基于BP神经网络的金属材料字符识别研究

字符识别是模式识别领域的一项传统课题,其内容是模式识别领域中很多课题的基本内容。人工神经网络的出现为字符识别的研究提供了一种新的手段,BP神经网络(Back Propagation Neural Network)作为人工神经网络的一个分支,现已成为其最广泛的应用。本文以三层BP网络作为模型,并将其应用于对金属角铁上的字符识别。由于角铁字符为数字与英文字母混合,文中在对传统的BP算法进行了改进的基础上,采用了分组神经网络的设计方法,取得了良好的识别效果。     

人工神经网络技术估测镀锌钢板点焊性能的研究

本文介绍了如何将人工神经网络引入到镀锌钢板点焊的质量预测中,通过镀锌钢板点焊工艺试验采集训练样本,建立焊接电流和焊接时间对点焊接头强度的ANN预测网络。验证结果表明,学习后的神经网络预测精度较好,能够合理地预测焊接电流和焊接时间对镀锌钢板点焊接头强度的影响规律。     

普通高校体育考核评价体系的实践探索

对人工智能以及人工神经网络产生、发展的基本思想做了简单阐述,从中归纳出:科学发展的大趋势;人工智能和科学哲学之间的相互作用以及彼此促进.引发了机器智慧超越人类智慧的思考,并从哲学角度得到高智能化的机器只能是人类的智慧结晶无法超越人类的结论.     

江西现代物流业及物流园区的发展平台--基于神经网络的物流园选址规划的建模研究

方兴未艾的物流园建设对于保证中国的快速发展是致关重要的。但它的规划频频失误也确实给我国带来了许多新的问题。物流园的规划是一个动态的复杂的系统工程,其存在着大量的非结构化的黑箱问题难以用传统的数学方法(如运筹学、统计学)及系统动力学方法、专家系统解决。本篇文章旨在引入一种新的思路方法即利用神经网络模型对我国物流园的规划进行建模分析,并尝试将该模型用在江西的两个已建、待建的物流园的选址规划评价中。本人非常期盼该模型的建立与使用对规避我国物流园建设的巨大投资风险具有一定的现实意义。     

Serum protein fingerprinting coupled with artificial neural network distinguishes glioma from healthy population or brain benign tumor

To screen and evaluate protein biomarkers for the detection of gliomas (Astrocytoma grade Ⅰ-Ⅳ) from healthy individuals and gliomas from brain benign tumors by using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) coupled with an artificial neural network (ANN) algorithm. SELDI-TOF-MS protein fingerprinting of serum from 105 brain tumor patients and healthy individuals, included 28 patients with glioma (Astrocytoma Ⅰ-Ⅳ), 37 patients with brain benign tumor, and 40 age-matched healthy individuals. Two thirds of the total samples of every compared pair as training set were used to set up discriminating patterns, and one third of total samples of every compared pair as test set were used to cross-validate; simultaneously, discriminate-cluster analysis derived SPSS 10.0 software was used to compare Astrocytoma grade Ⅰ-Ⅱ with grade Ⅲ-Ⅳ ones. An accuracy of 95.7%, sensitivity of 88.9%, specificity of 100%, positive predictive value of 90% and negative predictive value of 100% were obtained in a blinded test set comparing gliomas patients with healthy individuals; an accuracy of 86.4%, sensitivity of 88.9%, specificity of 84.6%, positive predictive value of 90% and negative predictive value of 85.7% were obtained when patient's gliomas was compared with benign brain tumor. Total accuracy of 85.7%, accuracy of grade Ⅰ-Ⅱ Astrocytoma was 86.7%, accuracy ofⅢ-Ⅳ Astrocytoma was 84.6% were obtained when grade Ⅰ-Ⅱ Astrocytoma was compared with grade Ⅲ-Ⅳ ones (discriminant analysis). SELDI-TOF-MS combined with bioinformatics tools, could greatly facilitate the discovery of better biomarkers. The high sensitivity and specificity achieved by the use of selected biomarkers showed great potential application for the discrimination of gliomas patients from healthy individuals and glioma from brain benign tumors.     

微机在继电保护中应用的历史、现状及未来

随着计算技术与通信技术、电子技术的迅猛发展,电力系统继电保护也进入微机继电保护发展时期。简述了微机继电保护的发展史,归纳了微机保护具有的优势。着重介绍了微机继电保护的新趋势：自适应控制技术、人工神经网络、变电站综合自动化技术的应用。     

Regression-Based Artificial Neural Network Methodology In Response Surface Methodology

Response surface methodology (RSM) is an important tool for process parameter optimization, robust design and other quality improvement efforts. When the relationship between influential input variables and output response is very complex, it‘ s hard to find the real response surface using RSM. In recent years artificial neural network (ANN) has been used in RSM. But the classical ANN does not work well under the constraints of real applications. An algorithm of regression-based ANN(R-ANN) is proposed in this paper, which is a supplement to the classical ANN methodology. It makes network closer to the response surface, so that training time is reduced and robustness is strengthened. The procedure of improving ANN by regressions is described and the comparisons among R-ANN, RSM and classical ANN are computed graphically in three examples. Our research shows that the R-ANN methodology is a good supplement to the RSM and classical ANN methodology, which can yield lower standard error of prediction under conditions that the scope of experiment is rigidly restricted.     

TixZryNiz合金放电容量模式识别与人工神经网络的研究

利用模式识别方法分析了TixZryNiz贮氢合金组分对其放电性能的影响，结果表明：其组成中Ti和Zr对放电容量的作用互相牵制，即适当增加Ti的含量或减少Zr的含量将有助于放电容量的提高；并通过人工神经网络方法对TixZryNiz合金放电容量进行预报，结果发现预报值和实验结果有很好的一致性．     

基于AHP和ANN的网络课程质量评价方法研究

网络课程的质量评价关系到网络教学水平和学习质量的提高。传统的网络课程质量评价方法过程复杂,往往带有很大的主观随意性。为了更科学、客观地评价网络课程,可应用层次分析法(AHP)确定网络课程质量评价指标体系,构建人工神经网络(ANN)的网络课程评价模型。实验结果表明,该评价方法具有速度快、准确率高的优点,并得到了满意的评价结果。     

Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis

Structural health monitoring （SHM） is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real structures, two or more sites or types of damage can be present at the same time. It has been shown that one kind of damaged condition can interfere with the detection of another kind of damage, leading to an incorrect assessment about the structure condition. Identifying combined damage on struc- tures still represents a challenge for condition monitoring, because the reliable identification of a combined damaged condition is a difficult task. Thus, this work presents a fusion of methodologies, where a single wavelet-packet and the empirical mode decom- position （EMD） method are combined with artificial neural networks （ANNs） for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure. Results showed that the proposed methodology is very efficient and reliable for identifying and locating the three kinds of damage, as well as their combinations. Therefore, this methodology could be applied to detection-location of damage in real truss-type structures, which would help to improve the characteristics and life span of real structures.