MCFC型燃料电池的神经网络建模与基于FGA的模糊控制研究

为了提高燃料电池的发电性能,熔融碳酸盐燃料电池(MCFC)堆的运行温度应该控制在一个合适的范围内。本文首先利用RBF神经网络辨识复杂非线性系统的能力,基于实验的输入输出数据,建立起MCFC电堆的神经网络温度模型;然后设计了MCFC电堆工作温度的一个基于模糊遗传算法的在线模糊控制器,用模糊遗传算法同时优化模糊控制器的参数及规则。最后用神经网络的辨识模型代替实际的电堆进行控制仿真,仿真结果证明建模是有效的,所设计的模糊控制器具有较好的性能。     

基于自适应模糊神经网络的DMFC温度建模和控制

针对DMFC电堆的实时控制要求，应用自适应模糊神经网络技术对DMFC电堆的工作温度进行辨识建模和控制。在温度控制过程中，将训练好的网络模型作为DMFC电堆控制系统的参考模型，并对控制模型的参数进行在线自适应调整。仿真结果表明所设计的自适应模糊神经控制器性能优越。     

汽车悬架磁流变减振器模型分析及半主动控制策略研究

用多项式模型作为磁流变减振器的力学模型,研究了传统"开-关"型半主动控制的阻尼力跳跃对汽车簧载质量振动加速度产生的不利影响,针对1/2汽车模型对具有磁流变减振器的汽车悬架简单模糊和自适应模糊神经半主动控制策略进行了数值仿真,基于数值仿真结果分析了各控制策略的效果.     

基于模糊神经网络的决策支持系统的应用研究

论述了模糊神经网络的概念及特点，在此基础上提出了基于模糊神经网络的决策支持模型，还介绍了其运行计算的过程与方法，并通过农机选型配套的决策支持实例验证模型，说明模型对复杂的决策问题具有简单方便及快速准确做出反应的优点。     

基于模糊神经网络的公差-成本模型研究

研究了面向制造环境的公差-成本模型。对制造环境中有关的加工因素进行分类及模糊化处理,构造了加工因素的成本模糊影响系数,并以此系数和零件公差作为输入,建立了基于模糊神经网络的公差-成本模型。此模型在表征加工成本、公差关系上的精度较高,更适应面向制造公差设计的要求。     

Fuzzy adaptive networks in machining process modelling: dimensional error prediction for turning operations

Due to the complexity of machine tool structure and the cutting process, the dynamics of machining processes are still not completely understood. This is especially true for high-speed machining processes. To model and control these complex processes, new approaches, which can represent complex phenomenon combined with learning ability, are needed. The combined neural-fuzzy approach appears ideally suited for this purpose. To illustrate the approach, the recently developed fuzzy adaptive networks are used to model dimensional error in turning operations. The fuzzy adaptive network has both the learning ability of a neural network and the linguistic representation of a complex, not well understood or vague phenomenon. An approximate model representing the influences of machining parameters on dimensional error is first established. This model is then improved by learning with the given training data. The improved models are verified by the use of test data, which are obtained by the use of actual experiments.     

Prediction and optimization of a ceramic casting process using a hierarchical hybrid system of neural networks and fuzzy logic

This paper is a case study that describes a hybrid system integrating fuzzy logic, neural networks and algorithmic optimization for use in the ceramics industry. A prediction module estimates two quality metrics of slip-cast pieces through the simultaneous execution of two neural networks. A process improvement algorithm optimizes controllable process settings using the neural network prediction module in the objective function. An expert system module contains a hierarchy of two fuzzy logic rule bases. The rule bases prescribe processing times customized to individual production lines given ambient conditions, mold characteristics and the neural network predictions. This paper demonstrates the applicability of newer computational techniques to a very traditional manufacturing process and the system has been implemented at a major US plant.     

Tools for intelligent control: fuzzy controllers,neural networks and genetic algorithms

The goal of this expository paper is to bring forth the basic current elements of soft computing (fuzzy logic, neural networks, genetic algorithms and genetic programming) and the current applications in intelligent control. Fuzzy sets and fuzzy logic and their applications to control systems have been documented. Other elements of soft computing, such as neural networks and genetic algorithms, are also treated for the novice reader. Each topic will have a number of relevant references of as many key contributors as possible.     

基于模糊神经网络的液体火箭发动机振动检测

液体火箭发动机振动检测涉及部件振动数据的收集、振动特征的抽取与度量以及度量结果的决策。基于模糊神经网络提出了一种发动机振动故障检测的基本系统。这种技术的吸引力在于：神经网络采用可变模糊集代表发动机工作模式，自然地提供了反映故障程度的有用信息；神经网络的离线学习算法可以从训练样本中提取振动知识；神经网络的监测算法不仅能正确预报故障，同时也能对新的振动信息进行在线学习。实验研究结果表明：模糊神经网络可以成功地用于泵压式液体火箭发动机热试车的振动故障检测。     

Automated Defect Recognition and Identification in Digital Radiography

Weld quality assurance is important for the safe exploitation of many products and constructions. This paper summarizes work on an advanced system for automated radiogram analysis. The most important parts of the process of radiogram analysis such as segmentation, thresholding and defect recognition and classification are discussed. A complex classifier composed of artificial neural networks and a fuzzy logic system is proposed and discussed in detail. The proposed classifier shows better performance and flexibility than the normal neural networks classifiers.     

An intelligent control for linear ultrasonic motor using interval type-2 fuzzy neural network

An interval type-2 fuzzy neural network (IT2FNN) is developed for the position control of a thetas-axis motion-control stage using a linear ultrasonic motor to confront the uncertainties of the motion-control stage. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part and a three-layer interval neural network as the consequent part. A general T2FNN is computationally intensive due to the complexity of reducing type 2 to type 1. Therefore an IT2FNN is adopted to simplify the computational process. Moreover, the developed IT2FNN combines the merits of an interval type-2 fuzzy logic system and a neural network. Furthermore, the parameter-learning of the IT2FNN, which is based on the supervised gradient decent method using a delta adaptation law, is performed on line. Experimental results show that the dynamic behaviours of the proposed IT2FNN control system are more effective and robust with regard to uncertainties than the type-1 FNN control system.     

Building an active material requirements planning system

A material requirements planning (MRP) system is usually implemented with several constraints, such as replenishment of inventories on a period-by-period bases, which obstruct its dynamic performance. This research proposes an active, bucketless, and real-time MRP system. The active MRP system utilizes hybrid architecture that includes an object-oriented database, fuzzy logic controllers, and neural networks. The object-oriented database, which maintains static data relationships, provides superior capabilities in reusability, complex structure operations, and potential integration. The complementary combination of fuzzy logic controllers and neural networks provides a model-free, human-like decision system. Adding triggers and assertions forms an active MRP model.     

Application of Hybrid Learning Neural Fuzzy Systems in Reliability Prediction

This study presents a hybrid learning neural fuzzy system for accurately predicting system reliability. Neural fuzzy system learning with and without supervision has been successfully applied in control systems and pattern recognition problems. This investigation modifies the hybrid learning fuzzy systems to accept time series data and therefore examines the feasibility of reliability prediction. Two neural network systems are developed for solving different reliability prediction problems. Additionally, a scaled conjugate gradient learning method is applied to accelerate the training in the supervised learning phase. Several existing approaches, including feed‐forward multilayer perceptron (MLP) networks, radial basis function (RBF) neural networks and Box–Jenkins autoregressive integrated moving average (ARIMA) models, are used to compare the performance of the reliability prediction. The numerical results demonstrate that the neural fuzzy systems have higher prediction accuracy than the other methods. Copyright © 2005 John Wiley & Sons, Ltd.     

一种模糊神经网络改进算法及其在振动强度识别中的应用

对传统模糊自适应Hamming网络算法进行了改进,通过引入新的模糊算法对传统算法中的类别选择函数进行改进,以提高网络的正确识别率,为了实现模式识别中网络的有序输出,对输出层获胜神经元的选取方法也进行了相应的改进。改进后的算法用于空调压缩机壳体振动强度的识别,利用模糊自适应Hamming神经网络综合考虑各测点振动、噪声信号所包含的信息,对壳体振动强度区域实现自动划分。通过改进师前、后两种算法在不同警戒参数下的试验结果发现,采用改进后的算法大大提高了网络的正确识别率,并能够很好地实现网络的有序输出。     

基于模糊RBF神经网络的水下机械臂控制研究

针对水下复杂工作环境下机械臂控制性能易受影响,而传统控制方法效果不佳的问题,提出了一种基于模糊RBF（radial basis function,径向基函数）神经网络的智能控制器,用于精确、稳定地控制水下机械臂。考虑到在水扰动环境下,机械臂通常受到附加质量力、水阻力和浮力的影响,运用拉格朗日法和Morison方程,建立包含水动力项的二杆机械臂动力学模型,通过模糊RBF神经网络对水下机械臂动力学方程中的水动力不确定项进行总体识别并拟合,利用模糊系统启发式搜索和RBF神经网络推理速度较快的优点,使水下机械臂系统具有较高的控制精度和较强的自适应性。考虑到水动力项,采用Lyapunov稳定性理论验证了水下机械臂系统的稳定性。最后利用MATLAB对二杆机械臂进行轨迹跟踪控制仿真实验,并对比模糊RBF神经网络与常规RBF神经网络识别方法和传统模糊控制方法的控制效果。仿真结果表明：与常规RBF神经网络识别方法相比,模糊RBF神经网络控制下二杆机械臂关节1的响应时间缩短了91%,相对误差减小了88%,关节2的响应时间缩短了92%,相对误差降低了77%;与传统模糊控制方法相比,关节1的相对误差减小了65%,关节2的相对误差减小了10%。研究结果表明模糊RBF神经网络的控制效果优于常规RBF神经网络识别方法和传统模糊控制方法,可为水下机械臂的控制提供一种精度较高、较有效的方法。     

基于遗传算法的模糊神经控制及其应用

将遗传算法和模糊神经网络相结合，提出了一类智能控制方案，仿真系统和实际温控表明，这类智能控制器可改善具有时变、非线性及大纯滞后系统的控制品质，其性能优于一般模糊控制。     

陶瓷浆料3D打印机挤压力模糊神经网络PID稳定控制研究

针对在微流挤出陶瓷浆料3D打印机作业过程中挤压力稳定控制的需求,根据打印机挤压力控制系统非线性、时变性的特点,总结了现有挤压力稳定控制策略的优缺点,并在模糊PID (proportion-integral-derivative,比例-积分-微分)控制器中嵌入神经网络结构,提出了挤压力模糊神经网络PID稳定控制策略。该策略基于六层模糊神经网络,以挤压力偏差值e和偏差值变化率ec为输入,PID控制器控制参数为输出,完成正向模糊控制过程,并基于神经网络的自学习优势实现反向传播及在线更新神经网络权值,以实现打印过程中挤压力的精准自适应调节。挤压力控制Simulink仿真、挤压力控制实验及坯体打印实验表明：相较于传统PID控制策略,采用模糊神经网络PID控制策略可使超调量减小20.9%,挤压力提前90 s达到稳定状态,压力峰值减小12 N,压力谷值增大18 N;相较于采用模糊PID控制策略,超调量减小1.73%,挤压力提前56 s达到稳定状态,压力峰值减小4 N,压力谷值增大8 N;模糊神经网络PID控制策略具有一定的优越性,可使打印过程中挤压力的控制精度更高,稳定速度更快,超调量更小,所打印坯...     

模糊神经网络在精密三轴转台机械台体故障诊断中的应用研究

针对三轴转台机械台体故障,提出了一种基于模糊神经网络的故障诊断方法,给出了模糊神经网络的结构和学习算法,并阐述了模糊故障诊断原理和故障判别方法将振动信号和电流噪声信号结构用于机械台体的故障诊断,测试结果表明,该方法是有效的。     

Application of fuzzy neural network to laser bending process of sheet metal

Abstract

The optimisation and selection of process plans is very important for laser bending of sheet metal to achieve the anticipated bending deformation. In this paper, an adaptive fuzzy neural network has been proposed to predict the bending deformation. This network integrates the learning power of neural networks with fuzzy inference systems. During the establishing process of the energy density (composed of three process parameters: laser power, scanning velocity, and spot diameter), width, thickness of sheet, and scanning path curvature were taken as four input variables of the network. The gradient descent learning algorithm was applied to optimally adjust the weight coefficients of the neural network and the parameters of the fuzzy membership functions. Then, the trained network was used to predict the laser bending deformation. Good correlation was found between the predictive and experimental results.     

递归神经网络的模糊随机学习算法

提出了一种新的基于模糊逻辑的Alopex学习算法（FLA）。FLA算法利用模糊逻辑推理实时获得适应于学习过程的适当的算法修正值，克服了Alopex算法中修正值固定不变的缺点，使得随机学习过程在速度、精度和稳定性之间获得平衡。将该算法应用于神经网络的训练，可以无需神经网络的梯度信息和结构信息，因此可以用于具有各种结构特性的递归神经网络对动态系统的学习过程。实验结果表明了FLA算法的有效性。