Classification method based on the deep structure and least squares support vector machine

Ma, Wenlu; Liu, Ran

doi:10.1049/el.2019.3776

Cited by 7 publications

(4 citation statements)

References 21 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an extension of SVM, LSSVM owns two principal characteristics: (i) equality constraints are utilized to replace the inequality ones in SVM; (ii) kernel function is adopted to transform prediction problems into solving equations. These two aspects can greatly improve assessment accuracy and speed [40].…”

Section: Lssvmmentioning

confidence: 99%

Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road

Liang

Wang

2021

Energies

View full text Add to dashboard Cite

Scientific and timely sustainability evaluation of the photovoltaic industry along the Belt and Road is of great significance. In this paper, a novel hybrid evaluation model is proposed for accurate and real-time assessment that integrates modified set pair analysis with least squares support vector machine that combines improved cuckoo search algorithm. First, the indicator system is set from five principles, namely economy, politics, society, ecological environment and resources. Then, the traditional approach is established through modifying set pair analysis on the basis of variable fuzzy set coupling evaluation theory. A modern intelligent assessment model is designed that integrates improved cuckoo search algorithm and least squares support vector machine where the concept of random weight is introduced in improved cuckoo search algorithm. In the case analysis, the relative errors calculated by the proposed model all fluctuate in the range of [−3%, 3%], indicating that it has the strongest fitting and learning ability. The empirical analysis verifies the scientificity and precision of the method and points out influencing factors. It provides a new idea for rapid and effective assessment of PV industry along the Belt and Road, as well as assistance for the sustainable development of this industry. This paper innovatively proposes the sustainability evaluation index system and evaluation model for the photovoltaic industry in countries along the Belt and Road, thus contributing to the promotion of sustainable development of the photovoltaic industry in countries along the Belt and Road.

show abstract

Section: Lssvmmentioning

confidence: 99%

Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road

Liang

Wang

2021

Energies

View full text Add to dashboard Cite

show abstract

“…However, because the goal of train samples for these models is to minimize the fitting error, it is easy to create problems such as overfitting or local optimality. The support vector machine (SVM) is an algorithm based on the statistical learning theory of small samples, which can be used to solve nonlinear and high dimension problems [24]. Wang et al [25] predicted the water level during an ice-jammed period by means of the BP neural network and the support vector machine.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Assessing Heat Balance State along a Water Transfer Channel during Winter Periods

Cheng

Wang

Sui

et al. 2022

Water

View full text Add to dashboard Cite

Ice problems in channels for water transfer in cold regions seriously affect the capacity and efficiency of water conveyance. Sometimes, ice problems such as ice jams in water transfer channels create risk during winter periods. Recently, water temperature and environmental factors at various cross-sections along the main channel of the middle route of the South-to-North Water Transfer Project in China have been measured. Based on these temperature data, the heat balance state of this water transfer channel has been investigated. A principal component analysis (PCA) method has been used to analyze the complex factors influencing the observed variations of the water temperature, by reducing eigenvector dimension and then extracting the principal component as the input feature. Based on the support vector machine (SVM) theory, a new approach for judging the heat loss or heat gain of flowing water in a channel during winter periods has been developed. The Gaussian radial basis is used as the kernel function in this new approach. Then, parameters have been optimized by means of various methods. Through the supervised machine learning process toward the observed water temperature data, it is found that the air–water temperature difference and thermal conditions are the key factors affecting the heat loss or heat absorption of water body. Results using the proposed method agree well with those of measurements. The changes of water temperature are well predicted using the proposed method together with the state of water heat balance.

show abstract

“…The core concept of these algorithms is to compress large datasets into smaller sub-datasets, and then train them in the LSSVM model [15,16]. Since the reduced sub-sample set carries almost all the important information of the original sample, it can be used as a training sample for the LSSVM model [11,12,14,20]. [11] and [12] solved the problem of fault diagnosis, [20] and [14] proposed a deep structure of LSSVM to solve classification problems.…”

Section: Introductionmentioning

confidence: 99%

“…Since the reduced sub-sample set carries almost all the important information of the original sample, it can be used as a training sample for the LSSVM model [11,12,14,20]. [11] and [12] solved the problem of fault diagnosis, [20] and [14] proposed a deep structure of LSSVM to solve classification problems. To increase the accuracy, a variety of deep network models based on SVM have been proposed in [6,7,19,21,29] and successfully applied to various classification and regression prediction scenarios.…”

Section: Introductionmentioning

confidence: 99%

Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

Liu

2021

ITC

Self Cite

View full text Add to dashboard Cite

Least squares support vector machine (LSSVM) is a machine learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, and it has good performance in the data processingof small samples. The LSSVM model lacks sparsity and is unable to handle large-scale data problem, this articleproposes an LSSVM method based on mixture kernel learning and sparse samples. This algorithm reduces theinitial training set to a sub-dataset using a sparse selection strategy. It converts the single kernel function in theLSSVM model into a mixed kernel function and optimizes its parameters. The reduced sub-dataset is used fortraining LSSVM. Finally, a group of datasets in the UCI Machine Learning Repository were used to verify theeffectiveness of the proposed algorithm, which is applied to real-world power load data to achieve better fittingand improve the prediction accuracy.

show abstract

Classification method based on the deep structure and least squares support vector machine

Cited by 7 publications

References 21 publications

Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road

Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road

A New Approach for Assessing Heat Balance State along a Water Transfer Channel during Winter Periods

Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

Contact Info

Product

Resources

About