An algorithm to determine sample sizes for optimization with artificial neural networks

Nuchitprasittichai, Aroonsri; Cremaschi, Selen

doi:10.1002/aic.13871

Cited by 51 publications

(31 citation statements)

References 35 publications

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“…In general, there is no rigorous, all‐encompassing analysis of surrogate model selection, sampling strategy, and underlying model; however, several groups actively pursuing various pieces of this puzzle, e.g., Boukouvala et al. , Nuchitprasittichai and Cremaschi , Eason and Cremaschi , Sikorski et al. , Cozad et al.…”

Section: Surrogate Modelingmentioning

confidence: 99%

“…In general, there is no rigorous, all-encompassing analysis of surrogate model selection, sampling strategy, and underlying model; however, several groups actively pursuing various pieces of this puzzle, e.g., Boukouvala et al [10], Nuchitprasittichai and Cremaschi [24], Eason and Cremaschi [25], Sikorski et al [11], Cozad et al [26,27], Wang and Ierapetritou [12], Bhosekar and Ierapetritou [7], Garud et al [8,22]. An overall discussion of current progress in these areas of surrogate modeling is presented by Bartz-Beielstein and Zaefferer [28].…”

Section: Design Of Experiments For Surrogate Modelingmentioning

confidence: 99%

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Overview of Surrogate Modeling in Chemical Process Engineering

McBride

Sundmacher

2019

Chemie Ingenieur Technik

211

108

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The ability to accurately model and simulate chemical processes has been paramount to the growing success and efficiency in process design and operation. These improvements usually come with increasing complexity of the underlying models leading to substantial computational effort in their use. It may also occur that the structure of the model is sometimes unknown making optimization and study difficult. To circumvent these issues, mathematically simpler models, commonly known as surrogate models, have been designed and used to successfully replace these complex, underlying models with much success. This technique has seen increasing use within the chemical process engineering field and this article summarizes some popular surrogates and their recent use in this area.

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Section: Surrogate Modelingmentioning

confidence: 99%

Section: Design Of Experiments For Surrogate Modelingmentioning

confidence: 99%

Overview of Surrogate Modeling in Chemical Process Engineering

McBride

Sundmacher

2019

Chemie Ingenieur Technik

211

108

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“…Our work has been used in other real world applications such as nano-scale CMOS invertors [11] and chemical engineering [10]. However, all these applications use Artificial Neural networks which is a non-incremental algorithm.…”

Section: Dynamic Adaptive Sampling Using Chernoff Inequalitymentioning

confidence: 99%

Intelligent sampling for big data using bootstrap sampling and chebyshev inequality

Satyanarayana

2014

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

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The amount of data being generated and stored is growing exponentially, owed in part to the continuing advances in computer technology. These data present tremendous opportunities in data mining, a burgeoning field in computer science that focuses on the development of methods that can extract knowledge from data. In many real world problems, these data mining algorithms have access to massive amounts of data. Mining all the available data is prohibitive due to computational (time and memory) constraints. Much of the current research is concerned with scaling up data mining algorithms (i.e. improving on existing data mining algorithms for larger datasets). An alternative approach is to scale down the data. Thus, determining a smallest sufficient training set size that obtains the same accuracy as the entire available dataset remains an important research question. Our research focuses on selecting how many (sampling) instances to present to the data mining algorithm. The goals of this paper is to study and characterize the properties of learning curves, integrate them with Chebyshev Bound to come up with an efficient general purpose adaptive sampling schedule, and to empirically validate our algorithm for scaling down the data.

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“…ANN fits any complex nonlinear functions, given sufficient complexity of the trained network . ANN has been applied successfully to many areas in chemical engineering, such as cracking furnace modelling and optimization, optimization of CO 2 capture cost using ANN surrogate models, optimization of industrial urea reactors, frictional pressure drop of tapered bubble columns, and estimation of gas‐oil minimum miscibility pressure using PSO‐ANN …”

Section: Introductionmentioning

confidence: 99%

“…ANN fits any complex nonlinear functions, given sufficient complexity of the trained network. [15] ANN has been applied successfully to many areas in chemical engineering, such as cracking furnace modelling and optimization, [16][17][18][19] optimization of CO 2 capture cost using ANN surrogate models, [20,21] optimization of industrial urea reactors, [22] frictional pressure drop of tapered bubble columns, [23] and estimation of gas-oil minimum miscibility pressure using PSO-ANN. [24] Although ANN has powerful function approximation ability, clear mathematical expression, and easy availability of implementation for training and analysis, the success of its application usually depends on structure selection and distribution of training data.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sampling for Surrogate Modelling with Artificial Neural Network and its Application in an Industrial Cracking Furnace

Jin

et al. 2016

Can J Chem Eng

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In surrogate modelling, a simple functional approximation of a complex system model is always constructed to reduce the computational expense, and the selection of a suitable surrogate model and a sampling method are key to obtaining a surrogate model for a complex system. To construct an appropriate surrogate model, three methods of adaptive surrogate modelling that use artificial neural networks (ANN) are developed by incorporating a new mechanism for automatically determining the number of hidden nodes and/or a new prediction error-based mixed adaptive sampling method. In the automatic determination, the number of hidden nodes can adaptively change according to the effective rate of parameters in the ANN during the adaptive surrogate modelling process. As a result, an improper number of hidden nodes determined by the empirical method can be avoided. The prediction error-based mixed adaptive sampling method is capable of finding the strong nonlinear behaviour of the underlying system, which is easily missed by the traditional prediction variance-based sampling method. The three methods and the previous method for adaptive surrogate modelling that use ANN are tested and compared in terms of replicating the behaviours of three types of challenge functions to determine the efficacy of the developed methods. Furthermore, these methods are used in an engineering problem of surrogate modelling for a cracking reaction simulator to validate the efficacy of the developed methods.

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An algorithm to determine sample sizes for optimization with artificial neural networks

Cited by 51 publications

References 35 publications

Overview of Surrogate Modeling in Chemical Process Engineering

Overview of Surrogate Modeling in Chemical Process Engineering

Intelligent sampling for big data using bootstrap sampling and chebyshev inequality

Adaptive Sampling for Surrogate Modelling with Artificial Neural Network and its Application in an Industrial Cracking Furnace

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