scholarly journals Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Hang-cheong Wong ◽  
Pak-kin Wong ◽  
Chi-man Vong
Keyword(s):  
Support Vector Machine ◽  
Relevance Vector Machine ◽  
Pi Controller ◽  
Support Vector ◽  
Machine Model ◽  
Predictive Controller ◽  
Fuel Consumption And Emissions ◽  
Modelling Methods ◽  
Engine Power

Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda) among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM), to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC) algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN) and decremental least-squares support vector machine (DLSSVM). Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC) is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI) controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.

scholarly journals Predicting Music Popularity on Streaming Platforms

2019 ◽  
Author(s):  
Carlos Soares Araujo ◽  
Marco Cristo ◽  
Rafael Giusti
Keyword(s):  
Support Vector Machine ◽  
Public Interest ◽  
Support Vector Machine Model ◽  
Classification Task ◽  
Support Vector ◽  
Popular Song ◽  
Machine Model ◽  
Music Consumption ◽  
Online Streaming

Online streaming platforms have become one of the most important forms of music consumption. Most streaming platforms provide tools to assess the popularity of a song in the forms of scores and rankings. In this paper, we address two issues related to song popularity. First, we predict whether an already popular song may attract higher-than-average public interest and become “viral”. Second, we predict whether sudden spikes in public interest will translate into long-term popularity growth. We base our findings in data from the streaming platform Spotify and consider appearances in its “Most-Popular” list as indicative of popularity, and appearances in its “Virals” list as indicative of interest growth. We approach the problem as a classification task and employ a Support Vector Machine model built on popularity information to predict interest, and vice versa. We also verify if acoustic information can provide useful features for both tasks. Our results show that the popularity information alone is sufficient to predict future interest growth, achieving a F1-score above 90% at predicting whether a song will be featured in the “Virals” list after being observed in the “Most-Popular”.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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