自学习模糊逻辑推理网络及模糊控制器的构成1)

提出了一种自学习模糊逻辑推理网络和自学习模糊控制器的构成方法.这种方法是把RCE(Restricted Coulomb Energy)模型进行扩展,使其能够进行模糊逻辑推理,并用于构成基于RCE模型的自学习模糊控制器RLFC(RCE-based Learning Fuzzy Controller).这种方法有以下特点a)学习速度高,追加学习容易;b)网络的信息处理工作单元的个数由自学习决定,通用性好;c)不存在局部极小点问题.自学习模糊控制器RLFC可以直接把熟练者的操作知识转换成模糊控制规则,自动构成模糊控制器.数值仿真实验表明其效果良好.     

一种新的分布式神经模糊控制器

提出了一种分布式神经模糊网络和自学习模糊控制器的构成方法。它是CMAC模型的一种扩展，使其能进行模糊推理和构成自学习的模糊控制器。该方法除具有CMAC优点外，还具有以下特点：输入数据通过模糊划分和隶属函数后自动编码，对精度没有限制；从现场数据直接获取控制规则，即使对未训练的数据，也能结合插值和泛化两种能力，推理给出合适的输出。学习实例证明了方法的有效性。     

模糊CMAC的柔性空间机器人轨迹跟踪自学习控制

针对不确定自由漂浮柔性空间机器人系统,采用模糊CMAC神经网络自学习控制策略来解决轨迹跟踪控制问题.首先建立漂浮基空间机器人的动力学方程,然后利用具有快速学习能力的模糊CMAC神经网络来逼近非线性柔性臂的逆动力学模型.网络参数采用改进的有监督的Hebb学习规则进行自适应在线调整,并通过关联搜索进行自学习和自组织,其误差代价函数由PID控制器提供.仿真结果表明,这种模糊CMAC逆模PID控制器能够达到较高的控制精度,具有一定的工程应用价值.     

一种新的分布式神经模糊控制器

本文提出了一种分布式神经模糊网络和自学习模糊控制器的构成方法，它是CMAC模型的一种扩展，使其能进行模糊推理和构成自习的模糊控制器，该方法除具有CMAC优点外，还具有以下特点；（1）输入数据通过 模糊划分和隶属函数后自动编码，对精度没有限制；（2）从现场数据直接获取控制规则，即使对未训练的数据，也能结合插值和泛化两种能力，推理出合适的输出。本文还对DNFN的逼近能力进行了讨论，学习实例证明了方法的有效性。     

一种基于RBF网络提取模糊规则的算法实现

径向基函数网络和模糊推理系统在一些柔和的情况下具有等价的功能,因此可以利用神经网络的学习算法来调节模糊系统的参数,学习后的模糊系统具有自学习和自组织性,但是削弱了模糊系统的可解释性。将模糊逻辑推理与神经网络控制技术相结合,分析了一种改进的径向基函数(RBF)神经网络结构,这种模糊神经网络结构能够有效地表达模糊系统可解释性这一突出特点,也使模糊系统具有了较好的自学习和自组织能力、通过VC 实现了基于这种RBF网络结构提取模糊规则的算法,并进行了仿真实验,仿真结果表明该算法是比较有效的。     

基于模糊神经网络的高校教师绩效评价方法

基于神经网络来构造模糊系统,综合模糊逻辑推理的结构性知识表达能力和神经网络的自学习能力,建立了高校教师绩效评价的模糊神经网络模型.通过建立评价指标建立等级模糊级,利用神经网络的学习能力,通过网络的训练样本,确定评价中的参数.克服了由专家确定参数的主观性,达到对教师绩效评价的客观性,科学性.     

无刷直流电机的模糊神经网络控制设计

在无刷直流电机(BLDCM)的控制上,传统PID等控制方法存在或多或少的不足.在模糊PID控制的基础上提出了一种模糊神经网络PI控制器的设计方法.该方法结合了模糊逻辑与神经网络,使得模糊控制器模拟了人的控制功能,不仅对环境变化有较强的适应能力,还拥有自学习能力.相比模糊PID控制,其具有计算量小、稳定性强等特点.对BLDCM进行建模与分析;在BLDCM数学模型的基础上,分别设计模糊PID控制器和模糊神经网络PI控制器;对设计的控制器进行仿真验证并分析.实验结果表明,模糊神经网络PI控制具有跟踪性能好、超调小、响应快、脉动小等优点,其动静态特性均优于模糊PID控制.     

一种模糊CMAC神经网络

提出了一种模糊CMAC(小脑模型关节控制器)神经网络,它由输入层、模糊化层、模糊相 联层、模糊后相联层与输出层等5层节点组成,具有与CMAC相似的单层连接权,可通过BP 算法学习推论参数或模糊规则.给出了网络的连接结构与学习算法,并将其应用于函数逼近 问题中仿真结果验证了该方法较之CMAC的优越性.     

高超声速飞行器的模糊CMAC神经网络控制器设计

由于采用机体一体化设计,吸气式高超声速飞行器的气动特性难以准确获知,建立的数学模型是极不准确的;设计了一种模糊CMAC神经网络(FCMAC)控制器及其学习算法,在CMAC神经网络控制器中结合模糊逻辑理论,使得CMAC控制器具有自学习能力;仿真用高超声速飞行器的纵向模型对该控制器进行了验证,证明该控制方法能够有效地跟踪飞行器的高度和速度指令。     

三维模糊控制器的结构研究

基于Zadeh的模糊逻辑推理和语言控制策略,进行了三维模糊控制器的结构研 究.证明了具有线性控制规则的三维模糊控制器可等同于一个全局多层次线性关系式和一 个局部非线性PID型控制器,由此剖析了模糊控制器的推理机制和其非线性本质.     

A STABLE SELF-LEARNING OPTIMAL FUZZY CONTROL SYSTEM

The issue of developing a stable self-learning optimal fuzzy control system is discussed in this paper. Three chief objectives are accomplished: 1) To develop a self-learning fuzzy controller based on genetic algorithms. In the proposed methodology, the concept of a fuzzy sliding mode is introduced to specify the system response, to simplify the fuzzy rules and to shorten the chromosome length. The speed of fuzzy inference and genetic evolution of the proposed strategy, consequently, is higher than that of the conventional fuzzy logic control. 2) To guarantee the stability of the learning control system. A hitting controller is designed to achieve this requirement. It works as an auxiliary controller and supports the self-learning fuzzy controller in the following manner. When the learning controller works well enough to allow the system state to lie inside a pre-defined boundary layer, the hitting controller is disabled. On the other hand, if the system tends to diverge, the hitting controller is turned on to pull the state back. The system is therefore stable in the sense that the state is bounded by the boundary layer. 3) To explore a fuzzy rule-base that can minimize a standard quadratic cost function. Based on the fuzzy sliding regime, the problem of minimizing the quadratic cost function can be transformed into that of deriving an optimal sliding surface. Consequently, the proposed learning scheme is directly applied to extract the optimal fuzzy rulebase. That is, the faster the hitting time a controller has and the shorter the distance from the sliding surface the higher fitness it possesses. The superiority of the proposed approach is verified through simulations.     

A FUZZY SELF-LEARNING POSITION CONTROL OF HYDRAULIC DRIVE

Fluid power actuators are characterized by their high-power density and excellent dynamic response. The hydraulic actuator, in particular, is capable of very high output power levels combined with very compact drive unit dimensions. It is ideally suited to many high dynamic applications in modern machines and mechanical systems. However, the disadvantages of hydraulic systems such as nonlinear dynamic behavior due to friction, fluid compressibility, etc. need to be overcome. This is successfully obtainable only by implementation of modern digital control systems designed on the basis of modern control theory. In other words, the modern electro-hydraulic drive has to possess more and more intelligence. The objective of many researchers is, therefore, to develop an algorithm that would be able to control the drive without any a priori knowledge of geometrical, operating, or any other parameters of the system. An attempt to achieve the above objective is represented in this article. The proposed new control strategy uses a combination of fuzzy logic and conventional control approaches. An adaptability is obtained by a fuzzy logic controller designed as a self-learning fuzzy system, based on a reinforcement learning method. The reference tracking behavior is improved by inverse model digital force-filter, while the accurate final positioning is achieved by a switching integrator. The results of experimental investigations are also shown.     

基于Takagi—Sugeno的再励学习模糊神经网络控制

提出一种模糊神经网络的自适应控制方案。针对连续空间的复杂学习任务，提出了一种竞争式Takagi—Sugeno模糊再励学习网络，该网络结构集成了Takagi-Sugeno模糊推理系统和基于动作的评价值函数的再励学习方法。相应地，提出了一种优化学习算法，其把竞争式Takagi-Sugeno模糊再励学习网络训练成为一种所谓的Takagi-Sugeno模糊变结构控制器。以一级倒立摆控制系统为例．仿真研究表明所提出的学习算法在性能上优于其它的再励学习算法。     

A NEW DESIGN APPROACH FOR FUZZY‐LEARNING FUZZY CONTROLLERS

In this paper, a new approach to designing fuzzy‐learning fuzzy controllers for a system plant without an exact mathematical model is presented. The cost function is defined as the square of the sliding function to alleviate the difficulty of overshoot when on‐line learning is conducted. The learning mechanism of a fuzzy controller is constructed so as to minimize the cost function with a set of linguistic rules. Moreover, to reduce the complexity of the fuzzy‐learning fuzzy controller, the fuzzy mechanism used for learning and the fuzzy mechanism contained in the fuzzy controller are designed so as to have the identical structures. Finally, simulations are included to show the effectiveness of the fuzzy‐learning fuzzy controllers.     

基于Takagi-Sugeno的再励学习模糊神经网络控制

提出一种模糊神经网络的自适应控制方案。针对连续空间的复杂学习任务,提出了一种竞争式Takagi-Sugeno模糊再励学习网络,该网络结构集成了Takagi-Sugeno模糊推理系统和基于动作的评价值函数的再励学习方法。相应地,提出了一种优化学习算法,其把竞争式Takagi-Sugeno模糊再励学习网络训练成为一种所谓的Takagi-Sugeno模糊变结构控制器。以一级倒立摆控制系统为例,仿真研究表明所提出的学习算法在性能上优于其它的再励学习算法。     

A SELF-IMPROVING FUZZY CEREBELLAR MODEL ARTICULATION CONTROLLER WITH STOCHASTIC ACTION GENERATION

A modified fuzzy cerebellar model articulation controller (FCMAC) with reinforcement learning capability is introduced in this article. This model utilizes the likelihood scheme to predict the evaluation of successive actions. Based on an approximating evaluation model, the proper output (action) is always selected. The structure of the proposed FCMAC consists of three parts: a fuzzy quantizer, which is used to represent the associative mapping function from the receptive field to the actual memory; an action evaluation module, which models and produces the expected evaluation signal and an action selection unit that generates an action with the expectation of better performance using a probability distribution function that estimates an optimal action selection policy. To demonstrate its excellent performance, the proposed self-improving model is implemented as a neural network controller for the swing control of a pendulum system. The results from both the simulation and experiment demonstrates better performance and applicability of the proposed learning model.     

一种模糊规则动态调整BP算法中参数的方法

文中首先对标准的ＢＰ算法进行了分析。然后在此基础上提出了通过模糊规则推理动态调整学习率和动量因子的改进的方法，并通过模糊推理系统实现了ＢＰ算法的模糊控制。最后通过实例将该算法与标准ＢＰ算法和Ｖｏｇｌ改进的算法进行了比较，实验结果表明通过模糊推理来改善神经网络的ＢＰ算法性能是一种很有前途的方法。     

竞争式Takagi-Sugeno模糊再励学习

针对连续空间的复杂学习任务,提出了一种竞争式Takagi-Sugeno模糊再励学习网络 (CTSFRLN),该网络结构集成了Takagi-Sugeno模糊推理系统和基于动作的评价值函数的再 励学习方法.文中相应提出了两种学习算法,即竞争式Takagi-Sugeno模糊Q-学习算法和竞争 式Takagi-Sugeno模糊优胜学习算法,其把CTSFRLN训练成为一种所谓的Takagi-Sugeno模 糊变结构控制器.以二级倒立摆控制系统为例,仿真研究表明所提出的学习算法在性能上优于 其它的再励学习算法.     

炼油厂常压塔侧线质量实时监测模糊专家系统

通过对炼油厂分馏塔侧线质量进行机理分析，并根据实际流程利用ＡＳＰＥＮ对过程进行模拟得到各控制变量与侧线质量的对应结果，利用数据统计方法，得到带有可信度ＣＦ量度的规则。利用正向推理，构置了一个基于规则在线监测炼油厂分馏塔侧线质量指标的模糊专家系统ＳＱＰＥＳ，并利用工厂采集的实时数据对所建立的专家系统进行了验证。结果表明，这种规则获取手段对建立炼油厂分馏塔质量指标监测的专家系统是适用的，它的建立，即可     

COMPARATIVE STUDY OF FUZZY CONTROL,PID CONTROL,AND ADVANCED FUZZY CONTROL FOR SIMULATING A NUCLEAR REACTOR OPERATION

Based on the background of fuzzy control applications to the first nuclear reactor in Belgium (BR1) at the Belgian Nuclear Research Centre (SCK˙CEN), we have made a real fuzzy logic control demo model. The demo model is suitable for us to test and compare some new algorithms of fuzzy control and intelligent systems, which is advantageous because it is always difficult and time-consuming, due to safety aspects, to do all experiments in a real nuclear environment. In this paper, we first report briefly on the construction of the demo model, and then introduce the results of a fuzzy control, a proportional-integral-derivative (PID) control and an advanced fuzzy control, in which the advanced fuzzy control is a fuzzy control with an adaptive function that can self-regulate the fuzzy control rules. Afterwards, we present a comparative study of those three methods. The results have shown that fuzzy control has more advantages in terms of flexibility, robustness, and easily updated facilities with respect to the PID control of the demo model, but that PID control has much higher regulation resolution due to its integration term. The adaptive fuzzy control can dynamically adjust the rule base, therefore it is more robust and suitable to those very uncertain occasions.