scholarly journals Fuzzy Control Stability Analysis Using a Generalized Fuzzy Petri Net Model

1999 ◽  
Vol 3 (2) ◽  
pp. 99-105 ◽  
Author(s):  
Takeshi Furuhashi ◽  
◽  
Hidehiro Yamamoto ◽  
James F. Peters ◽  
Witold Pedrycz ◽  
...  
Keyword(s):  
Neural Network ◽  
Stability Analysis ◽  
Fuzzy Control ◽  
Petri Net ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Network Representation ◽  
Nonlinear Input ◽  
Fuzzy Petri Net ◽  
Control Stability

Fuzzy inference is used to describe nonlinear input- output relationships using fuzzy if-then rules. Continuous values of input and output are converted into granules by fuzzy sets, and each granule is labeled with a symbol. Fuzzy inference has a multigranular architecture consisting of continuous values and symbols that has worked well incorporating expert knowledge into fuzzy control. An important issue in fuzzy control is guaranteeing fuzzy control stability. We applied Petri nets to fuzzy control stability analysis and derived a theory on asymptotic stability for symbolic representation of control. We present new bridging between symbolic stability analysis and actual control behavior numerically. We use a generalized fuzzy Petri net model and its neural network representation. Conditions for validity of granularized control stability analysis are derived from the movement of tokens in neural network representation. Simulations are used to study derived conditions.

A New Fuzzy Inference Method for Symbolic Stability Analysis of Multigranular Intelligent Control System

2000 ◽  
Vol 12 (6) ◽  
pp. 664-674
Author(s):  
Hidehiro Yamamoto ◽  
◽  
Takeshi Furuhashi
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Inference ◽  
Continuous System ◽  
Asymptotically Stable ◽  
Membership Functions ◽  
Symbolic System ◽  
Inference Method

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

Research on Fault Diagnosis Method of the Aircraft Flap System Based on Weighted Fuzzy Petri Net

2012 ◽  
Vol 166-169 ◽  
pp. 1465-1470
Author(s):  
Qing Hong Xu ◽  
Cai Jing ◽  
Yi Ming Xu
Keyword(s):  
Fault Diagnosis ◽  
Complex Systems ◽  
Petri Net ◽  
Fuzzy Inference ◽  
Fault Probability ◽  
Formal Process ◽  
Matrix Operations ◽  
Fuzzy Petri Net ◽  
Diagnosis Method ◽  
Reasoning Algorithm

Accordingly to the problem that during the fault diagnosis of the flap system, the existing experience and knowledge are always uncertainty, inconsistency, incompleteness, what’s more, the different fault cause has different effect on the fault. So the Weighted Fuzzy Petri Net is adopted, and fuzzy Petri net and matrix operations are combined, then the formal process of fuzzy inference reasoning algorithm is researched in order to obtain the fault probability of each event in the fault tree of flap system. The presented method effectively makes up the deficiency of the traditional FTA and FMEA, and provides a new fault diagnosis method for complex systems, so the fault diagnosis method based on Weighted Fuzzy Petri Net has a great practical value.

The Fault Diagnosis Model of FMS Workflow Based on Adaptive Weighted Fuzzy Petri Net

2012 ◽  
Vol 605-607 ◽  
pp. 837-843 ◽  
Author(s):  
Hai Lan Pan ◽  
Wen Rong Jiang ◽  
Hai Hui He
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Petri Net ◽  
Specific Process ◽  
Complex Relation ◽  
Fuzzy Petri Net ◽  
Diagnosis Model ◽  
Adaptive Ability

This paper proposes a method for using neural network and weighted fuzzy Petri net to diagnose fault. Aiming at the traditional Petri net can not precisely predict the complex relation of the default phenomenon and the cause, neural network, fuzzy logic and the traditional Petri net are combined, and a constructing method for adaptive weighted fuzzy Petri net model is proposed. Based on this, an improved BP algorism is introduced to train the weight of the model, and the specific process for using the model to diagnose the fault is given. Finally, the model was applied to the instance of FMS, and the model was proved to have the advantages of Petri net and neural network and have reasoning and adaptive ability.

Novel intelligent adjustment height method of Shearer drum based on adaptive fuzzy reasoning Petri net

2021 ◽  
pp. 1-15
Author(s):  
Weibing Wang ◽  
Shenquan Wang ◽  
Shuanfeng Zhao ◽  
Zhengxiong Lu ◽  
Haitao He
Keyword(s):  
Petri Net ◽  
Fuzzy Inference ◽  
Regression Tree ◽  
Fuzzy Reasoning ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Adaptive Fuzzy ◽  
Fuzzy Petri Net ◽  
Non Linear ◽  
Height Model

The complexity of the coalface environment determines the non-linear and fuzzy characteristics of the drum adjustment height. To overcome this challenge, this study proposes an adaptive fuzzy reasoning Petri net (AFRPN) model based on fuzzy reasoning and fuzzy Petri net (FPN) and then applies it to the intelligent adjustment height of the shearer drum. This study constructs adaptive and reasoning algorithms. The former was used to optimize the AFRPN parameters, and the latter made the AFRPN model run. AFRPN could represent rules that had non-linear and attribute mapping relationships and could adjust the parameters adaptively to improve the accuracy of the output. Subsequently, the drum adjustment height model was established and compared to three models neural network (NN), classification and regression tree(CART) and gradient boosting decision tree (GBDT). The experimental results showed that this method is superior to other drum adjustment height methods and that AFRPN can achieve intelligent adjustment of the shearer drum height by constructing fuzzy inference rules.

Sign in / Sign up

Export Citation Format

Share Document