基于RBF网络的增量学习

提出了一种基于径向网络的增量学习算法 ,在对新样本进行学习时 ,通过恢复受干扰样本的方式来尽可能保持已有的知识不变 .模拟实验证明了本算法的正确性和有效性 .     

基于神经网络的建筑行业投标报价研究

在建筑行业的投标报价过程中，如何在众多的信息中选出对最终报价影响较大的几项因素以及如何确定这些因素与最终报价之间的关系是一个棘手的问题．论文针对这两大难题提出了基于神经网络技术的解决方法．首先根据贡献变量分析理论确定出影响报价结果的9个报价因素，从而建立起基于神经网络的报价模型，然后在所确定模型的基础上改进传统的BP算法，进一步提高网络的泛化能力．从实际应用的结果可以看出，经过变量选择后所确定的报价因素是合理的，改进学习算法后的网络的泛化能力也有了很大的提高．     

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.     

高校后勤社会化改革的法律风险及其防范

由于高校后勤社会化模式的混乱,使后勤社会化改革中出现了一系列法律风险。为防范该风险,应在全国实行统一的后勤社会化改革模式——专门机构负责模式,即采用“外部采办”的方式,并通过相关的制度设计来规范高校后勤服务实体的运作。     

逆向物流网络结构研究

文章介绍了逆向物流概念,分析了发展逆向物流的必要性和紧迫性,从逆向物流的结构特征出发,以不同的角度对逆向物流系统做了分类研究,然后从逆向物流的网络着手,分析了逆向物流网络的构建环境、网络特征和网络类型。     

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     

无线异类数据网服务质量体系结构解决方案

设计了一套具有高性能的适合于移动数据网（超2．5G）和无线本地区域网（WLAN）组成的综合数据网的网络体系结构，提供基于预留端到端的QoS模型，使用带宽适应算法支持端到端的QOS，提高了系统资源的利用率，减少了呼叫拥塞概率和交接丢包概率，解决了2个系统QoS类不匹配等问题，平衡了2个系统的传输能力。