Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks

Abstract: After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks isintroduced, an intelligent PID controller is adopted to correct the errors including identified model errors and accumulatederrors produced in the recursive process. Characterized by predictive control, this method can achieve a good controlaccuracy and has good robustness. A simulation study shows that this control algorithm is very effective.     

Petri网络在无控制交叉口延误分析中的应用

选用比较适合描述系统组织结构和动态行为的Petri网络模型对主路优先通行的无信号交叉口运行过程建立HSPN模型．在此基础上，结合间隙接受理论，对无控制交叉口进行平均延误分析探索，为交叉口仿真控制软件的研发奠定了算法基础。     

Discovering Frequent Subtrees from XML Data Using Neural Networks

By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML has been available on Internet. In this paper, we study a data-mining problem of discovering frequent ordered sub-trees in a large collection of XML data, where both of the patterns and the data are modeled by labeled ordered trees. We present an efficient algorithm of Ordered Subtree Miner （OSTMiner） based on two- layer neural networks with Hebb rule, that computes all ordered sub-trees appearing in a collection of XML trees with frequent above a user-specified threshold using a special structure EM-tree. In this algo- rithm, EM-tree is used as an extended merging tree to supply scheme information for efficient pruning and mining frequent sub-trees. Experiments results showed that OSTMiner has good response time and scales well.     

亚双曲型广义Cartan矩阵的分类

本文解决了一类非双曲型的不定型（亚双曲型）广义Ｃａｒｔａｎ矩阵的分类问题．证明了亚双曲型矩阵的阶数范围，并做出了阶数为３，５，６的亚双曲型广义Ｃａｒｔａｎ矩阵的Ｄｙｎｋｉｎ图．     

传感器网络推拉混合模式代码传输方法

代码传输是实现无线传感器网络应用重构的关键技术.首先研究传感器网络环境自适应应用重构(EAAR)模型的两种代码传输模式-拉模式和推模式,并结合两种模式的特点提出一种适用于分簇传感器网络的能量有效代码传输模式-基于簇的混合代码传输(Cluster-based Hybrid Code Transmission, CHCT).在该模式下,簇头节点采用拉方式从基站获取代码,簇内节点采用推方式进行代码传输.仿真实验验证了方法的有效性并讨论了影响CHCT性能的主要参数.     

Efficient and power-awareness query over sensor networks

Sensor networks consisted of low-cost, low-power, multifunctional miniature sensor devices have played an important role in our daily life. Light and humidity monitoring, seismic and animal activity detection, environment and habitat monitoring are the most common applications. However, due to the limited power supply, ordinary query methods and algorithms can not be applied on sensor networks. Queries over sensor networks should be power-aware to guarantee the maximum power savings. The minimal power consumption by avoiding the expensive communication of the redundant sensor nodes is concentrated on. A lot of work have been done to reduce the participated nodes, but none of them have considered the overlapping minimum bounded rectangle (MBR) of sensors which make them impossible to reach the optimization solution. The proposed OMSI-tree and OMR algorithm can efficiently solve this problem by executing a given query only on the sensors involved. Experiments show that there is an obvious improvement compared with TinyDB and other spatial index, adopting the proposed schema and algorithm.     

Conflict Pattern Analysis: Preparing the Ground for Participation in Policy Implementation

The participation of non-state actors in implementation processes is often understood as a means to increase compliance efficiency. But the implementation of spatial policies frequently focuses on pre-established goals, processes and instruments and thus renders difficult open discourse and shared decision-making. This paper introduces conflict pattern analysis (CPA) as a tool that supports the analysis of the actual actor constellation in order to define efficient approaches that avoid common problems of participatory processes. CPA is a semi-formalised method that helps to identify key-actors, their relations and interaction amongst each other as well as their core beliefs, interests and resources. It aggregates this information to interaction patterns that can be compared, classified and linked to different participatory methods on a theoretically informed basis. Particularly on the local and regional level, this could be the first step for successful (participatory) implementation strategies.     

Samples Selection for Artificial Neural Network Training in Preliminary Structural Design

An artificial neural network (ANN) is applied in the preliminary structural design of reticulated shells. Major efforts are made to enhance the generalization ability of networks through well-selected training samples. Number-theoretic methods (NTMs) are adopted to generate samples with low discrepancy, i.e.uniformly scattered in the domain, where discrepancy is a quantitative measurement of the uniformity. The discrepancy of the NTM-based sample set is 1/6-1/7 that of samples with equal spacing. In a case study,networks trained by NTM-based samples are compared with those trained by equal-spaced samples in generalizing performance. The results show that both the computational precision and stability of the former ANNs are more satisfactory than those of the latter. It is concluded that the flexibility of ANNs in generalizing can be effectively increased by use of uniformly distributed training samples rather than simply piling data.More reliable uniformity should be obtained, however, through NTMs instead of equal-spaced samples.     

人工神经网络紫外分光光度法同时测定苯和甲苯

应用三层ANN_BP网络解析苯和甲苯的吸收光谱 ,不经分离 ,用紫外分光光度法同时测定苯和甲苯 .使用改进的BP算法 ,避免了神经网络学习过程中可能产生的麻痹现象 ,提出了目标向量的简单变换方法及便于网络参数选择的收敛评价函数 .实验验证了方法的有效性 ,同时表明 ,苯在 0 - 72 0mg/L和甲苯在 0 - 84 0mg/L范围内服从比尔定律     

神经网络自适应控制器设计

对前馈神经网络用于动态系统的辨识、自适应控制进行了分析和数学推导，给出了自适应神经网络控制器的设计方法．最后通过仿真实例进行了说明．