基于RBFNN在线辨识的自适应PID预焙阳极焙烧炉温度控制

针对工况变化频繁的焙烧炉焙烧过程,提出了采用基于径向基函数(RBF)神经网络(NN)在线辨识的自适应PID控制策略。该方法通过RBF神经网络的自学习能力在线辨识系统模型,进而获得被控对象的Jacobian信息,实现对PID参数的在线调整。在对算法进行改进的基础上将其应用于预焙阳极焙烧炉温度过程控制中,实验结果表明,该方法具有很强的自适应能力和鲁棒性,达到了满意的控制效果。     

自适应模糊PID在双铝轧机机架间张力控制中的应用

张力控制是铝带轧机生产过程控制的难点。在双机架铝轧机控制系统中,通过控制机架间的张力大小,可以减少轧机的负荷,在一定程度上也可以改善铝带的板型质量。分析了张力控制的基本原理,基于双闭环的动力系统建立了以速度为主要影响因素的张力系统模型。通过自适应模糊PID的智能控制方式对双机架铝冷轧机中的张力模型进行仿真,仿真研究表明,自适应模糊PID对张力系统的控制优于常规PID,可减小系统较大幅度的振荡,而且能更快地达到张力控制系统的稳定状态。     

一种基于模糊神经网络FNN在加热炉温度控制中的应用

从实际出发,以昆明钢铁集团公司中板厂加热炉为研究对象,对具有时变性、非线性、模糊性的随机过程进行了研究。着重研究了神经网络与模糊系统融合的可行性及融合方式,采用了一种新型的智能控制方案——模糊神经网络控制。对提出的模糊神经网络控制算法进行了仿真试验,仿真结果表明,对比PID控制和自整定PID控制,采用本文所提出的模糊神经网络控制算法对加热炉进行控制,具有推理速度快,跟踪性能好,抗干扰能力强的优点,它完全能够满足工业生产需要,具有较强的可行性和实用性。     

基于BP神经网络的干熄焦CO自适应控制系统

由于干熄焦生产过程复杂多变,因此很难获得系统更具说服力的数据和运算形式,所以在传统的基础上现行的种种PID控制所达到的程度并不令人满意。为了克服上述缺陷,提出了基于BP神经网络的干熄焦CO自适应控制新方法。该方法通过BP神经网络对PID控制参数Kp、Ki和Kd进行在线优化控制,使得CO浓度控制在有效合理的区间范围内,从而达到了最佳性能指标,取得了良好的控制效果。     

基于神经网络的软测量技术及其应用

针对工业过程控制中存在的被控参数测量，控制器参数整定两个方面的实际问题进行研究，利用神经网络对被测参数进行软测量，采用无辨识自适应控制解决PID参数的整定问题，两者结合，给出了一种切实可行的解决问题的方法，并举出了一个实际应用的例子。     

镍闪速熔炼过程的模糊建模

针对冶金工业中镍闪速熔炼复杂工艺过程,提出了利用模糊理论建立镍闪模型的方法。一种方法是利用专家知识和操作经验（即ＩＦ－ＴＨＥＮ规则）建立闪速炉的先验模型;另一种方法是利用自适应模糊神经网络方法建立闪速炉的学习模型。综合考虑两种模型的建模结果,建立整个的模型。经过两个月的现场离线指导表明,这种建模方法能够较为准确地反映镍闪速炉的运行过程。     

基于人工智能的自适应板形控制

针对板带材轧制过程是一个复杂的非线性过程及传统板形控制模型的固有缺陷,为了提高冷轧带钢的板形质量和成材率,提出一种基于神经网络模糊推理的自适应板形控制（AI－AFC）方案,并将其引入森吉米尔20辊轧机的板形控制系统。离线仿真结果表明：该系统具有良好的控制性能,可提高板形控制质量。     

Comparison of Neural and Conventional Approaches to Mode Choice Analysis

This paper describes a new approach to behavioral mode choice modeling using neurofuzzy models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. In addition, the approach only selects the variables that significantly influence the mode choice and displays the stored knowledge in terms of fuzzy linguistic rules. This allows the modal decision-making process to be examined and understood in great detail. The neurofuzzy model is tested on the U.S. freight transport market using information on individual shipper and individual shipments. Shipments are disaggregated at the five-digit Standard Transportation Commodity Code level. Results obtained from this exercise are compared with similar results obtained from the conventional logit mode choice model and the standard back-propagation artificial neural network. The advantages of using the neurofuzzy approach are described.     

烧结过程的递阶集成智能控制系统研究(Ⅰ)--方案设计

烧结生产过程机理复杂 ,难以用精确数学模型描述。本文提出一种实时智能控制方案 :融合人工智能、专家系统、模糊控制、神经网络预测和自适应控制的优点 ,设计成一个集成环境的实时递阶智能控制系统。     

Thickness and Shape Synthetical Adjustment for DC Mill Based on Dynamic Nerve-Fuzzy Control

Due to the complexity of thickness and shape synthetical adjustment system and the difficulties to build a mathematical model, a thickness and shape synthetical adjustment scheme on DC mill based on dynamic nerve-fuzzy control was put forward, and a self-organizing fuzzy control model was established. The structure of the network can be optimized dynamically. In the course of studying, the network can automatically adjust its structure based on the specific questions and make its structure the optimal. The input and output of the network are fuzzy sets, and the trained network can complete the composite relation, the fuzzy inference. For decreasing the off-line training time of BP network, the fuzzy sets are encoded. The simulation results indicate that the self-organizing fuzzy control based on dynamic neural network is better than traditional decoupling PID control.     

基于改进遗传算法优化神经网络的板形控制

冷轧板形控制系统是一个强耦合、非线性的多变量复杂系统,难以建立精确的数学模型,一般常规的控制方法难以取得令人满意的控制效果。本文依据现场的轧制数据,提出采用自适应竞争遗传算法优化神经网络对其进行建模,采用模糊控制,可实现实时控制,并利用MATLAB编程,仿真结果显示了算法的有效性和时效性。     

Adaptive PID control system for an autonomous underwater vehicle

The control system of an autonomous underwater vehicle (AUV) is introduced. According to the control requirements of the AUV, a simple but practical adaptive PID control method is de- signed. The semi-physical simulation is done to test the feasibility of the control system. The neural network idea and the structure of PID controller are referred to design the adaptive PID controller. An intelligent integral is introduced to improve control precision. Compaed with traditional PID con- trollers, the adaptive PID controller has simple structure, good online adjusting ability, fast conver- gence and good robustness. The simulation experiments also show that the adaptive PID control sys- tem has high precision and fine antijamming ability.     

基于非线性预测模型的单神经元自适应PID板形控制

为了解决传统PID板形控制精度低、速度慢、抗干扰能力差等问题,将BP神经网络和单神经元引入到板形的控制中,提出一种基于BP神经网络预测模型的单神经元自适应PID控制的板形控制策略。利用BP神经网络的非线性逼近能力和单神经元的自学习、自适应能力,通过两者的有机结合寻找一个最佳的P、I、D非线性组合控制律,实现对带钢板形缺陷的有效控制。仿真实验结果表明,该控制算法能很好地跟踪板形的目标设定值,提高了系统的控制精度,加快了系统的响应速度,并且具备较强的抗干扰能力。     

Evolutionary Fuzzy Neural Inference System for Decision Making in Geotechnical Engineering

Problems in geotechnical engineering are full of uncertain, vague, and incomplete information. In most instances, successfully solving such problems depends on experts’ knowledge and experience. The primary object of this research was to develop an evolutionary fuzzy neural inference system (EFNIS) to imitate the decision-making processes in the human brain in order to facilitate geotechnical expert decision making. First, an evolutionary fuzzy neural inference model (EFNIM) was constructed by combining the genetic algorithm (GA), fuzzy logic (FL), and neural network (NN). In the proposed model, GA is primarily concerned with optimizing parameters required in the fuzzy neural network; FL with imprecision and approximate reasoning; and NN with learning and curve fitting. This research then integrates the EFNIM with an object-oriented computer technique to develop an EFNIS. Finally, the potential to apply the proposed system to practical geotechnical decision making is validated using two real problems, namely estimating slurry wall duration and selecting retaining wall construction methods.     

Fuzzy Neural Model for Flatness Pattern Recognition

For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient, practical, and novel method for flatness pattern recognition.     

神经网络自适应控制在冷轧机AGC系统中的应用

常规PID控制器是在冷轧机厚度控制中应用广泛的一种方法,但当AGC（厚度自动控制）系统特性或运行条件发生变化时,需要重新整定控制器参数,才能保证系统正常运行,使系统处于最佳工作状态。针对冷轧机AGC系统中的滞后环节,运用神经网络的自学习能力在线调整积分控制器参数值,该神经网络的权值与积分参数值相对应,可根据被控系统的动态特性调整积分参数,提高了调节器的自适应能力。     

基于模糊神经解耦控制的板型板厚控制系统仿真

面对板型板厚控制这一复杂、多变量耦合的非线性系统,本文提出了一种两级串联结构的模糊神经网络解耦控制策略,前级为自调整模糊控制器,后级为基于动态耦合特性的自适应神经网络解耦控制器,并从理论上证明了学习算法的收敛性。实现了无模型板型板厚综合控制。仿真结果表明,该控制系统收敛性好、抗干扰性强,取得令人满意的板型板厚控制精度。     

模糊神经网络控制在矿渣粉生产线中的应用

针对磨机出人口温度控制过程非线性、大滞后等特点以及模糊控制理论,利用VB6．0语言编程进行硬件描述,通过模糊自整定参数的方式来整定PID控制器的3个参数,利用PID控制器进行控制输出,设计出了多通道模糊PID温度控制器。应用表明,该模糊神经网络具有良好的自学习功能,磨机出入口温度误差被控制在±5℃之内,控制效果良好。     

An adaptive neuro-fuzzy inference system-based modelling to predict mechanical properties of hot-rolled TRIP steel

A model based on adaptive neural network formalism coupled with fuzzy inference system has been developed to predict mechanical properties of hot-rolled TRIP steel. The developed model incorporates a wide range of data containing chemical compositions, thermo-mechanical processing parameters and mechanical properties of hot-rolled TRIP steel. A compact set of process variables has been selected as the model inputs for predicting tensile strength, yield strength, elongation and retained austenite under a given operating condition. The model predictions show that carbon, silicon and manganese content have a significant effect on the retained austenite which increases with the increased amount of these elements. The microalloying elements such as niobium and molybdenum have a little effect on the volume fraction of retained austenite. The present model provides a predictive platform for possible application of these artificial intelligence-based tools for automation, real-time process control and operator guidance in plant operation.     

基于模糊神经网络的PID钢丝电接触运动加热温度控制

对于非线性、时变性的工业对象,采用模糊神经网络整定PID参数,提高了传统PID控制的自适应能力。仿真结果及其应用表明,其控制性能优于一般PID的控制性能。