A Performance Analysis of GA-ELM Classifier in Classification of Abnormality Detection in Electrical Impednce Tomography (EIT) Lung Images

Prabu, R; Harikumar, R.

doi:10.5958/2249-7315.2016.00338.5

Cited by 4 publications

(1 citation statement)

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“…Diab et al predicted the viscosity and rheological behavior of SBS modified asphalt, according to Vinogradov Malkin and Phillips Deutsch models [16]. However, there are some problems with these methods [17,18]. For example, it is easy to fall into the local optimum solution in the process of solving; the parameters are not easy to determine; the requirements of training samples are high relatively; and the training is difficult.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Low‐Temperature Rheological Properties of SBS Modified Asphalt

Chen

Wang

Song³

2020

Advances in Civil Engineering

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The extreme learning machine (ELM) algorithm optimized by genetic algorithm (GA) was used to quickly predict the low-temperature rheological properties of styrenic block copolymer (SBS) modified asphalt through the properties of the raw materials. In this work, one hundred groups of survey data and test data were collected and analyzed. Fourteen vital raw material parameters, such as chemical composition indexes of matrix asphalt and technical indexes of SBS modifier, were selected as the input parameter. The stiffness modulus and m-value of SBS modified asphalt were taken as the output parameter. Then, the GA-ELM prediction model of low-temperature rheological properties was established. According to comparison and analysis with other prediction models, the accuracy and output stability of the GA-ELM prediction model were verified. The results show that the GA-ELM model had obvious accuracy and efficiency. It can be used to predict the low-temperature rheological properties of SBS modified asphalt. Compared with the traditional prediction models, the error of the GA-ELM model was reduced by 68.97–81.48%.

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Section: Introductionmentioning

confidence: 99%