首页|New Support Vector Machines Study Findings Recently Were Published by Researchers at Dalian Maritime University (Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm)
New Support Vector Machines Study Findings Recently Were Published by Researchers at Dalian Maritime University (Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm)
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NETL
NSTL
IEEE
Research findings on are discussed in a new report. According to news originating from Dalian, People’s Republic of China, by NewsRx editors, the research stated, “The key to reducing operation and maintenance costs and improving reliability is to evaluate the condition and fault detection methods of ship ballast water systems.” Financial supporters for this research include Innovation Engineering of The Offshore Liquefied Natural Gas Equipment Industry Chain; Development of Liquid Cargo And Electromechanical Simulation Operation System For Liquefied Natural Gas Ship. The news correspondents obtained a quote from the research from Dalian Maritime University: “To reduce the impact of support vector machine (SVM) parameter uncertainty and improve the accuracy of ship ballast water system fault diagnosis models, this paper proposes a multi-strategy collaborative improved sparrow search algorithm (ISSA) to optimize the parameters of SVM for fault diagnosis. First, ISSA initializes the population using tent chaotic maps to improve spatial distribution uniformity and stabilize the population quality. Second, the discoverer in the algorithm adopts an adaptive weighting strategy to avoid falling into local optima and accelerate the convergence speed. Finally, the follower position introduces mutation strategies and chaotic perturbations to enhance local evolution capabilities and improve the utilization of search areas. The experimental results show that compared with sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), genetic Algorithm (GA), whale optimization algorithm (WOA), and grey wolf optimization algorithm (GWO), ISSA can obtain better optimal solutions at the fastest convergence rate on the benchmark test function. On the dataset of the ship ballast water system, the ISSA-SVM fault diagnosis model proposed in this paper has the best classification performance, with an average accuracy of 97.44%.”
NETL
NSTL
IEEE
