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Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

机译：基于粒子群优化的组合核函数 - 最小二乘支持向量机结合混沌理论的短时交通流预测模型

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Qiang Shang; Ciyun Lin; Zhaosheng Yang; Qichun Bing; Xiyang Zhou;
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年度 2016
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1. Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory: [J] . Qiang Shang, Ciyun Lin, Zhaosheng Yang, Advances in Mechanical Engineering . 2016,第8期

机译：基于粒子群算法的组合核函数最小二乘支持向量机的短期交通流量预测模型与混沌理论的结合：
2. Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model [J] . Bing Qichun, Gong Bowen, Yang Zhaosheng, Mathematical Problems in Engineering . 2015,第PTa19期

机译：基于组合核函数相关向量机模型的短时交通流局部预测
3. Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model [J] . QichunBing, BowenGong, ZhaoshengYang, Mathematical Problems in Engineering: Theory, Methods and Applications . 2015,第5期

机译：基于组合核函数相关向量机模型的短时交通流局部预测
4. STUDY OF SHORT-TERM TRAFFIC FLOW FORECASTING MODELS BASED ON CHAOS - SUPPORT VECTOR MACHINE THEORY [C] . YANG RENDANG, LI WEIWEI, YANG FEI, ITS for a better life . 2007

机译：基于混沌支持向量机理论的短时交通流预测模型研究。
5. Combining biomarkers and clinicopathologic factors for prediction of response to adjuvant chemotherapy for breast cancer: Cox model and Support Vector Machine (SVM) methods. [D] . Liu, Xudong. 2010

机译：结合生物标志物和临床病理因素预测乳腺癌对辅助化疗的反应：Cox模型和支持向量机（SVM）方法。
6. Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization [O] . Haifeng Xing, Bo Hou, Zhihui Lin, 2017

机译：基于最小二乘支持向量机的混沌粒子群优化的MEMS陀螺随机漂移建模与补偿
7. Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization [O] . Haifeng Xing, Bo Hou, Zhihui Lin, 2017

机译：基于混沌粒子群算法优化的最小二乘支持向量机的mEms陀螺随机漂移建模与补偿

Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

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