pH精算及酸碱滴定曲线绘制通用软件的开发

以Microsoft.NET Framework为开发平台,使用面向对象的C#语言,开发pH计算及滴定曲线绘制的软件。该软件利用分布系数计算各种形态的平衡浓度,根据酸碱质子理论归纳出通用方程后,利用二分法求解氢离子浓度计算pH。软件收集了常见酸碱的离解常数,用户只要输入浓度后就可精算pH值、绘制酸碱滴定曲线,同时提供Excel数据输出、图像保存等功能。如果需要考虑活度,可根据德拜-休克尔公式计算活度系数,使用浓度常数代替活度常数,使所得pH更接近体系真实值。软件计算结果准确、界面友好、容易操作,可广泛应用于各种体系。     

pH中和系统辨识中的人工神经网络应用

pH中和作为化工、生物、发电和污水处理中的一个重要过程,具有极强的非线性和不确定性,很难对其进行精确建模,因此,pH值的控制一直是工业过程控制中的一个难题。本文借鉴了计算机领域中神经网络(NN)在非线性系统建模中的显著作用,结合对pH中和过程机理的分析,建立了基于BP神经网络的辨识模型,对典型的pH中和过程系统辨识进行了仿真研究,并进行了相关试验。试验结果表明:神经网络在pH中和过程辨识中具有较高的辨识精度,有着广阔的应用前景。     

沥青拌和楼控制系统数据报表设计

本文介绍了用组态软件开发沥青拌和楼控制系统时数据报告部分的设计方法,在此系统中,我们根据生产和管理的需要,设计了实时数据报告、中期数据报告、长期数据报告二种形式的数据保存和显示方式。     

测试系统曲线显示算法设计

本文对三种类型曲线显示的核心算法进行了研究.引进了阿克玛算法来进行平滑曲线的绘制,并给出了效果分析.该算法对实际工业测控系统有着重大的意义,并取得了很好的效果.     

Neural network for system identification

A double-hidden layer neural network is proposed to identify a non-linear dynamic system. The structure of this neural network is derived from the Kolmogorov theorem. Each node of the network corresponds to an unknown non-linear function to be estimated. An elementary function is designed to be a constituent of an arbitrary continuous function. The difficulty of function estimation is solved by estimating the weightings of the elementary functions. Two algorithms are applied to estimate the network weightings. The weightings of the upper hidden layer are estimated by the least squares method. On the other hand, the recursive prediction error algorithm is applied to estimate the parameters of the lower hidden layer. The simulation studies show that the proposed neural network can model the dynamics of a general non-linear system.     

A self-evaluating expert system for X-ray rocking curve analysis

X-ray rocking curve analysis is widely used in research and industry to investigate the perfection of a variety of natural and synthetic crystals. In this article a method is demonstrated for the effective self-evaluation of an expert system for x-ray rocking curve analysis. the method uses a combination of fuzzy logic and machine learning, the latter defined as a self-adaptive system that improves system performance over time. the heuristics of several experts are combined using rules, frames, and connection matrices. Each expert is weighted on the basis of experience and these credibility weights are used to influence the decision processes of the expert system. All weights are evaluated over time and the basis for evaluation is successful or unsuccessful expert system decisions. Individual rules are also evaluated and whenever a rule is shown to be ineffective it is hidden from the reasoning processes of the expert system. When new situations occur that have not been allowed for in the rules of the expert system, then existing rules are fine-tuned and changed to deal with these new facts. New rules are inferred and evaluated in the same way as the heuristics of the human experts. © 1994 John Wiley & Sons, Inc.     

Optimal inputs for linear system identification

This paper considers the design of optimal inputs for identifying parameters in linear dynamic systems. The criterion used for optimization is the sensitivity of the system output to the unknown parameters as expressed by the weighted trace of the Fisher information matrix. It is shown that the optimal energy constrained input is an eigenfunction of a positive self-adjoint operator corresponding to its largest eigenvalue. Several different representations of the optimal input and several methods for its numerical computation are considered. The results are extended to systems with process noise, and the relationship to other criteria for input design are brought out. Three analytical examples are solved in closed form which show that the optimal input is a sum of sine and cosine functions at appropriate frequencies. Optimal elevator deflections for identifying the short period parameters of C-8 aircraft are computed numerically and compared with the doublet input currently in use.     

Genetic least squares for system identification

The recursive least-squares algorithm with a forgetting factor has been extensively applied and studied for the on-line parameter estimation of linear dynamic systems. This paper explores the use of genetic algorithms to improve the performance of the recursive least-squares algorithm in the parameter estimation of time-varying systems. Simulation results show that the hybrid recursive algorithm (GARLS), combining recursive least-squares with genetic algorithms, can achieve better results than the standard recursive least-squares algorithm using only a forgetting factor.     

Fractal-fractional neuro-adaptive method for system identification

Engineering with Computers - Neuronal networks are used in different fields of science and technology due to their capacity to approximate nonlinear functions through the synaptic weights...     

Multivariable system identification for integral controllability

Integral controllability is necessary and sufficient for a multivariable model to be usable in a decoupling controller with integral action that can be arbitrarily detuned without jeopardizing closed-loop robust stability. The design of experiments for identification of integral controllable models is challenging, because it must satisfy cumbersome eigenvalue inequalities involving a coupling between the real system and its model. To address this challenge, an optimization-based mathematical framework is developed that characterizes efficient identification experiments ensuring integral controllability. The proposed framework recovers well known experiment designs but also produces new ones of both theoretical and practical interest. Such designs are expressed either analytically or as a result of numerical optimization and are demonstrated in a number of examples. These designs can be easily implemented in industrial practice. By combining additional objectives or constraints of interest, the proposed framework can further serve as a basis for new experiment designs in future work.     

Balanced canonical forms for system identification

Parameterizations of linear state-space systems by balanced canonical forms are considered. The structure, in particular the topological properties, of these parameterizations are investigated. Based on these results an algorithm for identification obtained by optimizing a likelihood-type criterion function is proposed and analyzed. In the algorithm both the estimation of real-valued parameters and of integer parameters (defining dynamic specification) is incorporated. The analysis of the algorithm emphasizes consistency results     

On output-error methods for system identification

In this paper are derived consistency and asymptotic normality results for the output-error method of system identification. The output-error estimator has the advantage over the prediction-error estimator of being more easily computable. However, it is shown that the output-error estimator can never be more efficient than the prediction-error estimator. The main result of the paper provides necessary and sufficient conditions for the output-error estimator and the prediction-error estimator to have the same efficiency, irrespective of the spectral density of the noise process.     

Hierarchical ANN system for stuttering identification

The presented work covers the issue of applying neural networks to the recognition and categorization of non-fluent and fluent utterance records. Speech samples containing three types of stuttering episodes (blocks before words starting with plosives, syllable repetitions, and sound-initial prolongations) were applied. The proposed system, built with hierarchical neural network framework, was used and then evaluated with respect to its ability to recognize and classify disfluency types in stuttered speech. The purpose of the first network was to reduce the dimension of vector describing the input signals. The result of the analysis was the output matrix consisting of neurons winning in a particular time frame. This matrix was taken as an input for the next network. Various types of MLP networks were examined with respect to their ability to classify utterances correctly into two, non-fluent and fluent, groups. Good examination results were accomplished and classification correctness exceeded 84–100% depending on the disfluency type.     

Fuzzy sets of rules for system identification

The synthesis of fuzzy systems involves the identification of a structure and its specialization by means of parameter optimization. In doing this, symbolic approaches which encode the structure information in the form of high-level rules allow further manipulation of the system to minimize its complexity, and possibly its implementation cost, while all-parametric methodologies often achieve better approximation performance. In this paper, we rely on the concept of a fuzzy set of rules to tackle the rule induction problem at an intermediate level. An online adaptive algorithm is developed which almost surely learns the extent to which inclusion of a rule in the rule set significantly contributes to the reproduction of the target behavior. Then, the resulting fuzzy set of rules can be defuzzified to give a conventional rule set with similar behavior. Comparisons with high-level and low-level methodologies show that this approach retains the most positive features of both     

On the use of Poisson wavelet transform for system identification

The Poisson wavelet transform (PWT) has been introduced and successfully applied to model estimation by Kosanovich et al. [K.A. Kosanovich, A.R. Moser, M.J. Piovoso, Poisson wavelets applied to model identification, J. Process Contr. 5 (4), (1995) 225–234]. In the original work the analytical expressions for the PWT of the step response of an FOPTD system is used to estimate the model parameters and verify the appropriateness of the FOPTD structure for the process. This set of analytical expressions, which are fundamental to the use of the PWT for identification are shown to be incomplete. The rectified versions of these expressions are provided in this paper. An additional contribution of this work is the development of a formal framework for the conditions for appropriateness of FOPTD as the proposed model structure based on sensitivity analysis of the PWT. It is shown that this framework can be used to determine the appropriateness of an SOPTD structure as well. Simulation studies are presented to support the above.     

Automation of the stabilization diagrams for subspace based system identification

For the continuous real-time monitoring of structures, the realization of a fully automatic real time system identification without any human intervention is the most crucial step. In this study, a new technique for the automation of the stability diagrams is developed that uses the modal phase collinearity (MPC) in order to quantify the spatial consistency of the identification results. In the new technique, the stabilization diagram is modeled as a histogram composed of overlapping bins. New strategies for the multiple occurrence of poles in the neighboring bins and double poles within a bin are developed. A new cluster validity index is proposed which can solve the problem caused by the scale of measurements and which can be directly calculated from non-normalized data. The threshold limits are defined for the proposed index. The results of the study show that the automation of the pole selection process from the stabilization diagrams is successfully realized. It is also shown that for local modes, the MPC value will be substantially smaller as compared to the global modes and can be used as a quick, efficient and powerful measure of global versus local response behavior.     

缩合反应喷射混合器的数值模拟与验证

采用Fluent软件数值模拟缩合反应制多胺系统的喷射混合器,根据不同几何尺寸喷射器内的速度、压力、组分浓度分布情况,提出优化的喷射器设计并中试装置验证,表明物料的混合效果良好,喷射器压力变化的实验值与模拟值几乎相符,本文所建模型可为工业规模的喷射混合器设计、放大提供依据和参考.     

基于指纹的考场身份自动识别系统设计

对于传统考试时考生身份不易检查的问题,指纹识别可以很好地解决。应用指纹识别传感器实现指纹的采集与辨识,得到的结果进入单片机进行处理,相关的学生信息以及语音信息存储在SD卡中,得到的指纹识别结果信息通过LCD显示出来,用语音模块提示当前指纹识别是否正确。实践表明,设计的考场指纹识别系统使用效果好,性价比高,有着很好的应用前景。     

A new algorithm for linear system identification

This correspondence considers the on-line parameter identification of a forced linear discrete-time dynamic system from a sequence of white-noise-corrupted output measurements. In contrast to other approaches, the proposed stochastic approximation algorithm does not require knowledge of the noise statistics and converges to the true value of the parameters in the mean-square sense. If the input measurements are also corrupted with white noise, an additional term depending on the variance of the noise is required.     

An algorithm for nonlinear system parameter identification

An algorithm for system parameter identification will be presented in this paper. The method is applicable to linear and nonlinear systems with known structures. It is applied in this paper to systems in which both system and measurement noises can be neglected. The algorithm requires less time per iteration and less computer storage than the quasilinearization method. A shaft position control system with multiple nonlinearities will be used to illustrate the method.