使用遗传算法划分交换式工业以太网

针对交换式工业以太网的数据通信特点,采用遗传算法来寻找其网络划分问题的次优解。将网络划分问题等价为一个多目标优化问题,即一方面要减少子网间通信量,同时需要平衡各个子网的通信负荷;此外交换机物理条件的限制也为该优化问题设置了约束条件。在设计遗传算法的适应度函数和遗传算子时,充分考虑了控制网络的通信特点:如控制器的存在和现场设备通信的单向性。最后通过仿真验证了该算法的有效性。     

Parameter Estimation and Subset Selection for Separable lower Triangular Bilinear Models

Abstract. Parameter estimation and subset selection for separable lower triangular bilinear (SLTBL) models are considered. Under a flat prior, we present an expectation–maximization (EM) algorithm to obtain the maximum likelihood estimation. Furthermore, two sub‐procedures are designed to select the best subset model after an initial fitting. Example with two simulated and one real data set illustrate the feasibility and validity of the proposed methods.     

基于优进策略的遗传算法对重油热解模型参数的估计

针对常规遗传算法全局寻优效率偏低的弱点，提出了一种优进策略，用以改进常规遗传算法。该策略将从繁衍过程中获取进化信息，自适应地改进子代分布，适时引入确定性操作，以提高全局寻优性能。提出的相关技术包括维持种群的多样性、改进交叉算子、增加Powell寻优算子等。实例测试表明这种优进策略效果良好，并已成功地应用于重油热解三集总动力学复杂数学模型的非线性参数估计。     

THE AUTOCORRELATION FUNCTION OF SEASONAL ARMA MODELS

Abstract. This note obtains the theoretical autocorrelation function of an ARMA model with multiplicative seasonality. It is shown that this function can be interpretated as the result of the interaction between the seasonal and regular autocorrelation patterns of the ARMA model. The use of this result makes easier the identification of the structure of the model, is helpful in choosing between a multiplicative or additive seasonal component and leads to a better understanding of the properties of the estimated autocorrelation function of scalar ARMA processes.     

基于Hammerstein模型的非线性系统辨识方法研究

工业大系统中Hammerstein模型的非线性系统,一般都是多输入多输出系统,具有大滞后、大惯性、时变性和强耦合性的特点,它的数学模型难于精确获得;且传统PID控制器无法使控制效果处于最佳状态的局限性.为了更加快速准确控制,使系统更加地稳定工作在最佳工作状态.利用分散辨识方法对Hammerstein模型的非线性系统进行...     

Bayesian Subset Model Selection for Time Series

Abstract.  This paper considers the problem of subset model selection for time series. In general, a few lags which are not necessarily continuous, explain lag structure of a time-series model. Using the reversible jump Markov chain technique, the paper develops a fully Bayesian solution for the problem. The method is illustrated using the self-exciting threshold autoregressive (SETAR), bilinear and AR models. The Canadian lynx data, the Wolfe's sunspot numbers and Series A of Box and Jenkins (1976) are analysed in detail.     

基于遗传算法的人工神经网络在油水层识别中的应用

油水层识别是测井二次解释的重要内容。基于遗传算法的人工神经网络方法具有自学、自适应和抗干扰等特点，能有效地区分油水层。实际应用表明，其误判率远低于传统方法。     

遗传算法及在采矿工程中的应用

遗传算法是建立在遗传学与自然选择基础上的搜索过程,搜索效率很高,能迅速收敛于最优解,叙述了传算法的原理及其在采矿工程中的应用,是解决采矿工程复杂问题的一种有效手段。     

Periodic autoregressive model identification using genetic algorithms

Periodic autoregressive (PAR) models extend the classical autoregressive models by allowing the parameters to vary with seasons. Selecting PAR time‐series models can be computationally expensive, and the results are not always satisfactory. In this article, we propose a new automatic procedure to the model selection problem by using the genetic algorithm. The Bayesian information criterion is used as a tool to identify the order of the PAR model. The success of the proposed procedure is illustrated in a small simulation study, and an application with monthly data is presented.     

Optimization of a Reverse Osmosis System Using Genetic Algorithm

Abstract

Reverse Osmosis (RO) has found extensive application in industry as a highly efficient separation process. In most cases, it is required to select the optimum set of operating variables such that the performance of the system is maximized. In this work, an attempt has been made to optimize the performance of RO system with a cellulose acetate membrane to separate NaCl‐Water system using Genetic Algorithm (GA). The GAs are faster and more efficient than conventional gradient based optimization techniques. The optimization problem was to maximize the observed rejection of the solute by varying the feed flowrate and overall permeate flux across the membrane for a constant feed concentration. To model the system, a well‐established transport model for RO system, the Spiegler‐Kedem model was used. It was found that the GA converged rapidly to the optimal solution at the 8th generation. The effect of varying GA parameters like size of population, crossover probability, and mutation probability on the result was also studied. The algorithm converged to the optimum solution set at the 8th generation. It was also seen that varying the computational parameters significantly affected the results.     

Identification of uncertain MIMO Wiener and Hammerstein models

Several approaches can be found in the literature to perform the identification of block oriented models (BOMs). In this sense, an important improvement is to achieve robust identification to cope with the presence of uncertainty.In this work, two special and widely used BOMs are considered: Hammerstein and Wiener models. The models herein treated are assumed to be described by parametric representations. The approach introduced in this work for the identification of the multiple input-multiple output (MIMO) uncertain model is performed in a single step. The uncertainty is described as a set of parameters which is found through the solution of an optimization problem.A distillation column simulation model is presented to illustrate the robust identification approach. This process is an interesting benchmark due to its well-known nonlinear dynamics. Both Hammerstein and Wiener models are used to represent this plant in the presence of uncertainty. A comparative study between these models is established.     

Recursive Prediction and Likelihood Evaluation for Periodic ARMA Models

This paper explores recursive prediction and likelihood evaluation techniques for periodic autoregressive moving-average (PARMA) time series models. The innovations algorithm is used to develop a simple recursive scheme for computing one-step-ahead predictors and their mean squared errors. The asymptotic form of this recursion is explored. The prediction results are then used to develop an efficient (and exact) PARMA likelihood evaluation algorithm for Gaussian series. We then show how a multivariate autoregressive moving average (ARMA) likelihood can be evaluated by writing the multivariate ARMA model in PARMA form. Explicit calculations for PARMA(1, 1) models and periodic autoregressions are included.     

基于遗传模拟退火算法的注塑机增力机构优化研究

对注塑机五铰链斜排双曲肘增力机构进行了运动和力学特性分析,采用遗传模拟退火算法对该增力机构进行了优化设计,以机构行程比较大、力的放大倍数较大和机构总长较小为目标函数建立了优化数学模型,该优化设计属于多目标函数优化问题,通过对增力机构实例优化计算与传统设计方法进行了比较,结果表明行程比增加5.97%,力的放大倍数增加14.10%,机构总长减小5.25%。     

Identification and Online Validation of a pH Neutralization Process Using an Adaptive Network-Based Fuzzy Inference System

In this study, the application of adaptive neuro-fuzzy inference system (ANFIS) architecture to build prediction models that represent the pH neutralization process is proposed. The dataset used to identify the process was obtained experimentally in a bench scale plant. The prediction model attained was validated offline and online and demonstrated as able to precisely predict the one step-ahead value of effluent pH leaving the neutralization reactor. The input variables were the current and one past value of the acid and base flow rates and the current value of the output variable. Variance accounted for (VAF) indices greater than 99% were achieved by the model in experiments in which the disturbances in the acid and basic solutions flow rates were applied separately. For tests with simultaneous disturbances, conditions never seen in the training and suffering from reactor level oscillations, the prediction model VAF index was still approximately 96%. The validations demonstrated the capability of ANFIS to build precise fuzzy models from input–output datasets. R² values achieved were always larger than 0.96.     

基于遗传算法的多效蒸发系统的优化

建立了包含冷凝水闪蒸、二次蒸汽抽取的复杂多效蒸发系统的优化模型，根据该模型约束方程多、具有强非线性等特点，研究了运用遗传算法求解该模型。通过实例运行表明，遗传算法具有较好的收敛性．能有效地求解复杂多效蒸发系统的优化模型。     

微分进化算法研究及其在热工过程参数辨识中的应用

被控对象的数学模型,对控制系统的设计和分析有着极为重要的意义.采用微分进化算法对被控对象参数进行辨识.这种算法与其它进化算法的不同之处在于它的变异算子是由种群中任意选取的多对向量的差值得到的,并且主要用于实参数优化问题.微分进化算法虽然有简单、搜索效率高的特点,但是仍存在局部最优的问题.因此,在对微分进化算法搜索机理进...     

基于自适应遗传算法的多变量系统近似模型设计方法

研究多变量系统近似模型设计方法中近似模型的选取问题,提出基于自适应遗传算法的最优近似模型选取算法。该方法是一种启发式智能选择方法,它改变了一直以来凭设计人员的经验选取近似模型的做法．使用方便,准确度高。所实现的基于近似模型技术的多变量系统鲁棒设计平台,集模型优化、系统分析和系统设计于一体,在工程上具有较大的应用价值。在自行研制的多变量系统设计平台中对一个参数不确定性工业对象的鲁棒系统进行设计,结果表明了该算法的有效性和实用性。     

Melt Instability Identification Using Unsupervised Machine Learning Algorithms

In industrial extrusion processes, increasing shear rates can lead to higher production rates. However, at high shear rates, extruded polymers and polymer compounds often exhibit melt instabilities ranging from stick-slip to sharkskin to gross melt fracture. These instabilities result in challenges to meet the specifications on the extrudate shape. Starting with an existing published data set on melt instabilities in polymer extrusion, we assess the suitability of clustering, unsupervised machine learning algorithms combined with feature selection, to extract and identify hidden and important features from this data set, and their possible relationship with melt instabilities. The data set consists of both intrinsic features of the polymer as well as extrinsic features controlled and measured during an extrusion experiment. Using a range of commonly available clustering algorithms, it is demonstrated that the features related to only the intrinsic properties of the data set can be reliably divided into two clusters, and that in turn, these two clusters may be associated with either the stick-slip or sharkskin instability. Furthermore, using a feature ranking on both the intrinsic and extrinsic features of the data set, it is shown that the intrinsic properties of molecular weight and polydispersity are the strongest indicators of clustering.     

多目标模糊评价遗传算法综合质量交换网络

综合质量交换网络时，评价指标往往不止一个，多个指标相互之间往往又是无法比较、相互矛盾的，甚至有的指标只是定性的，无法定量地表示。故尝试应用模糊数字对多目标进行评价，有效地模拟了多目标决策的过程，过程中规则的变换可反应决策者对不同目标的重要性取向。此外还提出了多目标模糊评价遗传算法（MOGAFR）求解多目标优化问题。数例的求解证明了该法可能得到非劣解集合，并可以对非劣解集中的个体区分出优劣，规则不同，可以收敛到非劣解集合的不同部分，多目标质量产换网络的综合证明其解决工程问题的有效性，所提出的方法适用于求解其他系统的多目标优化问题。