conditions is often inaccurate by implementing conventional methods since they fail to capture the dynamic behavior of internal combustion engines. The proposed model is based on Long Short-Term Memory (LSTM) Networks. A Deep Feed-forward Neural Network (DFNN) was also developed to validate the LSTM. The LSTM input is a time sequence of past measurements of the inputs while the DFNN only uses the most recent measurements. The Bayesian Hyberband Optimization (BOHB) algorithm determined the structure and parameters of each network. Each model uses the same inputs and is directly derived from the engine ECU. The LSTM validation showed that the model can generalize and accurately predict the NOx emissions under transient loading compared to the DFNN.

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“…To ensure that the initial conditions of the ODE lead a to feasible flux V p for the FBA problem, we created a warm-up routine which iteratively tries various initial conditions using a Bayesian optimization method 67 .…”

Section: Warm-up Routine For Initial Conditionsmentioning

confidence: 99%

Modelling dynamic host-pathway interactions at the genome scale

Merzbacher,

Aodha,

Oyarzún

2024

Preprint

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Pathway engineering offers a promising avenue for sustainable chemical production. The design of efficient production systems requires understanding complex host-pathway interactions that shape the metabolic phenotype. While genome-scale metabolic models are widespread tools for studying static host-pathway interactions, it remains a challenge to predict dynamic effects such as metabolite accumulation or enzyme overexpression during the course of fermentation. Here, we propose a novel strategy to integrate kinetic pathway models with genome-scale metabolic models of the production host. Our method enables the simulation of the local nonlinear dynamics of pathway enzymes and metabolites, informed by the global metabolic state of the host as predicted by Flux Balance Analysis (FBA). To reduce computational costs, we make extensive use of surrogate machine learning models to replace FBA calculations, achieving simulation speed-ups of at least two orders of magnitude. Through case studies on two production pathways inEscherichia coli, we demonstrate the consistency of our simulations and the ability to predict metabolite dynamics under genetic perturbations and various carbon sources. We showcase the utility of our method for screening dynamic control circuits through large-scale parameter sampling and mixed-integer optimization. Our work links together genome-scale and kinetic models into a comprehensive framework for computational strain design.

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ControlSystems.jl: A Control Toolbox in Julia

Cited by 9 publications

References 7 publications

Evolutionary Explainable Rule Extraction from (Modal) Random Forests

Evolutionary Explainable Rule Extraction from (Modal) Random Forests

Deep long short-term memory neural networks as virtual sensors for marine diesel engine NOx prediction at transient conditions

Modelling dynamic host-pathway interactions at the genome scale

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