A neuro-evolutive technique applied for predicting the liquid crystalline property of some organic compounds

Drăgoi, Elena-Niculina; Curteanu, Silvia; Lisa, Cătălin

doi:10.1080/0305215x.2011.644546

Cited by 14 publications

(14 citation statements)

References 29 publications

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“…Mean Squared Error is judged according to value. The lower the values, the better selection made to determine hidden neuron [11]. If for an example where the MSE value show zero, this define as there is no error present.…”

Section: Methodsmentioning

confidence: 99%

Optimal Number of Hidden Neuron Identification for Sustainable Manufacturing Application

Noor¹,

Fauadi²,

Jafar³

et al. 2019

IJRTE

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There were 50 data sample obtained from industries in Malaysia that practice sustainable manufacturing. Input file is presented in matrix 4x50 and 1x50 matrix as target file. However, there is no suitable number of hidden neuron that can be applied for the neural network model with 4 inputs and 1 output. An experiment has been done to identify the suitable hidden neuron through the observation of values from MSE and Regression. The hidden neuron must be no overfitting. The same goes for output and targets value must have close or linear relationship. The sample of tested hidden neuron is from 5 to 40 hidden neurons. The final answer obtained after look into Mean Square Error (MSE) values, Regression values and plots is hidden neuron 29. Hidden neuron 29 shows positive result in all criteria and should be implemented for this type of neural network model.

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Section: Methodsmentioning

confidence: 99%

Optimal Number of Hidden Neuron Identification for Sustainable Manufacturing Application

Noor¹,

Fauadi²,

Jafar³

et al. 2019

IJRTE

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show abstract

“…Neural networks were also used for classifying the compounds of class 2 depending on the liquid‐crystalline property in the study of Drăgoi et al . But in this research, the optimal network was developed using a methodology based on an evolutionary algorithm, namely differential evolution (DE). This algorithm was applied in a self‐adapting variant, performing structural and parametric optimization of NN and also determining the optimal values for its parameters.…”

Section: Resultsmentioning

confidence: 99%

“…Yet, given the complexity of the liquid-crystalline phase, it is difficult to predict the mesophase occurrence. In line with our previous research [4][5][6], this paper will employ the support vector machine (SVM) technique to define the liquid-crystalline (LC) structure-behavior relation for a set of aromatic derivatives. Support vector machines [7] represent a method of classification (binary classification in the standard approach) and regression.…”

Section: Introductionmentioning

confidence: 97%

Prediction of liquid‐crystalline property using support vector machine classification

Butnariu

Lisa

Leon

et al. 2013

Journal of Chemometrics

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A quantitative structure–property relationships analysis applied on a large dataset of aromatic organic compounds was performed using a methodology based on support vector machine (SVM). The liquid‐crystalline property was correlated with a series of structural and geometric molecular descriptors: length of the rigid and flexible sections, molecular mass, and an asymmetry parameter that quantifies the ratio between the total length and molecule diameter. The SVM classification was achieved, analyzing the influence of characteristic elements of this tool: type of kernel and parameters of the method. A comparison with other techniques (eager learners and lazy learners algorithms and neural networks) was made using the same dataset and working conditions. The accurate results provided by the SVM method, the small number of parameters, and ease of handling recommend the SVM tool as an appropriate technique for classification. Copyright © 2013 John Wiley & Sons, Ltd.

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“…The process parameters considered for model input were biomass concentration, superficial air velocity, specific power, and oxygen-vector volumetric fraction, and the output was the mass transfer coefficient. The algorithm, called SADE-NN-1, represents an improved version of SADE-NN proposed in Dragoi et al (2012a), the alteration consisting in the introduction of the following elements: (i) OBL to improve initialization, (ii) a new mutation strategy in order to improve the offspring generation, and (iii) increasing the number of activation functions that a neuron could have during the evolution (linear, hard limit, bipolar sigmoid, logistic sigmoid, tangent sigmoid, sinus, radial basis, and triangular basis functions). Simple ANNs, with one hidden layer and small number of intermediate neurons, accurately model the process considered as case study.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The prediction of the liquid crystalline property of some organic compounds (bis aromatic and azo aromatic types) was performed using ANNs optimized with two different DE self-adaptive versions (Dragoi et al 2012a). One version is proposed in Brest et al (2006) and is called jDE.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

The use of differential evolution algorithm for solving chemical engineering problems

Drăgoi

Curteanu

2016

Reviews in Chemical Engineering

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AbstractDifferential evolution (DE), belonging to the evolutionary algorithm class, is a simple and powerful optimizer with great potential for solving different types of synthetic and real-life problems. Optimization is an important aspect in the chemical engineering area, especially when striving to obtain the best results with a minimum of consumed resources and a minimum of additional by-products. From the optimization point of view, DE seems to be an attractive approach for many researchers who are trying to improve existing systems or to design new ones. In this context, here, a review of the most important approaches applying different versions of DE (simple, modified, or hybridized) for solving specific chemical engineering problems is realized. Based on the idea that optimization can be performed at different levels, two distinct cases were considered – process and model optimization. In both cases, there are a multitude of problems solved, from different points of view and with various parameters, this large area of successful applications indicating the flexibility and performance of DE.

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A neuro-evolutive technique applied for predicting the liquid crystalline property of some organic compounds

Cited by 14 publications

References 29 publications

Optimal Number of Hidden Neuron Identification for Sustainable Manufacturing Application

Optimal Number of Hidden Neuron Identification for Sustainable Manufacturing Application

Prediction of liquid‐crystalline property using support vector machine classification

The use of differential evolution algorithm for solving chemical engineering problems

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