基于人工神经网络的无机抗菌剂抗菌性能预测模型

Quantitatively evaluation of antibacterial activities of inorganic antibacterial agents is an urgent problem to be solved.Using experimental data by an orthogonal design, a prediction model of the relation between conditions of preparing inorganic antibacterial agents and their antibacterial activities has been developed.This is accomplished by introducing BP artificial neural networks in the study of inorganic antibacterial agents.It provides a theoretical support for the development and research on inorganic antibacterial agents.     

Prediction of malignancy selective neural networks degree in brain glioma using ensemble

A clustering algorithm based selective neural networks ensemble （CLUSEN） is proposed to predict the degree of malignancy in brain glioma. Since the degree prediction of malignancy is critical before brain surgery, many learning methods are used like rule induction algorithm, single neural networks, support vector machines, etc. Ensemble learning methods can improve the generalization of single learning machine, and are becoming popular in the machine learning and medical data processing communities. The procedure of CLUSEN can efficiently remove redundancy learning individuals and help improve the diversity of ensemble methods. CLUSEN is used to predict the degree of malignancy in brain glioma. Experimental results on a set of brain glioma data show that, compared to support vector machines, rule induction and single neural networks, the classification accuracy of CLUSEN is higher.     

一类二元离散神经网络周期解的存在性

研究具有两段常数不连续信号传递函数的二元离散神经网络模型周期解的存在性和吸引性，可获得存在性与吸引性的一个充分条件。     

神经网络的滚动轴承故障诊断

文章通过旋转机械故障实验平台,采集旋转机械故障实验台轴承的3种工作状态分别是轴承正常、轴承内圈裂缝、轴承外圈裂缝的振动加速度信号．对信号进行零均值化处理后,选择频率成分幅值较大的频率进行信号重组,提取其时域量纲特征值,利用神经网络进行故障类型的识别;通过实验,取得了很好的诊断结果．     

无机抗菌剂应用现状及发展趋势研究

介绍了抗菌材料、抗菌剂的基本分类,讨论了无机抗菌剂的分类、组成、性能、与抗菌机理,概括了无机抗菌剂应用领域,预测了无机抗菌剂的发展趋势。     

Nonlinear model predictive control with guaranteed stability based on pseudolinear neural networks

~~identificationis7-7-1,where3nm==,andtheRPEalgorithmisusedtoupdatetheweightingofPNN.Thewholetrainingprocessuses800iterations.InordertoovercometheinaccuracyofPNNmodel,thecontrollerstructureisacompositeoneasfbff()()()ututut=+,(37)wherefb()utistheoutputoffeedbackcontroller,ff()utistheoutputofpredictivecontrollerdescribedbyEq.(14),with0.20=,0.40=,andmax5K=.Insimulatedclosedloopcontrol,ufb(t)isaproportionalcontroller,fb()()Putket=and5.0Pk=.Theset-pointofthesystemisd0.15,if040,and120()0.24,if4…     

基于RBF神经网络的不确定机械臂自适应反演控制研究

针对机械臂位置跟踪问题,基于RBF神经网络控制理论,提出了一种自适应反演控制方法．该方法利用反演控制技术解决了系统的非线性问题,通过神经网络对系统中不确定函数进行逼近,实现神经网络自适应反演控制,最后通过Lyapunov稳定性理论证明所得闭环系统的最终一致有界．仿真结果证明该算法的有效性．     

基于聚类算法的选择性神经网络集成在大脑胶质瘤诊断中的应用

1 Introduction The degree of malignancy in brain glioma[1]domi-nates the way of treatment .In case of gradeⅠorⅡaccording to Kernohan,the success rate of operationis satisfactory. Otherwise ,for gradeⅢor IV,the sur-gical risk is high ,and poor life qual…     

基于蚁群算法的仿生模式识别神经网络构造方法

仿生模式识别神经网络（BPRNN）同传统BP、RBF神经网络相比具有更好的模式识别能力;训练样本库变更后网络的重新训练时间更小,但该网络构造过程中样本覆盖几何体参数的选择对网络识别率和复杂度有很大影响.本文通过引入蚁群算法来构造并优化网络参数,实验证明该算法法能较好的平衡网络性能和复杂度.     

BAM神经网络概周期解的存在性

本文利用巴拿赫不动点定理,得到了BAM神经网络存在唯一概周期解的充分条件。该结果改进了文【9】的结果,并有助于设计具有概周期振动解的BAM神经网络。     

基于神经网络的转换GPS高程的新方法

GPS高程是相对于WGS-84椭球体的大地高,因此,在工程应用中,GPS高程需要转换为正常高.转换GPS高程通常采用二次曲面拟合法(CFM)和神经网络方法(NNM),但这2种方法各有优缺点.在研究了这2种方法之后,提出了一种转换GPS高程的新方法,该方法综合了上述2种方法的优点,故取名为“CF&NNM”方法.介绍了CF&NNM方法的思路和计算过程.通过一个工程实例,列出了上述3种方法的数据处理结果,新方法效果最好.对CF&NNM方法进行了理论分析.     

An improved genetic algorithm for training layered feedforward neural networks

The new genetic algorithm for training layered feedforward neural networks proposed here uses a mutation operator for performing the search behaviors of local optimization. Combining the random restart method with the local search technique, the algorithm can converge asymptocally, to the optimal solution. Test with a practical example showed that the improved genetic algorthm is more efficient than the conventional genetic algorithm. Project supported by NSFC (No 39870940) and (G199054405-973) the National Key Scientific Research & Development Program.     

Nonlinear model predictive control with guaraneed stability based on pesudolinear neural networks

A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor. It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.     

基于MATLAB和BP网络的公路软基沉降量预测模型

人工神经网络具有强大的非线性映射能力,文章利用BP神经网络建立了公路软土地基沉降量预测模型,并用MATLAB人工神经网络工具箱进行了实现。根据实测资料,对此预测模型进行训练和预测,试验表明,预测模型具有较好的预测精度,操作简单,具有广阔的工程应用前景。     

基于人工神经网络BP算法的黄冈市房价预测

利用2003年至2010年黄冈市区人均地区生产总值、房地产开发投资、城镇居民人均住房使用面积等相关数据,根据人工神经网络BP算法,预测出2011年和2012年黄冈市区房价,与已经公布的这两年房价数据比较,符合预期误差范围。在此基础上,预测出2013年的房价,可以看出黄冈市区房价总体保持增长趋势,结合预测结果,给出合理化的解释。     

在视频对象平面生成中使用细胞神经网络进行初始对象的分割

1　Introduction　MPEG 4videocodingstandard providesobject basedfunctionalitiesbyintroducingtheconceptofvideoobject plane (VOP) .Withtheextractionofvideoobjectsandallocatingdifferentnumberofbitsordifferentframe ratesfordifferentobjects ,thestan dardcansupportobject basedscalabilitythatisusefulinmanypracticalapplications[1] .However,MPEG 4alwaysassumesthatthevideocontentstobecodedarewellrepresentedinvideoobjectswithoutmandatinganyspecifictechniques;sovideoobjectsegmentationbecomesanimportant…     

A comparison of conventional linear regression methods and neural networks for forecasting educational spending

This study presents an application of neural network methods for forecasting per pupil expenditures in public elementary and secondary schools in the United States. Using annual historical data from 1959 through 1990, forecasts were prepared for the period from 1991 through 1995. Forecasting models included the multivariate regression model developed by the National Center for Education Statistics for their annual Projections of Education Statistics Series, and three neural architectures: (1) recurrent backpropagation; (2) Generalized Regression; and (3) Group Method of Data Handling. Forecasts were compared for accuracy against actual values for educational spending for the period. Regarding prediction accuracy, neural network results ranged from comparable to superior with respect to the NCES model. Contrary to expectations, the most successful neural network procedure yielded its results with an even simpler linear form than the NCES model. The findings suggest the potential value of neural algorithms for strengthening econometric models as well as producing accurate forecasts. [JEL C45, C53, I21]     

Influence of measurement errors on structural damage identification using artificial neural networks

The effect of measurement errors on structural damage identification using artificial neural networks (ANN) was investigated in this study. By using back-propagation (BP) networks with proper input vectors, numerical simulation tests for damage detection on a six-storey frame were conducted with measurement errors in deterministic as well as probabilistic senses. The identifiability using ANN for damage location and extent was studied for the cases of measurement errors with different degrees. The results showed that there exists a critical level of measurement error beyond which the probability of correct identification is sharply decreased. The identifiability using the neural networks in the presence of modeling and measurement errors is finally verified using experimental data on a two-storey steel frame. Project supported by Hong Kong Polytechnic University.     

太子参白茶的抗菌活性研究

通过太子参白茶5个不同浓度的溶冲液对酿酒酵母菌、青霉、黑曲霉、金黄色葡萄球菌、藤黄八叠球菌、枯草杆菌、大肠杆菌、铜绿假单胞菌、青色链霉菌等九种菌的抑菌活性进行研究,结果表明5种不同浓度的太子参白茶溶冲液对九种受试菌都有抑菌作用,其中4倍液为真菌与细菌的最好的抑菌浓度．     

一种用于山区土壤重金属评估的集成RBF空间插值算法(英文)

提出了一种在山区能够准确、稳定地预测未采样点土壤重金属浓度的集成径向基函数神经网络空间插值方法(IRBFANNs).该方法集成径向基函数神经网络和神经网络集成技术的优点.为了研究所提IRBFANNs方法的性能,进行了3组不同采样密度条件下的实验.通过M n元素插值的均方根误差和分布估计图对IRBFANNs和其他6个插值方法进行了比较.实验结果表明:IRBFANNs方法在精确性和稳定性方面优于其他参评方法,且在采样密度稀疏条件下该方法能够提供细节较丰富的分布估计图.