基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题,提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network,AFNN)的模型预测控制(model predictive control, MPC)方法。首先,在离线建模阶段,AFNN采用规则自分裂技术产生初始模糊规则,采用改进的自适应LM学习算法优化网络参数;然后,在实时控制过程,AFNN根据系统输出和预测输出之间的误差调整网络参数,从而为MPC提供一个精确的预测模型;进一步,AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题,在线获取非线性控制量,并将其作用到动态系统实施控制。此外,给出了AFNN-MPC的收敛性和稳定性证明,以保证其在实际工程中的成功应用。最后,利用数值仿真和双CSTR过程进行实验验证。结果表明,AFNN-MPC能够取得优越的控制性能。     

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...     

RBF神经网络在非线性系统辨识中的应用

对于非线性系统的预测辨识,提出用RBF神经网络作为预测模型,通过函数逼近完成对非线性系统的辨识。     

基于自适应神经模糊推理系统的磁悬浮电动机控制

磁悬浮电动机是一个非线性、强耦合的复杂系统,阐述了它的工作原理并建立了数学模型,为使该系统具有良好的动态性能和稳定性,采用逆系统方法解耦线性化,然后以解耦后的伪线性子系统为对象,设计了神经模糊PID控制器,该控制器利用PID算法实现控制的准确性,利用模糊控制的逻辑能力和神经网络的自学习能力来提高控制的快速性和自适应性.仿真结果表明,采用神经模糊PID控制响应快、无超调、过渡时间短,实现了预期目的.     

非线性模糊规划中分段线性隶属函数的构造方法

首先给出了模糊规划中单、双边非线性隶属函数的构建技术。为了解决非线性模糊规划的求解困难问题,进一步提出了一种针对这两类非线性隶属函数进行分段线性化的方法,由高斯取整函数计算出非线性隶属函数的离散点,再根据决策者选择的基准点得到分段线性的隶属函数。构建了相应的模糊规划的规范求解形式,并对数值实例进行了测试,验证了其有效性。     

基于CFD的系统辨识方法

概述了计算流体力学(CFD)数值模拟和系统辨识的原理,详细阐述了基于CFD数值模拟的系统辨识“灰箱”建模方法的基本原理和实现步骤,对近几年该方法的应用案例进行分析,指出该建模方法存在的问题及发展方向.     

基于C-R模型的非线性系统模糊内模控制算法研究

针对大多数非线性系统的内模控制难以建立精确的对象模型和逆模型的问题,引入了基于C-R模型的非线性系统建模方法,并在此基础上研究基于C-R模型的非线性系统的模糊内模控制设计。由C-R模型的辨识递推算法获得对象模型和逆模型,并建立模糊内模控制算法。通过对连续搅拌反应釜控制系统的仿真表明,该控制方法有效、可行,具有良好的控制品质。     

基于快速滑窗QR分解的自适应子空间辨识

子空间辨识直接由输入输出数据辨识得到过程状态空间模型,在多变量系统的辨识中取得广泛应用。实现在线子空间辨识算法的关键在于快速、高效的QR分解及SVD分解更新算法。通过将Updating和Downdating操作有效结合,提出了一种快速的滑窗QR分解算法,减少了不必要的重复步骤,进一步提高了计算效率。复杂度分析结果表明,随数矩阵行数增加,快速滑窗QR分解算法比Updating、Downdating两步法可以减少8.3%的计算量。将快速滑窗QR分解算法用于PO-MOESP子空间辨识算法的自适应更新,并通过数值仿真实例验证了算法的有效性。     

一种非线性系统在线辨识的选择性递推方法

针对非线性系统的在线辨识, 提出了一种选择性递推岭参数极限学习机方法。首先, 推导了岭参数极限学习机模型节点增加的递推算法, 以有效地更新在线模型。其次, 结合训练模型的相对误差, 提出模型节点递推增加的选择性策略, 以限制模型的复杂度, 获得更简单的递推辨识模型。通过一个典型非线性化工过程的在线辨识, 从多方面比较验证了所提出方法的简单有效, 更适合非线性过程的在线辨识。     

基于改进自适应模糊推理系统的半导体制造系统瓶颈设备预测方法(英文)

Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.     

一种基于数据驱动的模糊系统建模方法

针对工业生产中的一些复杂、非线性模糊系统,传统的建模方法很难描述其特性,而在实际生产中存在大量输入输出数据,提出了一种通用的基于数据驱动的模糊系统建模方法.采用减法聚类和模糊C-均值相结合的模糊聚类算法对输入空间进行划分,进而从输入输出采样数据中提取系统模糊规则,这样使得被辨识模型可用若干局部线性模型表示,然后利用递推最小二乘法对后件参数进行辨识,从而建立了非线性系统的T-S模糊模型.最后,应用该方法对一个非线性系统进行辨识,仿真结果验证了所提方法的有效性.     

粗糙集与模糊推理相集成的过程建模方法及其应用

针对复杂化工过程机理建模困难的问题,采用适应性较广的模糊方法经验建模,鉴于模糊法对于高维、强相关的样本数据很难导出规则,本文提出先用粗糙集方法消除冗余性,约简系统,获取最小规则集,在此基础上构建结构合理、参数可适当初始化的模糊-神经网络,并采用LM算法训练,收敛速率快,模型预测性能良好.将此法用于PTA装置溶剂脱水塔精馏过程的经验建模,效果令人满意,性能优于现代统计方法和前馈神经网络.     

Modeling Intermittent Drying Using an Adaptive Neuro-Fuzzy Inference System

Artificial intelligence systems such as artificial neural networks (ANN) and fuzzy inference systems (FIS) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. The advantages of a combination of ANN and FIS are obvious. This article presents the application of a hybrid neuro-fuzzy system called adaptive-network-based fuzzy inference system (ANFIS) to time dependent drying processes and is illustrated by an application to model intermittent drying of grains in a spouted bed. An introduction to the ANFIS modeling approach is also presented. The model showed good performance in terms of various statistical indices.     

Modeling Intermittent Drying Using an Adaptive Neuro-Fuzzy Inference System

ABSTRACT

Artificial intelligence systems such as artificial neural networks (ANN) and fuzzy inference systems (FIS) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. The advantages of a combination of ANN and FIS are obvious. This article presents the application of a hybrid neuro-fuzzy system called adaptive-network-based fuzzy inference system (ANFIS) to time dependent drying processes and is illustrated by an application to model intermittent drying of grains in a spouted bed. An introduction to the ANFIS modeling approach is also presented. The model showed good performance in terms of various statistical indices.     

Fuzzy Inference System for Modeling of Zinc Removal Using Micellar-Enhanced Ultrafiltration

Discrete event computer simulation is one of the most widely used modeling tools for production systems. The major objective of this study is to develop a fuzzy rule-based model for the evaluation of micellar-enhanced ultrafiltration (MEUF) performance. In the current work, the foldover fractional factorial designs were applied as a screening experiment to determine all the influential factors affect Zn²⁺ rejection and permeate flux response. According to analysis of the variance (ANOVA) a three-screened significant factors, three-level full factorial designs (3 ^k ) was applied. Finally, a fuzzy model has been developed to predict and calculate the output variables. These mathematical models are found to be reliable predictive tools with an excellent accuracy with AARE ±1.08%, ±3.75%, in comparison with experimental values for permeate flux and rejection, respectively. It was observed that there is acceptable agreement between and fuzzy model results with experimental data.     

储层建模新技术、新方法研究——基于模糊系统的储层建模方法

本文将以模糊规则、解模糊化方法和自学习过程为核心的模糊系统引入到储层参数的建模中,提出了模糊系统插值和随机模拟的思路和实现方法,并以著名的GSL IB软件所提供的实测数据为例进行了对比分析。研究表明利用模糊系统可以有效进行地质参数的建模,与传统的地质统计学方法(克里格和序贯高斯模拟)相比。模糊系统方法在建模精度、对条件数据分布和特征参数的恢复方面均较优。这一研究成果对储层参数建模技术的发展有着重要的意义。     

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.     

基于模糊自适应PID的回转炉温度控制系统

针对回转炉温度控制的滞后性、强耦合性等特点,设计基于模糊自适应PID控制算法的回转炉温度控制系统。给出系统的结构、工作原理、网络结构与控制器设计方法。Matlab仿真实验结果证明:与传统PID控制相比,基于模糊自适应PID的回转炉温度控制系统具有更好的动态调节性能,满足控制需求。