基于模糊信息粒化和交叉验证算法的支持向量机(CV-SVM)预测时序回归模型,首先以欧洲碳排放配额(European union allowances,EUA)结算价为数据样本,预测未来连续5天的碳价波动情况,验证模型的可靠性及可用性.在得出科学结论的基础上,以...基于模糊信息粒化和交叉验证算法的支持向量机(CV-SVM)预测时序回归模型,首先以欧洲碳排放配额(European union allowances,EUA)结算价为数据样本,预测未来连续5天的碳价波动情况,验证模型的可靠性及可用性.在得出科学结论的基础上,以我国碳交易市场中湖北碳排放配额(Hubei emission allowances,HBEA)结算价及北京碳排放配额(Beijing emission allowances,BEA)结算价,两组典型的价格数据为例,利用该模型进行训练,对我国碳交易市场中未来连续5天内碳价的变化趋势和波动区间给出有效预测.结果显示,该模型对两类碳交易市场的预测结果均较为理想,预测值误差率最大为3.36%,但针对欧洲碳交易市场的预测更为精确,预测值误差率不超过1.21%,在一定程度上反映了中国的碳交易市场尚不成熟,碳价波动规律性较弱,需要在正确的政策引导下快速、健康发展.展开更多
提出一种基于最优样本子集的在线模糊最小二乘支持向量机(least squares support vector machine,LSSVM)混沌时间序列预测方法.算法选择与预测样本时间上以及欧氏距离最近的样本点构成最优样本子集,并采用ε不敏感函数对其进行模糊化处...提出一种基于最优样本子集的在线模糊最小二乘支持向量机(least squares support vector machine,LSSVM)混沌时间序列预测方法.算法选择与预测样本时间上以及欧氏距离最近的样本点构成最优样本子集,并采用ε不敏感函数对其进行模糊化处理,通过模糊LSSVM训练获得预测模型.随着时间窗口的滑动,最优样本子集和预测模型实时更新,模型更新采用分块矩阵方法降低运算复杂度.实验中对时变Ikeda序列进行预测,表明所提出的方法与离线和在线LSSVM相比,训练速度更快,预测精度更高.展开更多
A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural langu...A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine （SVM） theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.展开更多
文摘提出一种基于最优样本子集的在线模糊最小二乘支持向量机(least squares support vector machine,LSSVM)混沌时间序列预测方法.算法选择与预测样本时间上以及欧氏距离最近的样本点构成最优样本子集,并采用ε不敏感函数对其进行模糊化处理,通过模糊LSSVM训练获得预测模型.随着时间窗口的滑动,最优样本子集和预测模型实时更新,模型更新采用分块矩阵方法降低运算复杂度.实验中对时变Ikeda序列进行预测,表明所提出的方法与离线和在线LSSVM相比,训练速度更快,预测精度更高.
基金This work was partially supported by the Royal Society of UK and the National Natural Science Foundation of PRC (No. 60175028).
文摘A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine （SVM） theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.
