基于专家—神经网的递阶智能控制系统

本文介绍了专家系统与人工神经网络技术相结合的一些方法，提出了联合使用专家系统与人工神经网络的递阶智能控制系统结构，其组成为：监督控制专家系统，数据处理与故障诊断神经网络，常规分布式控制系统。其中的监督控制专家系统又由人工神经网络模拟实现。由于本系统吸取了专家系统技术及人工神经网络技术两者的长处，克服了各自的缺点，使得系统的性能大为提高。     

复杂工业过程的综合集成智能控制

本文针对铝厂熟料窑的现状：生产过程控制 复杂、具有慢时变、分布参数、非线性、大时滞特性,难以精确描述数学模型,提出一套实 时智能控制方案：将人工智能、专家控制、神经模型控制各自的长处综合一起,设计成一个 集成环境的实时多级智能控制系统,并应用于熟料窑温控系统中．经仿真和实时控制试验结 果表明这种系统对改善系统的动、静态特性,克服系统时滞、慢时变是有效的,控制性能令 人满意．     

复杂工业的多模态集成智能控制研究

针对当今工业现场实际,过程控制复杂,具有慢时变、分布参数、非线性、大时滞特性,难以精确描述数学模型,很难达到自动控制的目的;提出了一种多模态集成智能控制方案：将专家控制、神经模糊控制集成在一起,通过对复杂工业对象THJ-3装置进行仿真和实时控制实验,取得了满意的控制效果。     

智能集成控制技术在铝电解过程控制中的应用

研究了将专家系统、神经网络、模糊控制、混沌优化等先进智能技术相集成，共同完成铝电解生产过程控制的智能集成控制技术，建立了基于神经网络的电解槽参数预测模型，设计了槽况解析神经网络专家系统，设计了基于混沌优化的电解质温度模糊控制系统及电解槽极距专家模糊控制系统．实验结果表明，系统能有效地实现预焙铝电解槽智能集成控制，实现槽状态在线解析，并能达到优良的生产控制指标，为企业带来了巨大的经济效益和社会效益．     

基于小波神经网络的加工过程自适应控制

把信息熵、小波分析和神经网络相结合,提出了基于小波神经网络的加工过程自适应控制系统及其自适应控制算法。提出并定义了广义熵方误差函数,在理论上证明了广义熵方误差函数的有效性。用广义熵方误差函数准则取代BP算法的均方误差准则,用自适应地搜索小波基函数和自适应地调整小波的尺度参数、平移参数和神经网络权值的方法对参数变化的切削力进行在线控制。仿真结果表明,该系统响应快,无超调,比传统的加工过程神经网络自适应控制具有更好的控制效果。     

基于神经网络的分解炉喂煤量智能控制系统

介绍了一种基于神经网络的智能专家系统,给出了系统的结构,具体描述了神经网络专家系统的基本原理。并以水泥工业的新型干法分解炉喂煤量智能控制系统为例说明了神经网络智能控制的推理过程。控制结果表明了该方法的有效性。     

Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks

Chemical processes are nonlinear. Model based control schemes such as model predictive control are highly related to the accuracy of the process model. For a highly nonlinear chemical system, it is clear to implement a nonlinear empirical model, such as artificial neural network model, should be superior to a linear model such as dynamic matrix model. However, unlike linear systems, the accuracy of a nonlinear empirical model strongly depends on its original data or training data based on how the model is built up. A regional-knowledge index is proposed in this study and applied in the analysis of dynamic artificial neural network models in process control. New input patterns that imply extrapolations and thus unreliable prediction by an artificial neural network model can be recognized from a significant decrease in the regional-knowledge index. To tackle the extrapolation problem and assure stability of the control system, we propose to run a neural adaptive controller in parallel with a model predictive control. A coordinator weights the outputs of these two controllers to make the final control decision. The present state of the controlled process and the model fitness to the present input pattern determine the weightings of the controller's output. The proposed analysis method and the modified model predictive control architecture have been applied to a neutralization process and excellent control performance is observed in this highly nonlinear system.     

Application of neural networks to temperature control in thermal power plants

In a thermal power plant with once-through boilers, it is important to control the temperature at the middle point where water becomes steam. However, there are many problems in the design of such a control system, due to a long system response delay, dead-zone and saturation of the actuator mechanisms, uncertainties in the system model and/or parameters, and process noise. To overcome these problems, an adaptive controller has been designed using neural networks, and tested extensively via simulations.

One of the key problems in designing such a controller is to develop an efficient training algorithm. Neural networks are usually trained using the output errors of the network, instead of using the output errors of the controlled plant. However, when a neural network is used to control a plant directly, the output errors of the network are unknown, since the desired control actions are unknown. This paper proposes a simple training algorithm for a class of nonlinear systems, which enables the neural network to be trained with the output errors of the controlled plant. The only a priori knowledge of the controlled plant is the direction of its output response. Due to its simple structure and algorithm, and good performance, the proposed controller has high potential for handling difficult problems in process-control systems.     

一个复杂工业过程控制MAS的设计与实现*

针对复杂工业过程控制的特点,建立了由控制Agent和决策支持Agent组成的分层递阶MAS(Multi Agent System);研究了基于Ice中间件的MAS开发方法,采用COM技术构建了具有二进制重用性的Agent组件,可实现对原有系统的重用;实现了MAS与专家系统、神经网络和模糊控制技术的融合;开发了新型干法水泥生产过程控制MAS,实现了水泥熟料煅烧过程的优化控制.     

An automated signalized junction controller that learns strategies from a human expert

An automated signalized junction control system that can learn strategies from a human expert has been developed. This system applies machine learning techniques based on logistic regression and neural networks to affect a classification of state space using evidence data generated when a human expert controls a simulated junction.The state space is constructed from a series of bids from agents, which monitor regions of the road network. This builds on earlier work which has developed the High Bid auctioning agent system to control signalized junctions using localization probe data. For reference the performance of the machine learning signal control strategies are compared to that of High Bid and the MOVA system, which uses inductive loop detectors.Performance is evaluated using simulation experiments on two networks. One is an isolated T-junction and the other is a two junction network modelled on the High Road area of Southampton, UK. The experimental results indicate that machine learning junction control strategies trained by a human expert can outperform High Bid and MOVA both in terms of minimizing average delay and maximizing equitability; where the variance of the distribution over journey times is taken as a quantitative measure of equitability. Further experimental tests indicate that the machine learning control strategies are robust to variation in the positioning accuracy of localization probes and to the fraction of vehicles equipped with probes.     

先进控制技术在预焙铝电解过程控制中的应用

设计了预焙铝电解生产过程先进控制系统的总体结构,建立了基于神经网络的电解槽参数及阳极效应预测模型,设计了基于支持向量机的槽况解析与槽况诊断专家系统,提出了极距NN—PID控制策略、电解质温度基于混沌优化的模糊控制策略及氧化铝浓度的模糊专家控制策略;同时,针对有关参数的优化控制和产量质量能耗优化控制问题,提出了基于模糊专家控制技术的智能协调策略,以满足全局优化的控制目标;实验结果表明,系统能有效地实现预焙铝电解槽的优化控制,实现槽状态在线解析,并能达到优良的生产控制指标,为企业带来了巨大的经济效益和社会效益。     

焦炉加热智能控制系统的研究与应用

焦炉是具有大时滞、强非线性、多变量耦合、变参数的复杂对象，直行温度受多种因 素的影响，采用常规的控制方法难以将直行温度控制到要求的精度范围内．焦炉生产过程既 受连续时间信号驱动，又受离散事件信号驱动，本文将焦炉及其操作过程作为一类混杂系统 ，研究并开发了焦炉加热智能控制系统．系统采用神经网络建模、多变量模糊控制、专家控 制和预测控制等多种算法，构造了切换系统模型，在北京炼焦化学厂投入生产运行后，取得 良好控制效果．该系统对提高焦炭质量，降低能耗和延长炉体使用寿命都有重要的意义．     

基于神经网络的实时专家控制系统及其PTA工业应用

以精对苯二甲酸结晶过程为研究对象。提出一种基于神经网络模型的实时专家控制系统．该方法利用神经网络建模技术获取对象的机理知识,通过对影响模型特性的多个变量进行分析,自动得到常规专家控制系统难于获取的定性、定量知识,并按分级递阶的启发式搜索机制,实现了对工业过程对象的实时控制．实际应用表明：该方法不但克服了以往专家系统知识获取的瓶颈,而且有效实现了人机对话的功能,便于现场操作和更改专家知识库,为化工过程的多变量控制提供了新的思路．     

基于模糊神经网络的导弹故障诊断专家系统

为了实现对导弹测发控系统的故障诊断,研究了模糊神经网络理论与算法,以及和专家系统的结合方式;综合神经网络、专家系统和模糊逻辑的各自优点和特点,提出了构建基于模糊神经网络的故障诊断专家系统的基本原则,并给出了一种构建方法;通过将传统的专家系统技术与模糊神经网络技术相融合,文中构造了某型导弹测发控系统智能故障诊断系统,验证了方案的可行性,为类似系统的进一步实现进行了有益的探索。     

一种基于遗传算法的模糊神经网络最优控制

通过对控制系统的过程模拟,提出一种模糊神经网络最优控制方案。离线化部分基于遗传算法,分三阶段实现模糊神经网络控制器结构和参数的优化。在线优化部分通过重构模糊神经网络控制器的去模糊化部分,进一步调整控制规则,实现在线去模糊优化。仿真结果表明该方案优于常模糊控制方案和基于专家经验的模糊神经网络控制方案。     

基于神经网络的化工聚合过程故障诊断方法

针对化工聚合反应的特点,结合BP神经网络和专家系统各自在在故障诊断中的优点,利用神经网络的自学习能力和专家系统的解释机能,构建了基于专家经验的神经网络故障诊断系统,用于解决在生产过程中出现异常情况的故障诊断。仿真结果表明,该诊断系统不仅提高了系统运行的可靠性和准确性,而且运用神经网络的并行处理能力来完成推理过程,大大提高了故障诊断的速度。     

Quality control expert systems: a review of pertinent literature

Statistical quality control (SQC) is an effective tool that ensures quality products and services by means of control charts, the essence of SQC, and sampling plans. While the computation of sample statistics and the development of control charts are routine exercises, the interpretation of chart patterns, trends and the associated diagnosis of assignable causes requires expert knowledge. The present trend is to develop a quality control system and apply it throughout the company (company-wide quality control CWQC or total quality control - TQC). This frequently means involvement of non-quality personnel in QC teams. Additionally, many companies are faced with a shortage of experienced quality controllers and individuals who can train and educate others on statistical quality control techniques. Quality control expert systems (QCESs) are considered as one way to alleviate these difficulties. In recent years, quality control expert systems have attracted the attention of both quality researchers and practitioners. This paper reviews existing quality control expert systems and recommends a set of quality engineering techniques that should be used to form a knowledge base, the heart of an expert system.     

Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process

This paper presents a neural‐network‐based predictive control (NPC) method for a class of discrete‐time multi‐input multi‐output (MIMO) systems. A discrete‐time mathematical model using a recurrent neural network (RNN) is constructed and a learning algorithm adopting an adaptive learning rate (ALR) approach is employed to identify the unknown parameters in the recurrent neural network model (RNNM). The NPC controller is derived based on a modified predictive performance criterion, and its convergence is guaranteed by adopting an optimal algorithm with an adaptive optimal rate (AOR) approach. The stability analysis of the overall MIMO control system is well proven by the Lyapunov stability theory. A real‐time control algorithm is proposed which has been implemented using a digital signal processor, TMS320C31 from Texas Instruments. Two examples, including the control of a MIMO nonlinear system and the control of a plastic injection molding process, are used to demonstrate the effectiveness of the proposed strategy. Results from both numerical simulations and experiments show that the proposed method is capable of controlling MIMO systems with satisfactory tracking performance under setpoint and load changes. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

工业加热炉燃烧过程的模糊专家控制策略

本文将模糊控制与专家控制相结合，提出了一种模糊专家控制策略，用于工业加热炉燃烧过程的温度控制、在温度偏差较小时使用模糊控制，保证较好的控制精度；在温度偏差较大时采用专家控制，保证快速的升温和降温效果，实际应用表明，本文提出的控制策略具有响应快、鲁棒性强的特点，达到了较好的控制性能。     

Taking decisions in the expert intelligent system to support maintenance of a technical object on the basis information from an artificial neural network

In this paper, an intelligent operation system, which consists of an intelligent diagnostic subsystem (with a neural network) and an intelligent maintenance subsystem (with an expert system), was presented and discussed. The artificial neural network and the expert system, which use the information developed in the neural network, perform a special function in this system. The functional combination of the artificial neural network and the expert system together created a new solution in the form of an intelligent system, which was referred to as an intelligent maintenance system. This article also covers decision-making methods that are used in an expert maintenance system and whose purpose is an organization and control of the process of the prevention of technical objects. For this purpose, the method was described of taking decisions by an expert for complex parametric type hypotheses and for simple finished type hypotheses in the set of possible decisions’ hypotheses. A considerable part of this paper covers the presentation of the method to transform diagnostic information into the required form of maintenance information. For this purpose, an algorithm of the work of maintenance system was performed and descried. In the creation process of the maintenance knowledge base, the specialist knowledge of a human specialist was also used. Hence, a skilful and proper taking of decisions by an expert to create this set of information is essential. Two inference methods were characterized and described in this paper. The theoretical results obtained were verified in the examination of the influence of each of these decision-making inference methods on the final results of the process of the prevention treatment of an object.