A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm

Deng, Wu; Yao, Ru; Zhao, Huimin; Yang, Xinhua; Li, Guangyu

doi:10.1007/s00500-017-2940-9

Cited by 396 publications

(156 citation statements)

References 67 publications

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“…Theorem 3: The optimization approach presented in Algorithm 1 monotonically decreases the objective value of problem (8).…”

Section: B Convergence Analysismentioning

confidence: 99%

“…In this section, we mainly analyze the sensitivity of parameter selection of the proposed method and then provide a simple strategy to select the optimal parameters. From (8), we can find that the proposed method only contains two penalty parameters, i.e., λ 1 and λ 2 , which are regularized on the nearest neighbor preserving term and the feature selection term, respectively. To analyze the sensitivity of the classification performance to these two parameters, firstly, a large candidate range {10 −5 , 10 −4 , 10 −3 , 10 −2 , 10 −1 , 1, 10 1 , 10 2 , 10 3 , 10 4 , 10 5 } is defined for the two penalty parameters.…”

Section: Parameter Sensitivity and Selectionmentioning

confidence: 99%

“…In Fig. 7, we have plotted the objective function values and classification accuracies versus the iteration steps on the COIL100, PIE, LFW, and Scene SPM databases, respectively, in which the objective function value is directly calculated as obj = T − X T W 2 F + λ 1 T r W T S W W + λ 2 W 2,1 X 2 F according to the objective function (8). From these figures, it is obvious that the objective function value is monotonically decreasing till to the stationary point with the iteration increasing, which proves the point of Theorem 3.…”

Section: Experiments Of Convergence Studymentioning

confidence: 99%

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Adaptive Locality Preserving Regression

Wen

Zhong

Zhang

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional 'Frobenius norm' with the special l2,1 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owning to the row-sparsity property of the l2,1 norm. Extensive experiments conducted on the synthetic database with manifold structure and many realworld databases prove the effectiveness of the proposed method.

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“…Theorem 3: The optimization approach presented in Algorithm 1 monotonically decreases the objective value of problem (8).…”

Section: B Convergence Analysismentioning

confidence: 99%

Section: Parameter Sensitivity and Selectionmentioning

confidence: 99%

Section: Experiments Of Convergence Studymentioning

confidence: 99%

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Adaptive Locality Preserving Regression

Wen

Zhong

Zhang

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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“…Therefore, in the SVRM training process, it is particularly important to choose the appropriate parameters for the penalty factor C and kernel function width σ. At present, PSO algorithm has been used to optimize the parameters of least squares support vector machines in order to construct an optimal LS-SVM classifies [32] and an improved adaptive particle swarm optimization algorithm has been proposed [33]. In this paper, PSO algorithm is used to automatically select the appropriate parameters of SVRM model and PSO-SVRM model is proposed to obtain the best performance.…”

Section: Pso-svrm Modelmentioning

confidence: 99%

An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints

Zou

Sun

Yang

2019

Entropy

Self Cite

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In order to obtain comprehensive assessment of the factors influencing fatigue life and to further improve the accuracy of fatigue life prediction of welded joints, soft computing methods, including entropy-based neighborhood rough set reduction algorithm, the particle swarm optimization (PSO) algorithm and support vector regression machine (SVRM) are combined to construct a fatigue life prediction model of titanium alloy welded joints. By using an entropy-based neighborhood rough set reduction algorithm, the influencing factors of the fatigue life of titanium alloy welded joints such as joint type, plate thickness, etc. are analyzed and the reduction results are obtained. Fatigue characteristic domains are proposed and determined subsequently according to the reduction results. The PSO-SVRM model for fatigue life prediction of titanium alloy welded joints is established in the suggested fatigue characteristic domains. Experimental results show that by taking into account the impact of joint type, the PSO-SVRM model could better predict the fatigue life of titanium alloy welded joints. The PSO-SVRM model indicates the relationship between fatigue life and fatigue life influencing factors in multidimensional space compared with the conventional least-square S-N curve fitting method, it could predict the fatigue life of the titanium alloy welded joints more accurately thus helps to the reliability design of the structure. joint thickness and load model, the S-N sample data is compressed into a narrow band. In the present study, comprehensive and quantitative analysis of the influencing factors which affect the fatigue life of welded joints is lacking and the problem of low precision of fatigue life prediction of welded joints still exists.Thermodynamics entropy has been used for fatigue life prediction for a long time. For example, a damage metric based on the second law of thermodynamics and statistical mechanics was presented [8]. The proposed thermodynamic framework treated a solid body as a thermodynamic system and required that the entropy production be non-negative. A thermodynamic framework was presented for damage mechanics of solid materials [9]. It was believed that there was no need for physically meaningless empirical parameters to define a phenomenological damage potential surface or a Weibull function to trace damage evolution in solid continuum. A generalized theory of evolution based on the concept of tribo-fatigue entropy was proposed where tribo-fatigue entropy was determined by the processes of damage ability conditioned by thermodynamic and mechanical effects causing to the change of states of any systems [10]. The Shannon's differential entropies of both distributions of the strain in the loading direction and the first principal strain were employed at the tension peak of the cycles as measuring parameters of strain inhomogeneity [11]. A thermo-mechanical fatigue life prediction model based on the theory of damage mechanics was presented in [12]. The damage evolution, corresponding to the mate...

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“…where the value of w max is 0.9, and the value of w min is 0.4. iteration represents the current number of iterations, and iteration max represents the maximum number of iterations. c 1 and c 2 are the acceleration learning constants used to adjust the maximum step size of the particle search [52]. The updated formula is expressed as Equation (4).…”

Section: Optimization Principle Using the Particle Swarm Optimizationmentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

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Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

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A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm

Cited by 396 publications

References 67 publications

Adaptive Locality Preserving Regression

Adaptive Locality Preserving Regression

An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

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