A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection

Wang, Chuang; Zhang, Yong; Song, Jinbo; Liu, Qingqiang; Dong, Hongli

doi:10.1080/21642583.2019.1573386

Cited by 43 publications

(23 citation statements)

References 23 publications

(30 reference statements)

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“…Vapnik is introduced Support Vector Machine [26,27]. SVM is an algorithm for supervised learning, used for classification and regression [28,29,30]. SVM's aim is to find the individual hyperplane with the highest margin that can divide the classes linearly, as seen in (Fig2).…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Machine Learning Applications based on SVM Classification A Review

Abdullah¹,

Abdulazeez²

2021

QAJ

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Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Machine Learning Applications based on SVM Classification A Review

Abdullah¹,

Abdulazeez²

2021

QAJ

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“…This section employs the proposed modified PSO optimized SVM to design the HIC for haptic system [35]. Here, m-PSO has been utilized to overcome the drawbacks of manual selection of SVM independent parameters.…”

Section: Hic Design Using Proposed M-pso Optimized Svmmentioning

confidence: 99%

Novel m-PSO-SVM based Interface Controller Design for Haptic System

Kumar

Ohri

2020

J. commun. softw. syst. (Online)

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The haptic system has two key performance issues: stability and transparency. A haptic interface controller (HIC) is designed to address these issues. Addressing these issues becomes a complex problem as both are complementary to each other. Here, when transparency of the system is increased, its stability degrades and vice-versa. To overcome this problem, intelligent optimized solutions are used in this paper to design a HIC controller for the haptic system. SVM and NN techniques have been employed to identify the performance of the controller, ensuring stability and transparency both. The disadvantages of NN in terms of the number of neurons and hidden layers are overcome by SVM. Further, the performance of SVM is highly dependent upon the selection of free parameters. So, further, a modified PSO technique is employed for the optimal selection of these parameters to enhance the performance of SVM. Hence, this novel proposed hybrid technique of m-PSO optimized SVM is applied for the optimal design of the HIC to find out an optimal solution between trade-off the transparency and stability of the haptic device simultaneously. To appreciate the efficacy of the proposed technique, the result obtained with this is compared with HIC design using neural network and conventional ZN method also. This designed controller ensures stability as well as transparency, even under the presence of uncertainty, delay, and quantization error.

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“…If c is too large, it will cause over-learning, which will reduce the generalization ability of the classifier. In contrast, it will lead to the classification accuracy of the classifier being too low, and even the entire classifier model will be invalid (Wang, Zhang, Song, Liu, & Dong, 2019). Then, the Lagrangian function is introduced to solve Equation (10), and the structural superplane of the optimal classification is transformed into a convex quadratic programming problem:…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

An intelligent fault identification method of rolling bearings based on SVM optimized by improved GWO

Huang

Fan

Wei

et al. 2019

Systems Science & Control Engineering

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To improve the accuracy and the recognition efficiency of a bearing fault diagnosis, a fault diagnosis method based upon the improved grey wolf optimization (IGWO) algorithm and support vector machine (SVM) is proposed in the following manners. First, the data are pre-processed by using the set ensemble empirical mode decomposition (EEMD), Shannon wavelet packet entropy (SWPE), and principal component analysis (PCA). Next, the idea of updating the host bird nest by the cuckoo search (CS) optimization algorithm is introduced into the grey wolf optimization (GWO) algorithm to obtain the IGWO algorithm. Then, the SVM is optimized by the IGWO algorithm to obtain optimal parameters for a new diagnostic model. This model improves the problem where the algorithm easily to falls into a local optimum. The learning ability and the generalization ability of the SVM are also enhanced. Finally, the effectiveness of the optimization model is tested by two different bearing data sets. The results show that compared to the genetic algorithm (GA), particle swarm optimization (PSO) and GWO algorithm optimization, the IGWO algorithm can be more accurate and efficient when diagnosing bearings. ARTICLE HISTORY

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A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection

Cited by 43 publications

References 23 publications

Machine Learning Applications based on SVM Classification A Review

Machine Learning Applications based on SVM Classification A Review

Novel m-PSO-SVM based Interface Controller Design for Haptic System

An intelligent fault identification method of rolling bearings based on SVM optimized by improved GWO

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