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1.
基于聚类分析的径向基神经网络用于证候诊断的研究 总被引:17,自引:0,他引:17
目的:优化中医证候诊断模型,为中医证候诊断标准的研究提供可行性方法.方法:提出用于中医证候诊断的径向基(Radial Basis Function,RBF)神经网络,利用聚类分析确定RBF神经网络隐层的参数,运用最小二乘确定RBF神经网络输出层的参数.结果:通过模型检验,证候诊断模型判准率比BP网络模型判准率高;证候诊断模型训练速度比BP网络模型快.结论:基于聚类分析的RBF神经网络用于中医证候诊断的研究是可行的和有效的. 相似文献
2.
As one essential indicator of surface integrity, residual stress has an important influence on the fatigue performance of aero engines’ thin-walled parts. Larger compressive or smaller tensile residual stress is more prone to causing fatigue cracks. To optimize the state of residual stress, the relationship between the surface residual stress and the machining conditions is studied in this work. A radial basis function (RBF) neural network model based on simulated and experimental data is developed to predict the surface residual stress for multi-axis milling of Ti-6Al-4V titanium alloy. Firstly, a 3D numerical model is established and verified through a cutting experiment. These results are found to be in good agreement with average absolute errors of 11.6% and 15.2% in the σx and σy directions, respectively. Then, the RBF neural network is introduced to relate the machining parameters with the surface residual stress using simulated and experimental samples. A good correlation is observed between the experimental and the predicted results. The verification shows that the average prediction error rate is 14.4% in the σx direction and 17.2% in the σy direction. The effects of the inclination angle, cutting speed, and feed rate on the surface residual stress are investigated. The results show that the influence of machining parameters on surface residual stress is nonlinear. The proposed model provides guidance for the control of residual stress in the precision machining of complex thin-walled structures. 相似文献
3.
Background:Chronic kidney disease (CKD) can lead to systemic inflammatory responses and other cardiovascular disease. Diffusion tensor imaging findings generated by gadolinium-based MRI (DTI-GBMRI) is regarded as a standard method for assessing the pathology of CKD. To evaluate the diagnostic value of DTI-GBMRI for renal histopathology and renal efficiency, renal fibrosis and damage, noninvasive quantification of renal blood flow (RBF) were investigated in patients with CKD.Methods:CKD patients (n = 186) were recruited and underwent diagnosis of renal diffusion tensor imaging findings generated by MRI (DTI-MRI) or DTI-GBMRI to identify the pathological characteristics and depict renal efficiency. The cortical RBFs and estimated glomerular filtration rate were compared in CKD patients undergone DTI-GBMRI (n = 92) or DTI-MRI (n = 94).Results:Gadolinium enhanced the diagnosis generated by DTI-MRI in renal fibrosis, renal damage, and estimated glomerular filtration rate. The superiority in sensitivity and accuracy of the DTI-GBMRI method in assessing renal function and evaluating renal impairment was observed in CKD patients compared with DTI-MRI. Outcomes demonstrated that DTI-GBMRI had higher accuracy, sensitivity, and specificity than DTI-MRI in diagnosing patients with CKD.Conclusion:In conclusion, DTI-GBMRI is a potential noninvasive method for measuring renal function, which can provide valuable information for clinical CKD diagnosis. 相似文献
4.
《Computers in biology and medicine》2013,43(9):1182-1191
In this paper, an intelligent hyper framework is proposed to recognize protein folds from its amino acid sequence which is a fundamental problem in bioinformatics. This framework includes some statistical and intelligent algorithms for proteins classification. The main components of the proposed framework are the Fuzzy Resource-Allocating Network (FRAN) and the Radial Bases Function based on Particle Swarm Optimization (RBF-PSO). FRAN applies a dynamic method to tune up the RBF network parameters. Due to the patterns complexity captured in protein dataset, FRAN classifies the proteins under fuzzy conditions. Also, RBF-PSO applies PSO to tune up the RBF classifier. Experimental results demonstrate that FRAN improves prediction accuracy up to 51% and achieves acceptable multi-class results for protein fold prediction. Although RBF-PSO provides reasonable results for protein fold recognition up to 48%, it is weaker than FRAN in some cases. However the proposed hyper framework provides an opportunity to use a great range of intelligent methods and can learn from previous experiences. Thus it can avoid the weakness of some intelligent methods in terms of memory, computational time and static structure. Furthermore, the performance of this system can be enhanced throughout the system life-cycle. 相似文献
5.
Fatemeh Safara Shyamala Doraisamy Azreen Azman Azrul Jantan Asri Ranga Abdullah Ramaiah 《Computers in biology and medicine》2013
Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies. 相似文献
6.
目的研究小细胞肺癌(SCLC)和非小细胞肺癌(NSCLC)的分类问题。方法217例肺癌患者.其中男性165例.殳性52例;年龄35~80岁,平均年龄61.5岁。其中SCLC108例,NSCLC109例。提取患者764幅肺癌CT图像的灰度共生矩阵,选取对比度、熵、能量和逆差矩4个特征值,借助临床确诊结果,利用多层前向(BP)、径向基函数(RBF)人工神经网络对特征进行训练测试。结果BP人工神经网络对10%的78例样本进行测试,SCLC42例预测正确.NSCLC33例预测正确.3例预测失败。RBF神经网络对10%的78例测试样本进行测试,SCLC42例预测正确.NSCLC36例预测正确、类似方法对样本总数的70%进行训练,用30%的230例进行测试;BP人工神经网络有209例预测正确。正确率为90.9%:其中SCLC111例预测正确,正确检出率为88.8%;NSCLC98例预测正确,正确检出率为93.3%。RBF人工神经网络有216例预测正确.正确率为93.9%,其中SCLC117例预测正确,正确率为93.6%;NSCLC99例预测正确,止确检出率为94.3%。可见BP、RBF人1二神经网络对SCLC和NSCLC均具有90%以上的正确率,高于人工诊断结果。结论基于灰度共生矩阵的对比度、熵、能量和逆差矩4个特征值能反映SCLC和NSCLC的有效特征参量.通过人工神经网络能达到分类目的,辅助临床治疗。 相似文献
7.
Fast imaging strategies for mouse kidney perfusion measurement with pseudocontinuous arterial spin labeling (pCASL) at ultra high magnetic field (11.75 tesla) 下载免费PDF全文
8.
[目的]采用ED-NM-MO三联法对经基线等比增减设计的丹参、三七不同配比的药效学数据进行非线性拟合和多目标优化。[方法]对经基线等比增减设计的丹参、三七不同配比的药效学数据,以心肌缺血程度Σ-ST、心肌缺血程度(缺血区左室)等7个分别反映心肌缺血、心脏状态和血流动力学的指标作为待优化的药效目标,进行非线性拟合和多目标优化。[结果]分别得到针对7个药效指标和6个药效指标(不包含血清中心肌钙蛋白)的Pareto最优配比。[结论]ED-NM-MO三联法是一种适合复方特点的优化方法,可以应用于由多饮片多组分多成分复方药物的剂量配比优化。 相似文献
9.
将T-S模糊模型与RBF神经网络相结合,构成T-S模糊RBF神经网络,提出了一种自适应DNA免疫算法优化设计T-S模糊RBF神经网络的规则后件参数的方法。该方法采用基于抗体浓度和克隆选择的更新策略调节机制,能有效地保持抗体的多样性,避免早熟收敛。将该方法应用于延迟焦化汽油干点的软测量建模,仿真结果表明了DNA免疫遗传算法在T-S模糊神经网络系统优化设计中的有效性,并可获得较高精度的模型。 相似文献
10.