The aim is to study the set of subsets of grids of the Waterloo language from the point of view of abstract algebra and graph theory. The study was conducted using the library for working with transition graphs of non...The aim is to study the set of subsets of grids of the Waterloo language from the point of view of abstract algebra and graph theory. The study was conducted using the library for working with transition graphs of nondeterministic finite automata NFALib implemented by one of the authors in C#, as well as statistical methods for analyzing algorithms. The results are regularities obtained when considering semilattices on a set of subsets of grids of the Waterloo language. It follows from the results obtained that the minimum covering automaton equivalent to the Waterloo automaton can be obtained by adding one additional to the minimum covering set of grids. .展开更多
As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorit...As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.展开更多
针对现存陆地移动卫星(land mobile satellite,LMS)信道模型使用场景有限及状态持续时间概率密度函数(state duration probability density function,SDPDF)不精确问题,提出能准确有效地描述更多操作场景的窄带LMS信道模型.利用对数正...针对现存陆地移动卫星(land mobile satellite,LMS)信道模型使用场景有限及状态持续时间概率密度函数(state duration probability density function,SDPDF)不精确问题,提出能准确有效地描述更多操作场景的窄带LMS信道模型.利用对数正态分布近似SDPDF的Semi-Markov链实现状态序列.在每一状态内结合传播参数生成服从Loo分布的载噪比时间序列.通过在慢衰落成分中引入多普勒频移以增加参数数据库及扩展适用场景.研究不同场景下状态概率、载噪比时间序列的一阶统计特性和90%信号有效性所需的余量,并与实测值比较.研究结果表明:该模型能在多场景下有效地代表阴影条件,且在不同信道环境中的各仰角和方位角下具有较好适用性,可用于系统性能分析.展开更多
文摘The aim is to study the set of subsets of grids of the Waterloo language from the point of view of abstract algebra and graph theory. The study was conducted using the library for working with transition graphs of nondeterministic finite automata NFALib implemented by one of the authors in C#, as well as statistical methods for analyzing algorithms. The results are regularities obtained when considering semilattices on a set of subsets of grids of the Waterloo language. It follows from the results obtained that the minimum covering automaton equivalent to the Waterloo automaton can be obtained by adding one additional to the minimum covering set of grids. .
基金supported by the National Natural Science Foundation of China (61074127)
文摘As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.
文摘针对现存陆地移动卫星(land mobile satellite,LMS)信道模型使用场景有限及状态持续时间概率密度函数(state duration probability density function,SDPDF)不精确问题,提出能准确有效地描述更多操作场景的窄带LMS信道模型.利用对数正态分布近似SDPDF的Semi-Markov链实现状态序列.在每一状态内结合传播参数生成服从Loo分布的载噪比时间序列.通过在慢衰落成分中引入多普勒频移以增加参数数据库及扩展适用场景.研究不同场景下状态概率、载噪比时间序列的一阶统计特性和90%信号有效性所需的余量,并与实测值比较.研究结果表明:该模型能在多场景下有效地代表阴影条件,且在不同信道环境中的各仰角和方位角下具有较好适用性,可用于系统性能分析.
