The performance of PSO-SVM in inflation forecasting

Tang, Yizhou; Zhou, Jiawen

doi:10.1109/icsssm.2015.7170251

Cited by 13 publications

(6 citation statements)

References 5 publications

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“…In [39] a novel approach has been proposed to training noise-resilient concept detectors from click through data collected by image search engines. The SVM-based models (Fixed-SVM, PSO-SVM, GA-SVM) together with a BP neural network are employed to forecast Chinese inflation rate [47]. In [2], decomposition methods for SVM classification functions are developed and discussed, using polynomial approximation methods.…”

Section: Svm -Methods For Harmonic Estimationmentioning

confidence: 99%

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Özdemir¹,

Demirtaş²,

Aydın³

2016

NNW

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Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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Section: Svm -Methods For Harmonic Estimationmentioning

confidence: 99%

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Özdemir¹,

Demirtaş²,

Aydın³

2016

NNW

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“…Based on finite sample statistical learning theory and structural risk minimization principle, Vapnik proposed the support vector machine [18]. This machine learning approach has abundant unique benefits in solving small samples, nonlinear and high-dimensional pattern recognition.…”

Section: Principle and Training Of Svr Modelmentioning

confidence: 99%

Research on nonlinear compensation scheme of yarn tension sensor using SAW devices based on SSA–SVR model

Liu¹,

Xing²,

Duan³

et al. 2023

Meas. Sci. Technol.

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The detection accuracy of a yarn tension sensor using surface acoustic wave (SAW) devices has become increasingly important. We investigate a nonlinear compensation scheme based on SSA and SVR models to improve its detection accuracy, and the principle of SSA--SVR model and training method are also explored. We take the output frequency of the two sensors as input, the yarn tension applied to the working sensor as output, train an SSA--SVR model and use it for nonlinear compensation. We analyze and calculate the linearity, compensation accuracy and robustness of the SSA-SVR model, and compared it with the multiple regression model and BP neural network. The comparison results show that the SSA--SVR model has the best linearity, the highest compensation accuracy and the most robust. Finally, a novel nonlinear compensation scheme is proposed.

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“…This kernel is used when the variation in the data is small [99][100][101][102][103][104]. The polynomial kernel is expressed mathematically as:…”

Section: Support Vector Machinementioning

confidence: 99%

Solar Photovoltaic Power Forecasting: A Review

Iheanetu

2022

Sustainability

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The recent global warming effect has brought into focus different solutions for combating climate change. The generation of climate-friendly renewable energy alternatives has been vastly improved and commercialized for power generation. As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power supply. There has been tremendous growth in both on- and off-grid solar PV installations in the last few years. This trend is expected to continue over the next few years as government legislation and awareness campaigns increase to encourage a shift toward using renewable energy alternatives. Despite the numerous advantages of solar PV power generation, the highly variable nature of the sun’s irradiance in different seasons of various geopolitical areas/regions can significantly affect the expected energy yield. This variation directly impacts the profitability or economic viability of the system, and cannot be neglected. To overcome this challenge, various procedures have been applied to forecast the generated solar PV energy. This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented. The clarity provided will form a basis for higher accuracy in future models and applications.

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The performance of PSO-SVM in inflation forecasting

Cited by 13 publications

References 5 publications

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Research on nonlinear compensation scheme of yarn tension sensor using SAW devices based on SSA–SVR model

Solar Photovoltaic Power Forecasting: A Review

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