基于专家系统和Petri网的网络可靠性评估方法

针对网络系统可靠性建模难的问题,结合专家系统和Petri网理论各自的特点,提出了一种基于知识库系统的Petri网可靠性评估方法。首先引入模糊神经Petri网的定义和适用于网络系统可靠性建模的引发规则,然后在此基础上提出一种学习算法,其既可以表示和处理模糊产生式规则的知识库系统,又具有学习能力。最后以一个无向网络的可靠性评估为例,并通过定性分析和定量计算,验证了算法的合理性和有效性。     

Optimizing the functioning of systems by means of neural network models of Petri nets

The paper describes a method for the optimization of systems represented by models based on Petri nets. For solving optimization problems, it is proposed to use a Petri net model implemented by an artificial neural network. The method is exemplified by its application to control an imitation of a Petri net.     

基于Petri网的非线性多变量系统解耦控制

广义自控网系统是一类弧权值受库所控制的高级Petri网,能够简单有效地建模PID控制规律.借鉴单神经元PID控制原理,在广义自控网系统的基础上加入神经元网络的学习规则,设计基于广义自控网系统的PID控制器,并用于非线性多变量系统解耦控制.所提方法充分利用了自控网系统的特点,所设计的控制器模型能实现系统控制与参数学习的统一.结合双容水箱控制系统实例进行仿真分析,分析结果验证了所提方法的有效性.     

Reinforcement learning for high-level fuzzy Petri nets

The author has developed a reinforcement learning algorithm for the high-level fuzzy Petri net (HLFPN) models in order to perform structure and parameter learning simultaneously. In addition to the HLFPN itself, the difference and similarity among a variety of subclasses concerning Petri nets are also discussed. As compared with the fuzzy adaptive learning control network (FALCON), the HLFPN model preserves the advantages that: 1) it offers more flexible learning capability because it is able to model both IF-THEN and IF-THEN-ELSE rules; 2) it allows multiple heterogeneous outputs to be drawn if they exist; 3) it offers a more compact data structure for fuzzy production rules so as to save information storage; and 4) it is able to learn faster due to its structural reduction. Finally, main results are presented in the form of seven propositions and supported by some experiments.     

Neuro-fuzzy position control of demining tele-operation system based on RNN modeling

The paper considers the neuro-fuzzy position control of multi-finger robot hand in tele-operation system—an active master–slave hand system (MSHS) for demining. Recently, fuzzy control systems utilizing artificial intelligent techniques are also being actively investigated in robotic area. Neural network with their powerful learning capability are being sought as the basis for many adaptive control systems where on-line adaptation can be implemented. Fuzzy logic on the other hand has been proved to be rather popular in many control system applications providing a rule-base like structure. In this paper, the design and optimization process of fuzzy position controller is supported by learning techniques derived from neural network where a radial basis function (RBF) neural network is implemented to learn fuzzy rules and membership functions with predictor of recurrent neural network (RNN) model. The results of experiment show that based on the predictive capability of RNN model neuro-fuzzy controller with good adaptation and robustness capability can be designed.     

FMS工作流故障诊断自适应加权模糊Petri网模型

研究了使用人工神经网络和加权模糊Petri网对故障进行诊断的方法。针对传统Petri网难以精确地描述故障现象和故障原因之间的复杂关系,将人工神经网络、模糊逻辑和传统Petri网模型结合,定义了一种自适应的加权模糊Petri网模型以及模型的构造方法,在此基础上,提出了一种使用改进的BP算法对模型的权值进行训练的方法,并给出了采用构造的自适应模糊Petri网模型对故障进行诊断的具体步骤。最后对柔性制造系统(FMS)实例的故障进行诊断,验证了此自适应的加权模糊Petri网模型结合了Petri网和人工神经网络的优点,具有很强的故障推理能力以及自适应能力,能有效地对故障进行诊断。     

基于模糊神经Petri网的故障诊断模型

Petri网是对具有产生式规则的故障诊断系统的有力建模工具，但其缺乏较强的学习能力．本文以Petri网的基本定义为基础，结合模糊逻辑和Petri网模型，定义了模糊Petri网模型，在此基础上引入人工神经网络技术，给出了人工神经网络的模糊Petri网表示方法，并针对工程机械故障诊断异步、离散等特点，提出并建立了故障诊断的模糊神经Petri网模型及其改进模型．基于模糊神经Petri网的故障诊断系统结合了Petri网和人工神经网络的优点，经过自学习后同时具有很强的推理能力和自适应能力．     

Control of magnetic bearing systems via the Chebyshevpolynomial-based unified model (CPBUM) neural network

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.     

基于修剪策略的D-FNN直接逆控制算法研究

神经网络是模拟人脑结构,它具有大规模并行及分布式信息处理能力,但是不能处理和描述模糊信息.模糊系统具有推理过程容易理解,但它很难实现自适应学习的功能.如果结合神经网络与模糊系统,可以取长补短.基于此,本文提出了一种新型动态模糊神经网络（Dynamic fuzzy neural network,D-FNN）学习算法.因为它具有结构和参数同时调整且学习速度快等优点,所以既可以在模糊逻辑系统中包含低级的神经网络学习和计算功能,也可以为神经网络提供高级的类似人的思维和推理的模糊逻辑系统.此外,本文还开发了生物医学工程应用算法程序,针对药物注射系统的直接逆控制案例进行了仿真,结果表明:D-FNN具有实时学习和控制能力强、参数估计和结构辨识同时进行等优点.     

基于BP网络的模糊Petri网的学习能力

模糊Petri网(Fuzzy Petri Nets，FPN)是基于模糊产生式规则的知识库系统的良好建模工具，但自学习能力差是模糊系统本身的一个缺点．该文提出了适合模糊Petri网模型自学习的模糊推理算法和学习算法．在模糊推理算法中，通过对没有回路的FPN模型结构进行层次式划分以及建立变迁点燃和模糊推理的近似连续函数，从而把神经网络中的BP网络算法自然地引入到FPN模型中．在FPN模型上，用误差反传算法计算一阶梯度的方法对模糊产生式规则中的参数进行学习和训练．经过学习和训练的FPN具有很强的泛化能力和自适应功能．FPN模型经过训练得到的参数是有特定含义的，可以通过对这些参数的合法性分析，使得模糊产生式规则系统更加有效，也对知识库系统的建立、更新和维护有着重要的意义．