A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting

Kouziokas, Georgios N.

doi:10.1016/j.engappai.2020.103650

Cited by 48 publications

(20 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this section, the Mackey-Glass chaotic time series and Nonlinear Autoregressive Moving-Average (NARMA) series are used to verify the performance of DFPIO-SVM. DFPIO-SVM is compared to original SVM [20], PSO [29], DE [38], PIO [42], and OPIO [48]. For these prediction methods, the training number of samples is 900, and the prediction number of samples is 100. e dimension of the input and output are 5 and 1, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…where y n denotes target data, y n denotes predicted data, and N is the number of data samples. e prediction accuracy of DFPIO-SVM and other methods in [20,29,38,42,48] are shown in Table 1, and it Complexity can be seen that the prediction accuracy of the DFPIO-SVM is better than that of those methods. e average prediction accuracies of DFPIO-SVM are increased by 84.08%.…”

Section: 1mentioning

confidence: 99%

“…With the development of artificial intelligence technology, a lot of machine learning algorithms, Long Short-Term Memory (LSTM) [15][16][17], BP [18,19], SVM [20,21], Radial Basis Function Neural Network (RBFNN) [22][23][24], Echo State Network (ESN) [25][26][27], and Deep Belief Network (DBN) [28], have been widely used in time series prediction. SVM is used to solve regression problems due to its advantages of no large amount of sample data and fast training speed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temperature Prediction of Photovoltaic Panels Based on Support Vector Machine with Pigeon-Inspired Optimization

2020

View full text Add to dashboard Cite

The output stability of the photovoltaic (PV) system is directly affected by temperature change of PV panels. In this paper, a novel temperature prediction method of PV panels with support vector machine (SVM) is proposed, which can solve the temperature prediction problem in a complex environment. In order to optimize parameters of SVM, a Pigeon-Inspired Optimization (PIO) method is given. Meanwhile, the delay factor (DF) is added to improve the PIO algorithm for avoiding the problem of local optimum. Moreover, a multisensor monitoring system of PV is established, and the collected data of temperature are used to train and verify the accuracy of the model. Finally, the proposed method is evaluated using synthetic and actual data sets. Simulation results show that the DFPIO-SVM can obtain better predictive performance.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: 1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temperature Prediction of Photovoltaic Panels Based on Support Vector Machine with Pigeon-Inspired Optimization

2020

View full text Add to dashboard Cite

show abstract

“…The contraction-expansion coefficient β is the only parameter to be tuned in the QPSO algorithm, and this can be done through Bayesian optimization [92].…”

Section: Quantum-behaved Particle Swarm Optimization (Qpso)mentioning

confidence: 99%

Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

Franco¹,

Steiner²

2021

JGIS

View full text Add to dashboard Cite

The increasing demand for energy has intensified recently, requiring alternative sources to fossil fuels, which have become economically and environmentally unfeasible. On the other hand, the increasing land occupation in recent centuries is a growing problem, demanding greater efficiency, particularly in the reuse of abandoned areas, which has become an alternative. An interesting alternative would be installing energy facilities like solar, wind, biomass, and geothermal, in these areas. The objective of this paper is to develop a classification methodology, based on Artificial Intelligence (AI) and Quantum Theory (QT), to automatically carry out the classification of abandoned areas suitable for the settlement of these power plants. Artificial Neural Networks (ANNs) improved by the hybrid algorithm Quantum-behaved Particle Swarm Optimization (QPSO) together with the Levenberg-Marquardt Algorithm (LMA) were used for the classification task. In terms of Mean Squared Error (MSE), the QPSO-LMA approach achieved a decrease of 19.6% in relation to the classical LMA training with random initial weights. Moreover, the model's accuracy showed an increase of 7.3% for the QPSO-LMA over the LMA. To validate this new approach, it was also tested on six different datasets available in the UCI Machine Learning Repository and seven classical techniques established in the literature. For the problem of installing photovoltaic plants in abandoned areas, the knowledge acquired with the solar dataset can be extrapolated to other regions.

show abstract

“…Due to its super large amount of calculation in large samples, the attention of SVM has declined, but it is still a commonly used machine learning algorithm [9,18,26]. The applications of the SVM have been significantly increased in the last years in multiple sectors as a successful machine learning approach in modeling the relationship between the input and the output in regression problems [8,30,31].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

2021

ITC

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

A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting

Cited by 48 publications

References 34 publications

Temperature Prediction of Photovoltaic Panels Based on Support Vector Machine with Pigeon-Inspired Optimization

Temperature Prediction of Photovoltaic Panels Based on Support Vector Machine with Pigeon-Inspired Optimization

Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

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

Contact Info

Product

Resources

About