Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

He, Zezhao; Xia, Kewen; Niu, Wenjia; Aslam, Nelofar; Hou, Jun

doi:10.1155/2018/8243764

Cited by 11 publications

(11 citation statements)

References 23 publications

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“…The parameters of Terfenol-D used in the calculation were [31] M s = 1.65 × 10 5 A/m , λ s = 1000 × 10 −6 , k = 7000 A/m, c = 0.2, α = 0.0065, β = 5 × 10 −6 , µ 0 = 4π × 10 −7 H/m, N = 6.23 × 10 23 and k B = 1.38 × 10 −23 . The relevant parameters were optimized by a search algorithm [32] according to the experimental results.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Model and Experimental Study on Optical Fiber CT Based on Terfenol-D

Xia

Weng

2020

Sensors

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A nonlinear hysteresis model of magneto-mechanical-thermo coupling for Terfenol-D materials is presented according to Wiss ferromagnetic theory, thermodynamics relations and Jiles–Atherton model. Numerical calculation and experimental results show that the mode well reflects the magnetostrictive characteristics of Terfenol-D rod under the coupling of stress, temperature and magnetic field. A fiber Bragg grating current transformer based on Terfenol-D material is designed according to the strain sensing mechanism of fiber Bragg grating and the demodulation principle of unbalanced M–Z interferometer. The theoretical analysis and research on the working characteristics of the fiber current transformer under the influence of different prestressing force and bias current are carried out. The results are important for the design and application of the current transformer with the Terfenol-D material.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Model and Experimental Study on Optical Fiber CT Based on Terfenol-D

Xia

Weng

2020

Sensors

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“…. , M) of weak classifier S4VM; (6) Using the weight distribution β m , calculate the m th weak classifier G m ; 7Update the weight distribution of the training set w m+1,i ; (8) m � m + 1; (9) else (10) jump out of the loop; (11) end (12) end for (13) According to formula (11), m groups of weak classifiers are linearly combined, and the final classifier is output; (14) Use the final classifier to predict the training set classification. ALGORITHM 2: AdaBoost-ISSA-S4VM classification model algorithm.…”

Section: Comparison On Benchmark Functions With Hybridmentioning

confidence: 99%

“…(2) while (t < iter max) (3) Rank the fitness values and find the current best individual and the current worst individual. (4) R 2 � rand(1) (5) for i � 1: pop (6) Using formula (16), update the sparrow's location; (7) end for (8) for i � 1: pop (9) Using formula (17), update the sparrow's location; (10) end for / * the new division of labor structure scheme * / (11) Using formulas (18) and (19), update the producer and scrounger's cooperative location; (12) If the new location is better than before, update it use formula (20); (13) for l � 1: SD Num 14Using formula (9) update the sparrow's location; (15) end for (16) Get the current new location; (17) If the new location is better than before, update it; / * sine cosine algorithm scheme * / (18) Using formula (14), update the SCA sparrow's location; (19) If the new location is better than before, update it use formula (15); (20)…”

Section: Comparison On Benchmark Functions With Hybridmentioning

confidence: 99%

“…In order to overcome the above shortcomings, many researches have proposed the use of intelligent optimization algorithms to optimize the key parameters of the S4VM model. ese algorithms include quasi-Newton algorithms [9], cuckoo search algorithm (CS) [10], and beetle antennae search (BAS) [11]. For the advantages of sparrow search algorithm (SSA) [12], such as simple principle, strong mining capacity, and few adjustable parameters, the improved SSA that improves the classification performance of AdaBoost-S4VM is used to optimize the key parameters of AdaBoost-S4VM and S4VM in this research.…”

Section: Introductionmentioning

confidence: 99%

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Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection

Zhang

Xia

et al. 2021

Mathematical Problems in Engineering

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The Adaptive Boosting (AdaBoost) classifier is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost classifier directly to pulmonary nodule detection of labeled and unlabeled lung CT images since there are still some drawbacks to ensemble learning method. Therefore, to solve the labeled and unlabeled data classification problem, the semi-supervised AdaBoost classifier using an improved sparrow search algorithm (AdaBoost-ISSA-S4VM) was established. Firstly, AdaBoost classifier is used to construct a strong semi-supervised classifier using several weak classifiers S4VM (AdaBoost-S4VM). Next, in order to solve the accuracy problem of AdaBoost-S4VM, sparrow search algorithm (SSA) is introduced in the AdaBoost classifier and S4VM. Then, sine cosine algorithm and new labor cooperation structure are adopted to increase the global optimal solution and convergence performance of sparrow search algorithm, respectively. Furthermore, based on the improved sparrow search algorithm and adaptive boosting classifier, the AdaBoost-S4VM classifier is improved. Finally, the effective improved AdaBoost-ISSA-S4VM classification model was developed for actual pulmonary nodule detection based on the publicly available LIDC-IDRI database. The experimental results have proved that the established AdaBoost-ISSA-S4VM classification model has good performance on labeled and unlabeled lung CT images.

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“…The experimental results indicated that the proposed model can not only improve the forecasting accuracy, but also can be an effective tool in assisting the management of wind power plants [17]. A new cuckoo search algorithm based on a chaotic catfish effect optimization of the SVM was proposed by He and Xia, who applied it to oil layer recognition [18]. Dai and Niu proposed a SVM optimization based on differential evolution and the grey wolf optimization (DE-GWO-SVM) algorithm to predict power grid investment, which proved that the DE-GWO-SVM model had strong generalization capacity and had a good prediction effect on power grid investment forecasting in China [19].…”

Section: Introductionmentioning

confidence: 99%

Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine

Wang

Kong

et al. 2019

Applied Sciences

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Methane, known as a flammable and explosion hazard gas, is the main component of marsh gas, firedamp, and rock gas. Therefore, it is important to be able to detect methane concentration safely and effectively. At present, many models have been proposed to enhance the performance of methane predictions. However, the traditional models displayed inevitable shortcomings in parameter optimization in our experiment, which resulted in their having poor prediction performance. Accordingly, the improved chicken swarm algorithm optimized support vector machine (ICSO-SVM) was proposed to predict the concentration of methane precisely. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum due to its characteristics, so the ICSO algorithm was developed. The formula for position updating of the chicks of the ICSO is not only about the rooster of the same subgroup, but also about the roosters of other subgroups. Therefore, the ICSO algorithm more easily avoids falling into the local extremum. In this paper, the following work has been done. The sample data were obtained by using the methane detection system designed by us; In order to verify the validity of the ICSO algorithm, the ICSO, CSO, genetic algorithm (GA), and particle swarm optimization algorithm (PSO) algorithms were tested, and the four models were applied for methane concentration prediction. The results showed that he ICSO algorithm had the best convergence effect, relative error percentage, and average mean squared error, when the four models were applied to predict methane concentration. The results showed that the average mean squared error values of ICSO-SVM model were smaller than other three models, and that the ICSO-SVM model has better stability, and the average recovery rate of the ICSO-SVM is much closer to 100%. Therefore, the ICSO-SVM model can efficiently predict methane concentration.

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Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

Cited by 11 publications

References 23 publications

Model and Experimental Study on Optical Fiber CT Based on Terfenol-D

Model and Experimental Study on Optical Fiber CT Based on Terfenol-D

Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection

Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine

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