希尔伯特变换器优化设计研究

提出了一种基于神经网络算法的希尔伯特变换器优化设计方法，证明了神经网络算法的收敛性，提出了希尔伯特变换器的优化设计实例。仿真结果表明了该网络模型是高效的神经网络模型。     

Accurate forecasting of the undecided population in a public opinion poll

The problem of pollsters is addressed which is to forecast accurately the final answers of the undecided respondents to the primary question in a public opinion poll. The task is viewed as a pattern‐recognition problem of correlating the answers of the respondents to the peripheral questions in the survey with their primary answers. The underlying pattern is determined with a supervised artificial neural network that is trained using the peripheral answers of the decided respondents whose primary answers are also known. With peripheral answers as inputs, the trained network outputs the most probable primary response of an undecided respondent. For a poll conducted to determine the approval rating of the (former) Philippine president, J. E. Estrada in December 1999 and March 2000, the trained network predicted with a 95% success rate the direct responses of a test population that consists of 24.57% of the decided population who were excluded in the network training set. For the undecided population (22.67% of December respondents; 23.67% of March respondents), the network predicted a final response distribution that is consistent with the approval/disapproval ratio of the decided population. Copyright © 2002 John Wiley & Sons, Ltd.     

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．     

基于模糊神经网络方法的新构造控水专家系统

在对大量野外地质资料统计和综合分析基础上，依据地质专家在新构造领域的理论知识和成功经验，运用模糊数学理论和神经网络理论相结合的方法，提出一种用线性单元并联实现非线性划分的神经元网络模型，并研制出相应具有较强推理能力和学习功能、广泛用于非定性系统数据处理的专家系统，该系统经实例验证具有较强的适用性。     

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.     

基于灰色系统与神经网络的航材消耗广义加权函数平均组合预测模型研究

建立了航材消耗的灰色系统预测模型与神经网络预测模型，同时给出了一种新的具有广泛代表性的组合预测模型－－广义加权函数平均组合预测模型及其加权系数的参数估计方法，并利用此方法建立了基于灰色系统与神经网络的航材消耗广义加权函数平均组合预测模型，最后以实例说明了其预测效果。     

一种基于模糊神经网络的自适应模糊辨识方法

基于改进的T-S模型，提出一种自适应模糊神经网络模型(AFNN)。首先，基于模糊竞争学习算法确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。其次，利用卡尔曼滤波算法在线辨识AFNN的后件参数。AFNN具有结构简洁，逼近能力强，能够显著提高辨识精度，并且辨识的模糊模型简单有效。最后，将该AFNN用于非线性系统的模糊辨识，仿真结果验证了该方法的有效性。     

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.     

层析成像的模糊-神经网络重建方法

考虑投影数据不足时，射线层析成像的离散重建算法难以取得较好效果的情况，利用模糊理论建立了表示物体衰减系数的连续函数，用神经网络学习算法确定该连续函数中的待定参数．提出了一种适于投影数据或投影方向不够时的CT图像重建算法．计算机仿真结果显示了该方法在投影数据严重缺少时的良好效果。