测试设备故障诊断专家系统的设计与实现

通过分析测试设备故障诊断专家系统的目的和任务,将模糊神经网络理论与专家系统结合后用于测试设备故障诊断,建立了故障诊断专家系统的模型。探讨了故障诊断专家系统中知识库的构建及维护、不精确推理等问题。用面向对象分析方法分析测试设备故障诊断专家系统,并用软件加以实现。     

基于灰色神经网络的疲劳裂纹预测方法研究

为对构件疲劳损伤进行预测,提出了基于灰色神经网络模型的疲劳裂纹扩展预测方法。将灰色GM（I,1）模型向BP网络映射,建立了一维灰色神经网络GNNM（1,1）模型。基于灰色GM（1,1）模型的发展系数和灰作用量给出了GNNM（1,1）模型初始权值。应用建立的GNNM（1,1）模型预测了某不锈钢构件腐蚀疲劳裂纹的扩展,并与GM（I,1）模型的预测结果进行了对比,表明GNNM（1,1）模型具有更高的预测精度和模型精度。     

基于小波神经网络的无叶扩压器失速分析

用来自不同文献的实验数据作为样本,对小波神经网络进行训练,并利用训练好的神经网络研究叶轮和扩压器几何尺寸对无叶扩压器失速的影响.计算分析结果表明:几何尺寸对于宽、窄扩压器失速的影响表现不同,这也证明了不同宽度的扩压器的确存在不同的失速机理.文章的结果,对无叶扩压器的设计具有一定的借鉴意义.      

沟槽壁湍流多尺度相干结构实验研究

应用热线测速技术,对沟槽壁面平板湍流边界层的减阻机理进行了实验研究.测量了风洞中并排放置的沟槽壁面平板及光滑壁面平板湍流边界层在不同雷诺数下不同法向位置的瞬时流向、法向速度分量的时间序列信号.运用流向速度分量信号的多尺度子波系数辨识壁湍流多尺度相干结构,用条件采样和相位平均技术提取了壁湍流多尺度相干结构喷射和扫掠时流向速度分量、法向速度分量和雷诺应力分量的相位平均波形.分析了沟槽壁面平板及光滑壁面平板湍流边界层中多尺度相干结构的持续时间、条件相位平均波形等特征.对沟槽壁面平板及光滑壁面平板湍流边界层多尺度相干结构的多种统计平均特征进行了比较,从壁湍流多尺度相干结构控制的角度研究了沟槽壁面平板湍流边界层的减阻机理.      

一种航空发动机性能监控的免疫神经网络模型

提出了一种基于免疫神经网络的航空发动机性能监控与故障检测模型.利用人工免疫系统的识别机理,并结合人工神经网络,确定发动机性能偏离正常值的程度(异常度),实现发动机性能趋势的监控.该方法能够灵敏、准确地反映发动机整体性能的变化情况,提高发动机性能正常与否的识别率,并以此来发现发动机潜在早期故障,防止故障的扩大.通过对某型涡扇发动机进行监控,证明该方法确实有效.      

分形理论和小波在脉象识别中的应用

首先介绍了运用现代科学对脉象分类的意义和传统的脉象分类方法,尔后提出利用分形理论提取脉搏信号的分数维和基于小波变换的多尺度空间能量分布特征作为波形的重要特征,用于脉象分类识别.利用这些特征矢量用BP神经网络对实测的脉象数据进行了分类,取得了令人满意的分类正确率.通过两种提取方法相比较,盒维数方法更简单易行.     

星载FPGA内时序电路设计与时钟控制技术分析

在分析星载FPGA内时序电路特性以及FPGA可编程资源特性的基础上,指出了FPGA内同步时序电路出现时钟偏斜现象的机理。针对时钟偏斜,提出了星载FPGA内时序电路的设计准则。基于设计准则,提出了并行移位寄存器的一种异步化设计方法,阐述了在FPGA源代码中设置设计约束,或在逻辑综合与布局布线过程中联合设置设计约束,将主要同步时序电路时钟信号布置在全局时钟网络上的方法。工程实践表明：上述方法很好地解决了星载FPGA内同步时序电路时钟偏斜问题,可确保星载FPGA工作的稳定性与可靠性。     

基于人工神经网络的可压缩湍流大涡模拟模型

在国家数值风洞（NNW）工程项目的指导下，空间人工神经网络（SANN）模型被用于强可压缩湍流大涡模拟（LES）研究，其中流场的湍流马赫数分别为0.6、0.8、1.0。基于湍流的多尺度空间结构特性和人工神经网络方法发展的高精度空间神经网络（SANN）模型适用于不可压缩湍流和弱可压缩湍流。对于强可压缩湍流，流场中会出现激波结构，给大涡模拟带来了挑战。本文的研究结果表明：SANN模型适用于强可压缩湍流的大涡模拟。在先验分析中，SANN模型预测的亚格子应力和亚格子热流的相关系数超过0.995，远远高于梯度模型和近似反卷积模型等传统模型；传统模型的相对误差大于30%，而SANN模型在这方面有很大的改进，相对误差低于11%。在后验分析中，与隐式大涡模拟（ILES）、动态Smagorinsky模型（DSM）、动态混合模型（DMM）相比，SANN模型能更精确地预测能谱、各类湍流统计特性以及瞬态流动结构。因此，基于湍流多尺度空间结构特性的人工神经网络模型加深了人们对强可压缩湍流亚格子建模的认识，同时可以服务于NNW工程的流体力学模型构造。     

Development cost prediction of general aviation aircraft projects with parametric modeling

The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.     

Integration of artificial intelligence with meta classifiers for the gully erosion susceptibility assessment in Hinglo river basin,Eastern India

The main aim of this study is to evaluate the gully erosion susceptibility coupling the artificial intelligence and machine learning ensemble approaches. In the present study, the multilayer perceptron neural network (MLP) was used as the base classifier and the hybrid ensemble machine learning methods i.e. Bagging and Dagging were used as the functional classifiers. The Hinglo river basin, an important tributary of the Ajay River was selected as the study area, consists with the parts of Chhotonagpur plateau and Rarh lateritic region. The study area is facing the gully erosion problems which are interrupted the growth of the agriculture. The gully erosion susceptibility maps (GESMs), prepared by MLP, MLP-Bagging and MLP-Dagging were classified into four classes such as low, moderate, high and very high susceptibility classes with the help of natural break method (NBM) in GIS environment. The very high susceptibility class covered 19.41% (MLP), 13.52% (MLP-Bagging) and 15.30% (MLP-Dagging) areas of the basin. For the evaluation and comparison of the models, receiver operating characteristics (ROC), accuracy, mean absolute error (MAE) and root mean square error (RMSE) were applied. Overall, all the gully erosion susceptibility models were performed as excellent. Integration of hybrid ensemble models with MLP has increase the accuracy of the MLP models. Among these models MLP-Dagging has achieved the highest accuracy in compare to the other models. The importance of the selected factors in the present study was assessed by the Relief-F method. The results show that the soil type factor has the highest predictive performance. Sensitivity analysis also showed soil type as most important factor. The gully erosion susceptibility maps (GESMs) are considered as the efficient tool which could be used to take the necessary steps for mitigating and controlling the soil erosion problem and sustainable environmental management and development.