Auxiliary error and probability density function based neuro-fuzzy model and its application in batch processes

This paper focuses on resolving the identification problem of a neuro-fuzzy model(NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function(PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF of modeling error. More specifically, a virtual adaptive control system is constructed with the aid of the auxiliary error model and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.

Stability of Stochastic Logistic Model with Ornstein-Uhlenbeck Process for Cell Growth of Microorganism in Fermentation Process

Current research is concerned with the stability of stochastic logistic equation with Ornstein-Uhlenbeck process. First, this research proves that the stochastic logistic model with Ornstein-Uhlenbeck process has a positive solution. After that, it also introduces the sufficient conditions for stochastically stability of stochastic logistic model for cell growth of microorganism in fermentation process for positive equilibrium point by using Lyapunov function. In addition, this research establishes the sufficient conditions for zero solution as mentioned in Appendix A due to the cell growth of microorganism μmax, which cannot be negative in fermentation process. Furthermore, for numerical simulation, current research uses the 4-stage stochastic Runge-Kutta (SRK4) method to show the reality of the results.

Dynamic modeling and analysis of the closed-circuit grinding-classification process

Mathematical models of the grinding process are the basis of analysis, simulation and control. Most existent models in- cluding theoretical models and identification models are, however, inconvenient for direct analysis. In addition, many models pay much attention to the local details in the closed-circuit grinding process while overlooking the systematic behavior of the process as a whole. From the systematic perspective, the dynamic behavior of the whole closed-circuit grinding-classification process is consid- ered and a first-order transfer function model describing the dynamic relation between the raw material and the product is established. The model proves that the time constant of the closed-circuit process is lager than that of the open-circuit process and reveals how physical parameters affect the process dynamic behavior. These are very helpful to understand, design and control the closed-circuit grinding-classification process.

Application of Random Process in Soil Profile Modeling

When using the random process in soil profile modeling, the stationary and ergodicity of the soil properties in the profile must be tested. This paper describes a procedure for stationary and ergodicity testing. Numerical examples were given for demonstration. A log-cosine function is suggested to simulate the correlation function, which has been proved to be good for soil profile modeling.

Lognormal Process Software Reliability Modeling with Testing-Effort

We propose a software reliability growth model with testing-effort based on a continuous-state space stochastic process, such as a lognormal process, and conduct its goodness-of-fit evaluation. We also discuss a parameter estimation method of our model. Then, we derive several software reliability assessment measures by the probability distribution of its solution process, and compare our model with existing continuous-state space software reliability growth models in terms of the mean square error and the Akaike’s information criterion by using actual fault count data.

The Global Stability Analysis of a Mathematical Cellular Model of Hepatitis C Virus Infection with Non-Cytolytic Process

The aim of this work is to analyse the global dynamics of an extended mathematical model of Hepatitis C virus (HCV) infection in vivo with cellular proliferation, spontaneous cure and hepatocyte homeostasis. We firstly prove the existence of local and global solutions of the model and establish some properties of this solution as positivity and asymptotic behaviour. Secondly we show, by the construction of appropriate Lyapunov functions, that the uninfected equilibrium and the unique infected equilibrium of the mathematical model of HCV are globally asymptotically stable respectively when the threshold number and when .

Identification of Neuro-Fuzzy Hammerstein Model Based on Probability Density Function

A new identification method of neuro-uzzy Hammerstein model based on probability density function(PDF) is presented,which is different from the idea that mean squared error(MSE) is employed as the index function in traditional identification methods.Firstly,a neuro-fuzzy based Hammerstein model is constructed to describe the nonlinearity of Hammerstein process without any prior process knowledge.Secondly,a kind of special test signal is used to separate the link parts of the Hammerstein model.More specifically,the conception of PDF is introduced to solve the identification problem of the neuro-fuzzy Hammerstein model.The antecedent parameters are estimated by a clustering algorithm,while the consequent parameters of the model are identified by designing a virtual PDF control system in which the PDF of the modeling error is estimated and controlled to converge to the target.The proposed method not only guarantees the accuracy of the model but also dominates the spatial distribution of PDF of the model error to improve the generalization ability of the model.Simulated results show the effectiveness of the proposed method.

义符认知功能的心理语言学探索——三十年研究工作之回顾

在汉字词认知研究中,义符是最具有特色的领域。义符在汉字词认知中具有重要的功能。三十年来,诸多研究者考察了义符在汉字词识别、语义提取、语法分类、知觉加工和语音提取等方面的作用,揭示了义符作为汉字部件系统的类聚功能,探索了义符的主观熟悉性、位置、频率、家族大小、类别一致性和句法倾向性的影响,并采用脑科学技术探讨了义符激活在形声字认知中的时间进程,相继提出了义符参与汉字词认知加工的7个模型。未来应该对义符与汉字的构造规律、义符的表义类型和表义度、义符与英文词根的对比、义符认知功能与汉民族思维方式的关系做进一步的研究。

基于正余弦函数设计位移运动算法的研究与实现

为实现机床加工运动过程中算法规划能输出连续的运动指令,提高位置控制精度,文章基于正余弦函数值周期连续变化的特性,设计出位移运动指令随时间连续变化的算法模型并进行公式推导。结合Matlab对算法的规划细节进行仿真分析,得到算法规划的控制指令平稳且连续,证明了算法模型建立与公式推导过程的正确性。选择机床加工PCB覆铜板运动过程为试验研究对象,使用激光干涉仪对机床加工实际位移距离进行测量,测得采用所设计的位移运动算法进行运动过程规划,位置控制精度能达到1.788μm,验证了位移运动算法的正确性。

Investigation of Probability Generating Function in an Interdependent <i>M/M/</i>1:(∞;GD) Queueing Model with Controllable Arrival Rates Using Rouche’s Theorem

Present paper deals a M/M/1:(∞;GD) queueing model with interdependent controllable arrival and service rates where- in customers arrive in the system according to poisson distribution with two different arrivals rates-slower and faster as per controllable arrival policy. Keeping in view the general trend of interdependent arrival and service processes, it is presumed that random variables of arrival and service processes follow a bivariate poisson distribution and the server provides his services under general discipline of service rule in an infinitely large waiting space. In this paper, our central attention is to explore the probability generating functions using Rouche’s theorem in both cases of slower and faster arrival rates of the queueing model taken into consideration;which may be helpful for mathematicians and researchers for establishing significant performance measures of the model. Moreover, for the purpose of high-lighting the application aspect of our investigated result, very recently Maurya [1] has derived successfully the expected busy periods of the server in both cases of slower and faster arrival rates, which have also been presented by the end of this paper.

更新过程若干矩性质及其风险理论应用

更新过程作为一类重要的计数随机过程,在金融风险理论、可靠性理论等研究领域均具有广泛应用。本文从更新过程的均值函数更新函数有限证明出发,利用其证明思想,进一步研究了更新过程若干矩函数性质,包括任意阶矩函数、指数阶矩函数性质等。最后本文列举了更新过程矩函数性质在风险理论中的一个简单应用。

建筑内热源滞后冷负荷监测模型研究

准确获取内热源实时冷负荷对于制定供暖空调系统节能调控策略及降低建筑运行能耗具有重要意义,而辐射热传递过程产生的滞后冷负荷是内热源冷负荷计算的难点,为此,基于围护结构蓄放热规律、热传递特性及传递函数原理等建立内热源滞后冷负荷监测模型,该模型为10阶传递函数。为了简化计算过程、提高计算精度,利用辐射时间系数对该模型进行参数辨识及降阶分析,最终得到2阶传递函数。建立试验系统对模型进行验证,结果显示,模型的平均绝对百分比误差为8.19%。内热源滞后冷负荷监测模型可依托能耗监测平台实现负荷的在线监测,对描述滞后冷负荷的时间延迟规律及幅度衰减特性具有理论意义,为制定供暖空调系统节能调控策略提供理论依据。

基于协同优化的耐压柱壳多学科多目标优化

为提高耐压柱壳的设计效率和结构性能,本文基于多学科设计优化思想,进行了耐压柱壳多学科设计优化的学科分解,确定了耐压柱壳多学科优化设计的设计参数,明确了设计变量间的耦合关系,设计了耐压柱壳参数化分析流程,进行了设计变量灵敏度分析。在利用响应面模型建立了具有较高拟合精度耐压柱壳近似模型的基础上,以质量排水量比和极限载荷为目标函数,建立了基于协同优化方法的耐压柱壳多学科多目标优化模型,在确定了系统层和学科层的优化求解方法基础上,设计了基于协同优化的耐压柱壳多学科多目标优化框架,进行了优化的求解工作,得到了优化方案。与初始方案的对比表明:所得到的Pareto解集中,部分方案在2个目标函数上均得到了优化,验证了优化框架的有效性。

无人自主系统能力边界参数自适应判别方法

为有效应对仿真测试面临的维度灾难问题,降低传统全参数空间遍历中所需的仿真次数,需要获取针对性的仿真数据以准确反映实验数据建模特征,以较少的仿真次数获得信息量丰富且代表原始数据特征的样本。提出一种面向无人自主系统能力边界参数自适应判别的数字化仿真测试模型,采用多权重结构的佳点集进行初始构建,结合自适应核函数边界点判别算法,通过高斯过程回归对模型进行迭代优化,自适应地判别无人自主系统的能力边界。实验结果表明:该方法能够降低建模所需数据量,提高自适应参数边界判别的效率,为提升智能无人系统试验的效率提供了高效途径。

机器与社会理想:莫西·金兹堡的构成主义功能性设计方法逻辑探析

20世纪20年代,金兹堡面对新旧风格之交替、艺术与技术的争辩提出了一种集社会理想与机器性为一体的建筑设计方法—“功能性设计方法”,其成为各先锋思潮在建筑本体域的一次聚合与重要接驳点。然而鉴于政治因素之介入,功能方法一度在建筑历史理论研究中处于失语状态。文章将从功能方法之历史时代特征出发,探究其思想之源并着重探析其以社会理想为目标、内化于机械生产逻辑的机器范式,揭示其对构成主义建筑理论与现代主义理性设计方法的重要奠基作用。

基于综合满意度函数的D2钢激光抛光工艺参数优化方法

目的同步提升D2模具钢激光抛光效果及抛光质量的稳定性。方法提出了一种基于综合满意度函数的D2钢激光抛光工艺参数优化方法。首先,以表面粗糙度、显微硬度和抛光深度为优化目标,基于Box-Behnken方法,以激光功率、扫描速度和搭接率为试验因素进行3因素3水平试验。其次,基于试验数据构建各优化目标均值与标准差的双响应面模型,采用熵权理论获得了各优化目标均值与标准差的客观熵权,并与主观熵权结合构建综合熵权。最后,将双响应面模型与综合熵权引入满意度函数构建了改进综合满意度模型,通过随机梯度下降法获得最佳工艺参数组合。结果在激光功率为551W、扫描速度为9mm/s、搭接率为0.55的条件下,表面粗糙度均值由Ra=5.188μm下降至Ra=1.056μm,降幅为79.65%,粗糙度标准差为0.0128μm;显微硬度由541.3HV0.5下降至509.3HV0.5,降幅为5.91%,显微硬度标准差为12.9811HV0.5;抛光深度为0.331mm,抛光深度标准差为0.0024mm。结论该方法可以有效提升D2模具钢激光抛光效果,同时避免抛光后表面质量波动,可为其他模具钢激光抛光工艺参数寻优提供方法借鉴。

一类具有Ornstein-Uhlenbeck过程的水母模型

为探究随机扰动对水母种群动力学行为的影响,考虑环境噪声扰动建立了一类具有Ornstein-Uhlenbeck过程的水母模型。首先分析了模型全局解的存在唯一性,并采用Lyapunov函数方法给出了水母灭绝性的充分条件。其次利用Khasminskii理论得到了模型平稳分布存在性的充分条件。最后通过具体的数值例子验证了理论结果的可行性。结果表明,回复速率的增大或环境波动强度的减小都会加快水母种群的灭绝速率,这意味着可以通过适当调节回复速率的大小或环境波动的强度来控制水母种群的增长。该研究可为制定水母暴发防治策略提供一定的理论参考。

CNN-ERI地质识别模型的研究与应用

基建工程对地质勘探精度的要求不断提高,研究人工智能技术在地质勘探数据分析处理中的应用具有重要意义。基于卷积神经网络技术的数据降维处理功能,提出一种改进的CNN-ERI地质识别模型,分析了该模型在地质识别方面的优越性,探讨了数据量和环境因素对模型识别精度的影响,并结合某隧洞工程,探析了模型的具体应用问题。

生物氧化预处理过程pH值随机分布控制方法研究

生物氧化预处理过程中氧化槽pH值是影响细菌活性的关键因素之一,而pH值输出形态分布不符合高斯分布,使传统的均值和方差难以描述输出pH值分布,本文提出一种对矿浆输出pH的概率密度函数(PDF)统计信息控制方法。首先,采用B样条逼近矿浆输出pH值的PDF统计信息;其次,针对权值向量之间的关系,利用动态神经网络(DNN)建立控制输入和权值向量之间的非线性动态模型,基于建立pH的PDF统计信息权值模型,设计滑模变结构控制器,通过构造Lyapunov函数进行稳定性分析;最后,实现输出PDF统计信息对目标PDF统计信息的跟踪。仿真结果验证了所提方法的有效性,为生物氧化预处理过程提供了新方法。

建筑工程全过程造价管理成熟度评价模型研究

文章以某公寓楼项目为例,探究了全过程造价管理成熟度评价模型的构建策略和应用方法。从工程项目的投资决策、设计、招标、施工与验收5个阶段选取了16个评价指标,并利用熵权法确定各个指标的权重。使用灰色白化权函数建立等级评分标准、划分评价灰类,求出5个灰类的综合聚类系数,根据计算结果判断指标成熟度等级。结果表明:该工程项目的综合聚类系数为0.4703,属于规范级,表明全过程造价管理较为规范,但个别环节仍需改进。