advanced mathematics 1 by pandurangappa c pdf

In addition, compared with original particle swarm optimization (PSO) algorithm, QPSO algorithm is tested more efficiently for the improvement of global search ability and robustness during the procedure of parameter selection. The results show that the online direct prediction method is the most accurate in the three prediction methods. Meanwhile, PM2.5 concentration is predicted by three different prediction methods, including the recursive prediction method, direct prediction method, and online direct prediction method. According to hourly PM2.5 and NO2 concentrations and meteorological conditions from May 2014 to April 2015 in Wanliu Monitoring Station of Beijing in China, the experiment is carried out based on the data of 3 months. And in order to improve the problem that the fixed SVR model is difficult to adapt to the highly nonlinear process, a simple online SVR based on re-modeling method is proposed instead of the fixed one. Quantum-behaved particle swarm optimization (QPSO) algorithm is used to select the optimal parameters influencing the performance of SVR. In order to improve the forecasting accuracy of atmospheric pollutant concentration, a prediction model of atmospheric PM2.5 and nitrogen dioxide (NO2) concentration based on support vector regression (SVR) is established. Several experiments based on PM2.5 and PM10 datasets in three cities are analyzed and discussed to verify the excellent performance of our proposed forecasting system, and the results indicate that the forecasting system outperforms others with respect to the accuracy, stability and generalization capabilities which are the basis of a robust air quality early-warning system in practice.

Moreover, Cuckoo Search algorithm applied in the system can determine the optimal weights for models aggregation. Based on the fuzzy theory and decorrelation maximization method, our proposed forecasting system can considering more information and maintaining the diversity of models. Thus, in this paper, a novel fuzzy combination forecasting system based on the data preprocessing, fuzzy theory, and advanced optimization algorithm is proposed to improve the accuracy and stability of forecasting results. However, the accidental and cognitive uncertainty in the model selection or parameter setting of a single system will result in inaccurate and unstable forecasting results. Effective forecasting of the air pollutant concentration is crucial for a robust air quality early-warning system and has both theoretical and practical significance.

Advanced mathematics 1 by pandurangappa c pdf