基于人工神经网络的SPM自动标定

针对扫描探针显微镜中扫描图像显示的大小与实际大小存在较大误差,提出了基于图像处理和人工神经网络标定的方法。经过图像处理提取标准光栅图像的特征矩阵,用人工神经网络训练特征矩阵和真实矩阵,用于预测扫描图像实际施加在x,y轴的电压值。应用于某原子力显微镜的标定软件中,使扫描图像的x_y表面失真度从7.2%降至2.44%。     

PLS-BP法近外光谱同时检测饲料组分的研究

建立了用偏最小二乘(partial least squares,PLS)与人工神经网络(artificial neural networks,ANN)联用对饲料样品同时测定水分、灰分、蛋白质、磷含量的预测校正模型.光谱数据用二阶微分及标准归一化处理(SNV),用PLS法将原始数据压缩提取前10个主成分与2个特征峰值作为12个输入向量,采用单隐层的反向传播人工神经网络(Back-Propagation Network,BP),确定中间层的神经元个数为23,初始训练迭代次数为1 000.PLS-BP模型对样品四个组分含量的预测决定系数(r2)分别为:0.995 0,0.998 0,0.999 0和0.967 0;样品平行扫描光谱预测值的标准偏差分别为:0.027 74,0.048 53,0.032 92和0.022 04.     

模式识别技术用于果酸混合物分析

研究了偏最小二乘法和人工神经网络法用于水果的紫外可见多组分光度分析。当混合物中诸组分存在相互作用，光谱加和性受到 扰动时，ＰＬＳ法和ＡＮＮ法用于混和物的分析仍能得到较满意的结果。     

干涉型光纤陀螺仪零漂建模方法

光纤陀螺仪零漂是衡量光纤陀螺仪精度的重要指标。文中对某光纤陀螺仪的零偏数据进行分析和预处理,采用时间序列分析法建立了AR（2）模型,同时基于BP神经网络建立了预测模型。建模结果分析表明：AR建模方法简单易行,但适用性不如BP网络模型,BP网络模型建模精度高,但算法复杂且收敛速度慢,容易陷入局部极小,因此采用了改进训练方法,改善了BP网络性能。     

PDK1的吲哚酮类抑制剂定量构效关系研究

3'磷酸肌醇依赖的激酶1(3'-phosphoinositide-dependent kinase1,PDK1)是--个63kDa的丝氨酸/苏氨酸(Ser/Thr)蛋白酶,它是Akt1的上游活化激酶~([1,2]).PDK1可在磷酸化的Akt1的Thr308位点使Akt1的活性增加30倍以上.但这种活化以依赖PIP3或PIP2的方式完成,PDK1也就因此而得名~([3]).     

Artificial intelligence based modelling and multi-objective optimization of vinyl chloride monomer (VCM) plant to strike a balance between profit,energy utilization and environmental degradation

The current research focuses on the creation of a general technique that solves the key issue of any operational chemical plant, namely, how to strike a delicate balance between profit and environmental impact. As a case study, a commercial vinyl chloride monomer (VCM) production unit was used. This research produced a new modelling and optimization tool that commercial chemical plants can use to measure their environmental impact and strike a careful balance between profit and environmental damage. This paper demonstrates how to model commercial complex reactors using Aspen and ANN in an easy-to-use manner. The current study used a multi-objective hybrid ANN and genetic algorithm to find a delicate balance between profit and environmental damage. A case study of a commercial VCM manufacturing process demonstrates the efficacy of the proposed methodology. The suggested methodology creates optimal VCM reactor operating parameters, which can be used in commercial plants to increase profit. Furthermore, the suggested methodology creates a set of Pareto optimal solutions platform to acquire insight into the profit-environmental impact balance. These insights could be extremely beneficial to plant management in making educated decisions about plant operations.     

A 21st century technique for food control: Electronic noses

This work examines the main features of modern electronic noses (e-noses) and their most important applications in food control in this new century. The three components of an electronic nose (sample handling system, detection system, and data processing system) are described. Special attention is devoted to the promising mass spectrometry based e-noses, due to their advantages over the more classical gas sensors. Applications described include process monitoring, shelf-life investigation, freshness evaluation, authenticity assessment, as well as other general aspects of the utilization of electronic noses in food control. Finally, some interesting remarks concerning the strengths and weaknesses of electronic noses in food control are also mentioned.     

基于BP算法的企业自我实现能力综合评价

针对企业需要的多层次及动态复杂性特征．结合企业需要层次理论。在建立企业自我实现能力指标体系的基础上。构造了基于改进的BP算法的企业自我实现能力综合测评模型。该模型具有较好的收敛速度和自适应能力。具有方便、准确、可靠、快速等特点。实证分析和仿真结果的比较表明该算法是有效的。     

人工神经网络分类鉴别苦丁茶红外光谱

为了分类鉴别苦丁茶,采用竞争神经网络(CNN)和反向传播人工神经网络(BP网络)两种模式的人工神经网络(ANN)分别分析了各种苦丁茶的红外谱图。作者采用25个样本作训练集,11个样本作检验集,用两种网络进行了训练。结果表明,CNN网络和BP网络均能够有效地实现苦丁茶产地的鉴别,但CNN网络能够进一步地区分苦丁茶的级别。实验表明,CNN速度快,预测结果准确,可望用竞争神经网络(CNN)和红外光谱法结合分类鉴别苦丁茶。     

Developing an early warning system to predict currency crises

The purpose of this paper is to develop an early warning system to predict currency crises. In this study, a data set covering the period of January 1992–December 2011 of Turkish economy is used, and an early warning system is developed with artificial neural networks (ANN), decision trees, and logistic regression models. Financial Pressure Index (FPI) is an aggregated value, composed of the percentage changes in dollar exchange rate, gross foreign exchange reserves of the Central Bank, and overnight interest rate. In this study, FPI is the dependent variable, and thirty-two macroeconomic indicators are the independent variables. Three models, which are tested in Turkish crisis cases, have given clear signals that predicted the 1994 and 2001 crises 12 months earlier. Considering all three prediction model results, Turkey’s economy is not expected to have a currency crisis (ceteris paribus) until the end of 2012. This study presents uniqueness in that decision support model developed in this study uses basic macroeconomic indicators to predict crises up to a year before they actually happened with an accuracy rate of approximately 95%. It also ranks the leading factors of currency crisis with regard to their importance in predicting the crisis.