Influence of fuzzy index parameter on FSVM classifier performance

Flame monitoring and categorization become important in efficient power generation and energy conservation sectors. Flicker Frequency Range (FFR) is considered as the key factor to monitor flames and fuel identification. The fluctuation of combustion arises due to the varying quality of fuel, which causes the overlapping of FFR for different fuels leading to misclassification. In this work, we attempt to classify fuels based on flame characteristics output from a Digital Flame Scanner. A new model is proposed by combining Time Series Analysis with Fuzzy Support Vector Machine (TSA-FSVM) for training, classification, and prediction of fuel types. With the help of 12,000 real-time data collected from the Bharat Heavy Electricals Limited (BHEL), Tuticorin branch (a public sector company, which manufactures power plant equipment), a comparative analysis is performed using different Machine Learning algorithms with the proposed technique. From the results, it is found that TSA-FSVM outperforms existing as well as other Machine Learning methods by increasing the accuracy of predicting the right fuel. Thus, it helps to avoid the boiler system explosion/OFF state, which leads to conserving the energy, increases electricity production, and cost-efficiency.

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“…[38][39][40] Significant spikes obtained from the above methods are used to identify features that are considered input data for FSVM. 41…”

Section: Methodsmentioning

confidence: 99%

Fuel Classification based on Flame Characteristics using a Time Series Analysis with Fuzzy Support Vector Machine Algorithm

Annadurai

Arock

2020

Asia-Pacific J Chem Eng

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A New Methodology to Arrive at Membership Weights for Fuzzy SVM

Maruthamuthu

Murugesan

Muthulakshmi

2022

International Journal of Fuzzy System Applications

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Support Vector Machine (SVM) is a supervised classification technique that uses the regularization parameter and Kernel function in deciding the best hyperplane to classify the data. SVM is sensitive to outliers, and it makes the model weak. To overcome the issue, the Fuzzy Support Vector Machine (FSVM) introduces fuzzy membership weight into its objective function, which focuses on grouping the fuzzy data points accurately. Knowing the importance of the membership weights in FSVM, we have introduced four new expressions to compute the FSVM membership weights in this study. They are determined from the Fuzzy C-means Algorithm's membership values (FCM). The performances of FSVM with three different kernels are assessed in terms of accuracy. The experiments are conducted for various combinations of FSVM parameters, and the best combinations for each kernel are highlighted. Six benchmark datasets are used to demonstrate the performance of FSVM and the proposed models’ performance are compared with the existing models in recent literature.

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Influence of fuzzy index parameter on FSVM classifier performance

Cited by 2 publications

References 18 publications

Fuel Classification based on Flame Characteristics using a Time Series Analysis with Fuzzy Support Vector Machine Algorithm

Fuel Classification based on Flame Characteristics using a Time Series Analysis with Fuzzy Support Vector Machine Algorithm

A New Methodology to Arrive at Membership Weights for Fuzzy SVM

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