Learning Convergence of CMAC Algorithm

CMAC convergence properties both in batch and in incremental learning are analyzed. The previous conclusions about the CMAC convergence, which are deduced under the condition that the articulation matrix is positive definite, are improved into the new less limited and more general conclusions in which no additive conditions are needed. An improved CMAC algorithm with self-optimizing learning rate is proposed from the new conclusions. Simulation results show the correctness of the new conclusions and the advantages of the improved algorithm.     

序列模式挖掘的增量式算法的设计原则

在序列模式的分层算法框架下,从理论上分析并讨论了数据集的渐进性和算法参数的相似性为增量式挖掘带来的启发信息,提出了增量式挖掘算法设计中的４项原则,并结合任务分解原则研究了搜索空间的分割。     

一个基于部分求值的增量式数据库引擎

文中介绍一个采用增量计算进行数据库操作的数据库引擎,阐述了利用部分求值技术实现增量式数据库查询的方法,给出了增量式数据库引擎的系统框架与实现方法。性能测试的实验结果的说明了这种数据库引擎的使用能够有效地提高数据库查询的效率。     

不确定性操作臂的混合模糊P+ID控制的实验研究

提出了一种机械臂的混合模糊控制策略,它由一企增量模糊逻辑控制器和传统的积分－微分管制器构成,简称ＰｕｚｚｙＰ＋ＩＤ控制器,与传统的ＰＩＤ控制器相比,设计ＦｕｚｚｙＰ＋ＩＤ控制器时,只需多调节一个附加参数,我们应用ＦｕｚｚｙＰ＋ＩＤ控制器控制几何参数变化的两连杆机构臂ＥＤＤＡ,ＥＤＤＡ的阶跃和跟踪控制的实验结果表明：ＦｕｚｚｙＰ＋ＩＤ控制器更有效和更鲁棒。     

Efficient maintenance of multiple-level association rules for deletion of records

Developing an efficient algorithm that can maintain discovered information as a database changes is quite important in data mining. Many proposed algorithms focused on a single level, and did not utilize previously mined information in incrementally growing databases. In the past, we proposed an incremental mining algorithm for maintenance of multiple-level association rules as new transactions were inserted. Deletion of records in databases is, however, commonly seen in real-world applications. In this paper, we thus attempt to extend our previous approach to solve this issue. The concept of pre-large itemsets is used to reduce the need for rescanning original databases and to save maintenance costs. A pre-large itemset is not truly large, but promises to be large in the future. A lower support threshold and an upper support threshold are used to realize this concept. The two user-specified upper and lower support thresholds make the pre-large itemsets act as a gap to avoid small itemsets becoming large in the updated database when transactions are deleted. A new algorithm is thus proposed based on the concept to maintain discovered multiple-level association rules for deletion of records. The proposed algorithm doesn＇t need to rescan the original database until a number of records have been deleted. It can thus save much maintenance time.     

Incremental Evolution in ANNs: Neural Nets which Grow

This paper explains the optimisation of neuralnetwork topology using Incremental Evolution;that is, by allowing the network to expand byadding to its structure. This method allows anetwork to grow from a simple to a complexstructure until it is capable of fulfilling itsintended function. The approach is somewhatanalogous to the growth of an embryo or theevolution of a fossil line through time, it istherefore sometimes referred to as anembryology or embryological algorithm. Thepaper begins with a general introduction,comparing this method to other competingtechniques such as The Genetic Algorithm, otherEvolutionary Algorithms and SimulatedAnnealing. A literature survey of previous workis included, followed by an extensive newframework for application of the technique.Finally, examples of applications and a generaldiscussion are presented.     

约束关联规则的增量式维护算法

关联规则的挖掘是一个重要的数据挖掘问题,在关联规则的挖掘过程中加入约束条件,是实现用户参与挖掘的一种方式。在有约束的关联规则挖掘过程中,用户会不断调整约束条件,并要求更新挖掘结果。针对这种情况,提出了约束关联规则的增量式维护算法Separate_M,当约束条件发生变化时,在原有挖掘结果的基础上实现增量式更新,较重新运行Separate算法而言,减小了搜索空间,节约了时间,提高了挖掘效率。     

工件坐标系编程的译码程序设计

数控系统对零件加工程序段中的坐标值译码最为复杂,采用C语言开发了数控系统中的译码程序,实现了工件坐标系下的编程译码,为今后实现更多编程方法提供了方便。     

可用于人脸识别的反馈型二元神经网络

提出和分析了一种新型的反馈型随机神经网络,并将其用于解决复杂的人脸识别问题.该模型采用随机型加权联接,神经元为简单的非线性处理单元.理论分析揭示该网络模型存在唯一的收敛平稳概率分布,当网络中神经元个数较多时,平稳概率分布逼近于Boltzmann-Gibbs分布,网络模型与马尔可夫随机场之间存在密切关系.在设计了一种新型模拟退火和渐进式Boltzmann学习算法后,系统被成功地应用于难度较大的静态和动态人像识别,实验结果证实了系统的可行性和高效率.     

CMAC 算法收敛性分析及泛化能力研究

利用矩阵理论和线性方程组迭代收敛的一般性原理,在不附加特殊条件折情况下,证明了CMAC算法在批量和增量两种学习方式下的收敛定理,对在关联矩阵正定条件下得出的结论进行推广和改进。在此基础上提出了一种学习率自寻优的CMAC改进算法,并提出一种简单可行的评价CMAC网络整体泛化性能的指标,通过计算仿真验证了收敛定量的正确性和改进算法的优越性,并研究得出了CMAC网络各个参数对其泛化性能影响的相关结论。