遗传神经网络-紫外分光光度法同时测定苯酚和邻苯二酚

研究吸收光谱重叠严重的苯酚和邻苯二酚的两组分体系,针对BP神经网络易陷入局部极小等缺陷,将遗传算法与BP神经网络相结合,用遗传算法优化神经网络的初始权值和阀值,由神经网络输出的误差构造适应度函数,建立遗传神经网络算法,用紫外分光光度法同时测定混合的苯酚和邻苯二酚,预测集样品的相对平均误差分别为0.818%和0.366%,对水样的加标回收率分别为104.7%和102.9%。     

苯酚和间苯二酚混合体系的因子分析——紫外分光光度法直接测定

本文研究了因子分析—分光光度法及其在多组份混合体系测定中的应用。成功地确定了苯酚和间苯二酚混合体系中的吸光物种数、物种种类及各物种的含量。并同卡尔曼滤波测定的结果进行了比较。     

反向传播网络—吸光光度法同时测定生物实样中的锌和铜

研究了新显色剂 1 ,1 ,5-二 ( 2 -羟基 5 溴苯 ) 3 氰基甲日 (HBPCF)和锌及铜及铜的显色反应 ,并以反向传播人工神经网络———吸光光度法 ,对花生仁和绿豆等实际生物试样中锌及铜的含量 ,进行了同时测定。     

人工神经网络在机械手动力学辨识和位置控制中的应用

本文提出了用人工神经网络近似机械手的逆动力学模型,实现基于模型的非线性控制方案,并以实际的两臂液压机械手为对象给出了仿真结果,整个方案的实现不需要任何关于系统模型的知识.仿真结果表明所研究的位置控制系统具有良好的跟踪性能,并且展示了人工神经网络解决非线性系统辨识和控制问题的潜力.     

因子分析──光度法同时测定合成食品色素

本文研究了使用目标因子分析──光度法对合成食品色素苋菜红，胭脂红，赤藓红的混合体系和亮兰，柠檬黄，胭脂红的混合体系的同时测定。在不需引进其它额外因子的情况下该法成功地确定了混合体系中的吸光物种数，种类及其含量（浓度范围４．３２－４ｐｐｍ，回收率为１００＋５．８１％），对连续波长范围及波长数目对算结果的影响也做了探讨。     

因子分析─光度法同时测定合成食品色素

本文研究了使用目标因子分析─光度法对合成食品色素苋菜红、胭脂红、赤藓红的混合体系和亮兰、柠檬黄、胭脂红的混合体系的同时测定。在不需引进其它额外因子的情况下该法成功地确定了混合体系中的吸光物种数、种类及其含量（浓度范围４．３２─４ｐｐｍ，回收率为１００＋５．８１％），对连续波长范围及波长数目对计算结果的影响也做了探讨。     

BP人工神经网络分光光度法同时测定电池厂废水中镉和镍

研究BP神经网络和分光光度法结合的镉、镍含量同时测定方法.在Na2B4O2缓冲溶液中(pH=10.2),邻羧基苯基重氮氨基偶氨苯(o-CADD)与镉、镍发生灵敏的显色反应,形成红色配合物,最大吸收峰分别位于525 nm和540 nm.利用镉和镍配合物吸收光谱上的差异,对16个不同镉、镍离子浓度的混合液组成的校正集进行训练.通过选择测定波长间隔,网络隐含层神经元数,训练函数等,优化了网络.并对3组预测集验证,相对误差的绝对值小于5%.该方法用于电池厂废水中痕量镉镍的同时测定,加标回收率在104%v95～95.5%之间,表明方法具有较高的准确性.     

紫外分光光度法同时测定复方磺胺甲噁唑片组分含量

用人工神经网络解析复方磺胺甲噁唑片的紫外吸收光谱数据,达到同时测定各组分含量的目的。按L25（5^6）正交表设计,制备了25组标准溶液的混合液,将其吸光度数据和浓度数据作为人工神经网络的训练集。混合液中各组分的5个浓度水平分别为80％、90％、100％、110％、120％。预报集采用自制的模拟样品和市售的复方磺胺甲噁唑片的吸光度数据。网络的输入为各溶液在246～290nm间的吸光度,网络的输出为各组分的浓度。利用Bayesian规则化调整的BP人工神经网络处理数据。结果表明,人工神经网络紫外分光光度法预测模拟样品中的磺胺甲噁唑（SMZ）、甲氧苄啶（TMP）的含量,平均回收率分别为100．53％和100．91％,相对标准偏差分别为1．17％和2．79％。对市售的复方磺胺甲噁唑片中的SMZ、TMP的含量也能取得较好的预测结果。结论：人工神经网络紫外分光光度法可以快速、准确地测定复方磺胺甲噁唑片中组分含量。     

多元校正-光度法同时测定啤酒中醛类化合物

根据2,4-二硝基苯肼与醛类化合物发生显色反应的特征,选择最佳反应条件,采用偏最小二乘法结合光度法对吸收光谱重叠的甲醛、乙醛、苯甲醛的模拟样品进行同时分析,对测定条件进行优化,并用此法对啤酒样品的醛类进行同时测定。对模拟样品,回收率分别为100.4%、96.7%和93.6%。本法测量体系稳定,仪器简单,成本低,结果可靠。     

Simultaneous optimization of artificial neural networks for financial forecasting

Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.     

Simultaneous process mean and variance monitoring using artificial neural networks

Control chart patterns (CCPs) can be employed to determine the behavior of a process. Hence, CCP recognition is an important issue for an effective process-monitoring system. Artificial neural networks (ANNs) have been applied to CCP recognition tasks and promising results have been obtained. It is well known that mean and variance control charts are usually implemented together and that these two charts are not independent of each other, especially for the individual measurements and moving range (X–R_m) charts. CCPs on the mean and variance charts can be associated independently with different assignable causes when corresponding process knowledge is available. However, ANN-based CCP recognition models for process mean and variance have mostly been developed separately in the literature with the other parameter assumed to be under control. Little attention has been given to the use of ANNs for monitoring the process mean and variance simultaneously. This study presents a real-time ANN-based model for the simultaneous recognition of both mean and variance CCPs. Three most common CCP types, namely shift, trend, and cycle, for both mean and variance are addressed in this work. Both direct data and selected statistical features extracted from the process are employed as the inputs of ANNs. The numerical results obtained using extensive simulation indicate that the proposed model can effectively recognize not only single mean or variance CCPs but also mixed CCPs in which mean and variance CCPs exist concurrently. Empirical comparisons show that the proposed model performs better than existing approaches in detecting mean and variance shifts, while also providing the capability of CCP recognition that is very useful for bringing the process back to the in-control condition. A demonstrative example is provided.     

Compartmental modelling with artificial neural networks

In this paper, we introduce an abbreviated compartmental modelling scheme which may be of interest to those in neuron- based adaptive systems because of the additional scope it provides for studying biologically-inspired learning mechanisms. The scheme, although not as flexible and precise as the general compartmental approach, allows one to design Hodgkin-Huxley style cells, and passive dendritic trees with an arbitrary number of synaptic connections. The trade-offs made for computational performance, may make the modelling scheme more appropriate for practical applications. The modelling scheme is based upon artificial neural networks, which we have used to represent cylindrical compartments (both passive and active) of different lengths, two types of voltage-dependent channels, and basic chemical synapses with variable time constants.     

Speech recognition with artificial neural networks

In this paper, artificial neural networks were used to accomplish isolated speech recognition. The topic was investigated in two steps, consisting of the pre-processing part with Digital Signal Processing (DSP) techniques and the post-processing part with Artificial Neural Networks (ANN). These two parts were briefly explained and speech recognizers using different ANN architectures were implemented on Matlab. Three different neural network models; multi layer back propagation, Elman and probabilistic neural networks were designed. Performance comparisons with similar studies found in the related literature indicated that our proposed ANN structures yield satisfactory results.     

Short-term ozone forecasting by artificial neural networks

In this work we report preliminary results of a study aiming to develop an intelligent tool for performing ozone forecasting in the polluted atmosphere of México City. This tool is based in the paradigm of neural networks. Two neural models are used in this work, namely, the Bidirectional Associative Memory (BAM) and the Holographic Associative Memory (HAM). We analyse and preprocess daily patterns of meteorological variables and concentrations of pollutants as measured by five monitoring stations in México City. These patterns are used to train both neural networks and then we use them to predict ozone at one point in the city. Preliminary results are reported and some conclusions are drawn.     

Calibrating artificial neural networks by global optimization

Artificial neural networks (ANNs) are used extensively to model unknown or unspecified functional relationships between the input and output of a “black box” system. In order to apply the generic ANN concept to actual system model fitting problems, a key requirement is the training of the chosen (postulated) ANN structure. Such training serves to select the ANN parameters in order to minimize the discrepancy between modeled system output and the training set of observations. We consider the parameterization of ANNs as a potentially multi-modal optimization problem, and then introduce a corresponding global optimization (GO) framework. The practical viability of the GO based ANN training approach is illustrated by finding close numerical approximations of one-dimensional, yet visibly challenging functions. For this purpose, we have implemented a flexible ANN framework and an easily expandable set of test functions in the technical computing system Mathematica. The MathOptimizer Professional global-local optimization software has been used to solve the induced (multi-dimensional) ANN calibration problems.     

Clustering with artificial neural networks and traditional techniques

In this article, two clustering techniques based on neural networks are introduced. The two neural network models are the Harmony theory network (HTN) and the self‐organizing logic neural network (SOLNN), both of which are characterized by parallel processing, a distributed architecture, and a large number of nodes. After describing their clustering characteristics and potential, a comparison to classical statistical techniques is performed. This comparison allows the creation of a correspondence between each neural network clustering technique and particular metrics as used by the corresponding statistical methods, which reflect the affinity of the clustered patterns. In particular, the HTN is found to perform the clustering task with an accuracy similar to the best statistical methods, while it is further capable of proposing an optimal number of groups into which the patterns may be clustered. On the other hand, the SOLNN combines a high clustering accuracy with the ability to cluster higher‐dimensional patterns without a considerable increase in the processing time. © 2003 Wiley Periodicals, Inc.