限定记忆法辨识带钢热镀锌退火炉模型参数

最小二乘参数估计方法可用于线性的或非线性的系统参数辨识,包括动态的、静态的和参数的或非参数的模型辨识,其递推算法更是收敛可靠,简单实用。但是随着数据的不断增长,最小二乘的递推算法将失去修正能力,出现数据饱和现象,限定记忆最小二乘法解决了这一问题,并能得到无偏、一致、有效估计。以已建立的连续带钢热镀锌退火炉数学模型为实例,用限定记忆最小二乘法辨识连续带钢热镀锌退火炉模型参数。通过对限定记忆最小二乘法的研究,进行模型参数辨识,并给出辨识结果和分析,结果证明了该方法的优越性。     

连续退火炉加热段过程静态模型的建立

连续退火炉是生产高质量深冲汽车板生产线上重要的生产设备之一。针对其作为一个非线性、时变、多变量强耦合、大时滞的控制对象,难以建立精确的数学模型的问题,在某一连续退火炉加热段已得的静态模型基础上,运用最小二乘法,对模型参数进行辨识,完善模型结构;介绍了最小二乘静态模型算法的编程思想,并通过对大量数据进行模型辨识及Matlab仿真,证明了该算法的有效性。该技术提高了模型精度,对提高工业生产的产品质量及产量具有重要意义。     

Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network

In this study, an artificial neural networks study was carried out to predict the compressive strength of ground granulated blast furnace slag concrete. A data set of a laboratory work, in which a total of 45 concretes were produced, was utilized in the ANNs study. The concrete mixture parameters were three different water–cement ratios (0.3, 0.4, and 0.5), three different cement dosages (350, 400, and 450 kg/m³) and four partial slag replacement ratios (20%, 40%, 60%, and 80%). Compressive strengths of moist cured specimens (22 ± 2 °C) were measured at 3, 7, 28, 90, and 360 days. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, ground granulated blast furnace slag, water, hyperplasticizer, aggregate and age of samples and, an output parameter which is compressive strength of concrete. The results showed that ANN can be an alternative approach for the predicting the compressive strength of ground granulated blast furnace slag concrete using concrete ingredients as input parameters.     

基于粒子群优化的Wiener模型辨识与实例研究

针对一类工业过程中可描述成Wiener模型的非线性系统,其辨识问题可等价成以估计参数为优化变量的非线性极小值优化问题．利用粒子群优化(PSO)算法在整个参数空间内并行搜索获得极小值优化问题的最优解(Wiener模型的最优估计),通过对粒子的迭代轨迹进行分析,改进了PSO算法中惯性权重和学习因子的选择．通过一个Wiener模型的数值仿真验证了本文提出的辨识方法的有效性和实用性,并将该方法应用在连续退火机组加热炉产品质量模型的辨识研究,取得了满意的辨识效果．     

改进退火遗传算法在SQL数据挖掘参数优化中的应用

针对SQL数据挖掘在复杂动力学系统故障诊断中的模式分类问题,以决策树参数优化为例,开展SQL数据挖掘分类算法参数优化研究。目前数据挖掘中的各类算法参数往往根据经验值设定,预测精度不高;只用遗传算法进行参数优化,分类预测结果容易发生振荡和早熟现象。采用改进的退火遗传算法对SQL数据挖掘中的决策树算法参数进行优化,解决了人工经验设置参数效率低下、精度不高的问题,同时实现了全局搜索,快速收敛到全局最优解。     

Fuzzy modeling of fluidized catalytic cracking unit

The paper deals with the fuzzy system identification of reactor–regenerator–stripper–fractionator's (RRSF) section of a fluidized catalytic cracking unit (FCCU). The fuzzy system identification based on the data collected from an operating refinery of FCCU of capacity, 1.2 MMPTA, with a sample time of 10 min. A generalized fuzzy model (GFM) and identification of structure and model parameter for multi-input/single output is presented. The GFM has the capability of representing both the CRI model and TS model under certain conditions. The structure identification and the parameter estimation are carried out using hybrid learning approach comprising modified mountain clustering and gradient descent learning with least square estimation (LSE) for the identification of a fuzzy model. The modified mountain clustering considers every data point as a potential cluster center in x × y hyperspace. The optimum number of clusters, which leads to an optimum number of rules, is determined with the help of validity function that guides the search. The obtained result from the modified mountain clustering initializes the GFM. Further hybrid of the gradient descent technique and LSE is aimed at learning of the GFM parameters in two phases. In the first phase of an epoch of learning gradient descent tunes the premise parameter and index of fuzziness of each rule. In second phase, LSE utilizes the results of first phase for evaluating the coefficient of local linear model of corresponding rules.     

A systematic neuro-fuzzy modeling framework with application tomaterial property prediction

A systematic neural-fuzzy modeling framework that includes the initial fuzzy model self-generation, significant input selection, partition validation, parameter optimization, and rule-base simplification is proposed in this paper. In this framework, the structure identification and parameter optimization are carried out automatically and efficiently by the combined use of a sell-organization network, fuzzy clustering, adaptive back-propagation learning, and similarity analysis-based model simplification. The proposed neuro-fuzzy modeling approach has been used for nonlinear system identification and mechanical property prediction in hot-rolled steels from construct composition and microstructure data. Experimental studies demonstrate that the predicted mechanical properties have a good agreement with the measured data by using the elicited fuzzy model with a small number of rules.     

A regression tree algorithm for the identification of convergence clubs

The concept of convergence clubs is analyzed and compared with classical methods for the study of economic β-convergence, which often consider the entire data set as one sample. A technique for the identification of convergence clubs is proposed. The algorithm is based on a modified version of the usual regression trees procedure. The objective function of the method is represented by the difference among the parameters of the model under investigation. Different strategies are adopted in the definition of the model used in the objective function of the algorithm. The first is the classical non-spatial β-convergence model. The others are modified β-convergence models which take into account the dependence showed by spatially distributed data. The proposed procedure identifies situation of local stationarity in the economic growth of the different regions: a group of regions is divided into two sub-groups if the parameter estimates are significantly different among them. The algorithm is applied to 191 European regions for the period 1980-2002. Given the adaptability of the algorithm, its implementation provides a flexible tool for the use of any regression model in the analysis of non-stationary spatial data.     

Artificial bee colony algorithm for small signal model parameter extraction of MESFET

This paper presents an application of swarm intelligence technique namely artificial bee colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs metal extended semiconductor field effect transistor (MESFET) device and compares its performance with particle swarm optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the quality of solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16—element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5–25 GHz.     

The optimal stochastic control of an epitaxial process unit

The problem of computerized batch control of the silicon epitaxial layer deposition technological process has been solved using optimal stochastic control methods. A control algorithm is presented the main emphasis being given to the forecasting and compensating of disturbing processes which act on a process unit under real operation conditions. The method of multidimensional time series, stochastic model form identification for the process noise is developed based on multidimensional time series, correlation analysis results. The “maximum likelihood” identification method is applied in order to obtain efficient estimates of the model parameters. The identification of the model form and model parameters is carried out on the basis of a rather extensive set of data obtained from operation records of a high capacity epitaxial unit. The adequacy of the identified models is checked by means of a correlation analysis of the model residuals. It is demonstrated that results comparable to those with an intuitive process control by an experienced operator, can be achieved when using the algorithm presented in the present work for process computer control. This algorithm thus represents a reliable and rational basis for process control computer software development.     

Optimization strategies for non-linear material parameters identification in metal forming problems

The quality of Finite Element Analysis (FEM) results relies on the input data, such as the material constitutive models. In order to achieve the best material parameters for the material constitutive models assumed a priori to represent the material, parameter identification inverse problems are considered. These inverse problems attempt to lead to the most accurate results with respect to physical experiments, i.e. minimizing the difference between experimental and numerical results.In this work three constitutive models were considered, namely, a non-linear elastic–plastic hardening model, a hyperelastic model -more specifically the Ogden model- and an elasto-viscoplastic model with isotropic and kinematic work-hardening.For the determination of the best suited material parameter set, two different optimization algorithms were used: (i) the Levenberg–Marquardt algorithm, which is gradient-based and (ii) a real search-space evolutionary algorithm (EA).The robustness and efficiency of classical single-stage optimization methods can be improved with new optimization strategies. Strategies such as cascade, parallel and hybrid approaches are analysed in detail. In hybrid strategies, cascade and parallel approaches are integrated. These strategies were implemented and analysed for the material parameters determination of the above referred material constitutive models.It was observed that the developed strategies lead to better values of the objective function when compared with the single-stage optimizers.     

Implicit rule-based fuzzy-neural networks using the identification algorithm of GA hybrid scheme based on information granulation

This paper proposes an identification method for nonlinear models realized in the form of implicit rule-based fuzzy-neural networks (FNN). The design of the model dwells on the technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithm. The FNN modeling and identification environment realizes parameter estimation through a synergistic usage of clustering techniques, genetic optimization and a complex search method. An HCM (Hard C-Means) clustering algorithm helps determine an initial location (parameters) of the membership functions of the information granules to be used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the optimization algorithm of a GA hybrid scheme. The proposed GA hybrid scheme combines GA with the improved complex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) is used in the model design in order to achieve a sound balance between its approximation and generalization abilities. The proposed type of the model is experimented with several time series data (gas furnace, sewage treatment process, and NOx emission process data of gas turbine power plant).     

Identifiability analysis and parameter estimation of a single Hodgkin-Huxley type voltage dependent ion channel under voltage step measurement conditions

Identifiability analysis of a single Hodgkin-Huxley (HH) type voltage dependent ion channel model under voltage clamp circumstances is performed in order to decide if one can uniquely determine the model parameters from measured data in this simple case. It is shown that the two steady-state parameters (m_∞, h_∞) and the conductance (g) are not globally identifiable together using a single step voltage input. Moreover, no pair from these three parameters is identifiable. Based on the results of the identifiability analysis, a novel optimization-based identification method is proposed and demonstrated on in silico data. The proposed method is based on the decomposition of the parameter estimation problem into two parts using multiple voltage step traces. The results of the article are used to formulate explicit criteria for the design of voltage clamp protocols.     

Optimization techniques in respiratory control system models

One of the most complex physiological systems whose modeling is still an open study is the respiratory control system where different models have been proposed based on the criterion of minimizing the work of breathing (WOB). The aim of this study is twofold: to compare two known models of the respiratory control system which set the breathing pattern based on quantifying the respiratory work; and to assess the influence of using direct-search or evolutionary optimization algorithms on adjustment of model parameters. This study was carried out using experimental data from a group of healthy volunteers under CO₂ incremental inhalation, which were used to adjust the model parameters and to evaluate how much the equations of WOB follow a real breathing pattern. This breathing pattern was characterized by the following variables: tidal volume, inspiratory and expiratory time duration and total minute ventilation. Different optimization algorithms were considered to determine the most appropriate model from physiological viewpoint. Algorithms were used for a double optimization: firstly, to minimize the WOB and secondly to adjust model parameters. The performance of optimization algorithms was also evaluated in terms of convergence rate, solution accuracy and precision. Results showed strong differences in the performance of optimization algorithms according to constraints and topological features of the function to be optimized. In breathing pattern optimization, the sequential quadratic programming technique (SQP) showed the best performance and convergence speed when respiratory work was low. In addition, SQP allowed to implement multiple non-linear constraints through mathematical expressions in the easiest way. Regarding parameter adjustment of the model to experimental data, the evolutionary strategy with covariance matrix and adaptation (CMA-ES) provided the best quality solutions with fast convergence and the best accuracy and precision in both models. CMAES reached the best adjustment because of its good performance on noise and multi-peaked fitness functions. Although one of the studied models has been much more commonly used to simulate respiratory response to CO₂ inhalation, results showed that an alternative model has a more appropriate cost function to minimize WOB from a physiological viewpoint according to experimental data.     

LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages

Leaf area index (LAI) is a basic quantity indicating crop growth situation and plays a significant role in agricultural, ecological and meteorological models at local, regional and global scale. It is a common approach to invert LAI based on canopy reflectance models using optimization method. Radiative transfer model for continuous vegetation canopy such as SAIL models is widely used for crop LAI inversion. However, crops are mostly planted as row structure in China and they don't fit the assumptions of continuous vegetation especially at the earlier growth stages. What kind of models should be used to invert LAI for typical row-planted crops at different growing stages? Taking corn as an example, the factors which influence the row planted crop LAI estimation are investigated in this paper. Using the computer simulated BRDF data sets, different models for LAI inversion at different growth stages are evaluated based on parameter sensitivity analysis. Bayes theory is used to introduce a priori knowledge in the inversion process. In 2005, a field campaign is carried out to validate LAI inversion accuracy during corn's growing stages in Huailai, Hebei Province, China. Inverted LAI from both the measured Canopy Reflectance (CR) data and Moderate Resolution Imaging Spectroradiometer (MODIS) data are very promising. The results show that at least two kinds of models should be adopted for corn canopy at different growth stages, i.e., row structure model for early growth stage (before elongation) and homogeneous canopy model for later growth stage (after elongation).     

一种T-S 模型的在线辨识算法

提出一种能通过输入输出数据在线获得T-S模型的结构和参数的辨识算法。首先,对输入空间进行划分,并在线优化子空间的形状和个数;然后,通过RLS更新子模型参数,使各个子模型逼近当前工况的实际系统;当子空间生成或形状发生变化时,调整相应子模型参数和数据矩阵;最后,针对非线性动态系统和煤气炉数据进行仿真实验,验证了所提出算法的有效性。     

Data reconciliation for real-time optimization of an industrial coke-oven-gas purification process

Online optimization is more and more used in the chemical industry to run a process near its optimum operating condition by providing real-time computed optimal set-points to the distributed control system. Process measurements are necessary for these applications to determine the actual state of the process and to increase the accuracy of the model with parameter estimation techniques. However, these measurements usually contain random as well as gross errors which have to be identified and eliminated before the measurements are used for online optimization. In this contribution, a data reconciliation approach was integrated into an online optimization framework for the ammonia hydrogen sulfide circulation scrubbing, a common industrial coke-oven-gas purification process. We used a rigorous rate-based model to describe this reactive absorption and desorption process. To increase the accuracy of the model, we estimated several process parameters using a sequential parameter estimation approach. Data reconciliation was performed based on simple component balances to achieve model-consistent data and to identify measurement biases. The model was then validated online on a pilot plant by connecting the estimation package through the process control system. Based on the online measured data, operating cost minimization was carried out and the computed optimal set-points realized real-time. A satisfactory agreement between measured data and optimization was achieved.     

Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms

All dynamic crop models for growth and development have several parameters whose values are usually determined by using measurements coming from the real system. The parameter estimation problem is raised as an optimization problem and optimization algorithms are used to solve it. However, because the model generally is nonlinear the optimization problem likely is multimodal and therefore classical local search methods fail in locating the global minimum and as a consequence the model parameters could be inaccurate estimated. This paper presents a comparison of several evolutionary (EAs) and bio-inspired (BIAs) algorithms, considered as global optimization methods, such as Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) on parameter estimation of crop growth SUCROS (a Simple and Universal CROp Growth Simulator) model. Subsequently, the SUCROS model for potential growth was applied to a husk tomato crop (Physalis ixocarpa Brot. ex Horm.) using data coming from an experiment carried out in Chapingo, Mexico. The objective was to determine which algorithm generates parameter values that give the best prediction of the model. An analysis of variance (ANOVA) was carried out to statistically evaluate the efficiency and effectiveness of the studied algorithms. Algorithm's efficiency was evaluated by counting the number of times the objective function was required to approximate an optimum. On the other hand, the effectiveness was evaluated by counting the number of times that the algorithm converged to an optimum. Simulation results showed that standard DE/rand/1/bin got the best result.     

Rule-based multi-FNN identification with the aid of evolutionary fuzzy granulation

In this paper, we introduce a category of Multi-Fuzzy-Neural Networks (Multi-FNNs) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs are based on a concept of fuzzy rule-based FNNs that use H ard C-M eans (HCM) clustering and evolutionary fuzzy granulation and exploit linear inference being treated as a generic inference mechanism of approximate reasoning. By this nature, this FNN model is geared toward capturing relationships between information granules–fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership functions) becomes an important design feature of the FNN model that contributes to its structural and parametric optimization. The genetically guided global optimization is then augmented by more refined gradient-based learning mechanisms such as a standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the experimental data, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates, and momentum coefficients) are adjusted using genetic algorithms. The proposed aggregate performance index helps achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate an effectiveness of the introduced model, several numeric data sets are experimented with. Those include a time-series data of gas furnace, NOx emission process of gas turbine power plant and some synthetic data.