Computational neural networks for mapping calorimetric data: Application of feed-forward neural networks to kinetic parameters determination and signals filtering

Sbirrazzuoli, Nicolas; Brunel, Didier

doi:10.1007/bf01414100

Cited by 24 publications

(16 citation statements)

References 28 publications

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“…In order to give comparisons between the results presented in this study and those obtained with the usual methods (Achar-Brindley-Sharp or multiple linear regression methods) used in thermal analysis and presented elsewhere [4,15], absolute relative errors were computed. These errors were computed for a parameter x, as:…”

Section: Resultsmentioning

confidence: 99%

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Neural networks for kinetic parameters determination, signal filtering and deconvolution in thermal analysis

Sbirrazzuoli

Brunel

Elegant

1997

Journal of Thermal Analysis

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Feedforward neural networks have been used for kinetic parameters determination and signal filtering in differential scanning calorimetry. The proper learning function was chosen and the network topology was optimized, using an empiric procedure. The learning process was achieved using simulated thermoanalytical curves. The resilient-propagation algorithm have led to the best minimization of the error computed over all the patterns. Relative errors on the thermodynamic and kinetic parameters were evaluated and compared to those obtained with the usual thermal analysis methods (single scan methods). The errors are much lower, especially in presence of noisy signals. Then, our program was adapted to simulate thermal effects with known thermodynamic and kinetic parameters, generated electrically, using a PC computer and an electronic interface on the serial port. These thermal effects have been generated by using an inconel thread.

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Section: Resultsmentioning

confidence: 99%

“…An application of artificial neural networks to DSC is presented in this study, and is generalizable to any thermal analysis [4].…”

Section: Introductionmentioning

confidence: 99%

Neural networks for kinetic parameters determination, signal filtering and deconvolution in thermal analysis

Sbirrazzuoli

Brunel

Elegant

1997

Journal of Thermal Analysis

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“…To determine the term in equation 10 it may be explicit as (13) Considering that S(w) contribution is not relevant, . Using this result along equations 10 and 11, the parameters can be obtained by solving (14) However, in this work, it is assumed the first-order regularization in equation 13, (15) with I being the identity matrix and µ as the regularization parameter.…”

Section: Levenberg-marquardt (Lm) Fittingmentioning

confidence: 99%

“…Neural network can also be used to successfully fit experimental data with high accuracy and reduced computational effort. [13][14][15][16] The artificial neural network application to filter and deconvolute calorimetric signals was initially proposed by Sbirrazzuoli and Brunel. 13 In their study, synthetic DSC curves were adjusted and the error analysis established by an objective function defined as the difference between synthetic and determined data.…”

Section: Introductionmentioning

confidence: 99%

Hopfield Neural Network-Based Algorithm Applied to Differential Scanning Calorimetry Data for Kinetic Studies in Polymorphic Conversion

Araujo¹,

Carvalho²,

Marques³

et al. 2020

J. Braz. Chem. Soc.

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A general kinetic equation to simulate differential scanning calorimetry (DSC) data was employed along this work. Random noises are used to generate a thousand data, which are considered to evaluate the performance of Levenberg-Marquardt (LM) and a Hopfield neural network (HNN) based algorithm in the fitting process. The HNN-based algorithm showed better results for two different initial conditions: exact and approximated values. After this statistical analysis, DSC experimental data at three heating rates for losartan potassium, an antihypertensive drug, was adjusted by the HNN method using different initial conditions to obtain the activation energy and frequency factor. Additionally, it was possible to recover the parameters for the kinetic model with accuracy, showing that the conversion is described by a complex process, once these values do not correspond to any ideal models described in the literature.

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“…This allows the application of the ANNs for solving difficult tasks such as pattern recognition, pattern classification, prediction, etc. [1][2][3]. The goal of an ANN designed for a pattern classification task is to adapt itself to classify input vectors, representing physical objects or events, into several categories.…”

Section: Introductionmentioning

confidence: 99%

Application of Feed-forward Artificial Neural Networks to the Identification of Defective Analog Integrated Circuits

Mičušík

Stopjaková

Benuskova

2002

Neural Comput Applic

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This paper presents a new approach for detecting defects in analog integrated circuits using a feedforward neural network trained by the resilient error back-propagation method. A feed-forward neural network has been used for detecting faults in a simple analog CMOS circuit by representing the differences observed in power supply current of fault-free and faulty circuits. The identification of defects was performed in time and frequency domains, followed by a comparison of results achieved in both domains. We show that resilient backpropagation neural networks can be a very efficient and versatile approach for identifying defective analog circuits. Moreover, this approach is not limited to the supply current analysis, because it also offers monitoring of other circuit parameters. The type of defects detected by the resilient backpropagation neural networks, as well as other possible applications of this approach, are discussed.

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Computational neural networks for mapping calorimetric data: Application of feed-forward neural networks to kinetic parameters determination and signals filtering

Cited by 24 publications

References 28 publications

Neural networks for kinetic parameters determination, signal filtering and deconvolution in thermal analysis

Neural networks for kinetic parameters determination, signal filtering and deconvolution in thermal analysis

Hopfield Neural Network-Based Algorithm Applied to Differential Scanning Calorimetry Data for Kinetic Studies in Polymorphic Conversion

Application of Feed-forward Artificial Neural Networks to the Identification of Defective Analog Integrated Circuits

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