Diagnose Expert System of Engine Based on Fuzzy Neural Network

2012 ◽  
Vol 588-589 ◽  
pp. 1472-1475
Author(s):  
Miao Tian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural Networks ◽  
Learning Ability ◽  
Fuzzy Neural ◽  
Enhance Efficiency ◽  
Self Learning

Engine has a high chance of failure, it usually accounts for about 40% of vehicle failures. Study expert system of engine fault diagnosises that it can locate fault timely and accurately, and enhance efficiency. However, the traditional expert system has shortcomings so as inefficient inference and poor self-learning capability. The fuzzy logic and traditional neural networks are combined to form fuzzy neural networks, they are established a model of fuzzy neural network (FNN) of fault diagnosis, and that the model is applied to engine fault diagnosis, complementary advantages, to effectively enhance efficiency of inference and self-learning ability, its performance is higher than the traditional BP network.

scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

Dynamic Controller Based on Ying Learning Algorithm for Medical Temperature System

2011 ◽  
Vol 187 ◽  
pp. 371-376
Author(s):  
Ping Zhang ◽  
Xiao Hong Hao ◽  
Heng Jie Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
The Novel ◽  
Fuzzy Neural ◽  
Learning Set ◽  
And Training ◽  
Novel Algorithm

In order to avoid the over fitting and training and solve the knowledge extraction problem in fuzzy neural networks system. Ying Learning Dynamic Fuzzy Neural Network (YL-DFNN) algorithm is proposed. The Learning Set based on K-VNN is constituted from message. Then the framework of is designed and its stability is proved. Finally, Simulation indicates that the novel algorithm is fast, compact, and capable in generalization.

Fingerprint Classification Using Fuzzy-neural Network and Other Methods

2014 ◽  
Vol 3 (3) ◽  
pp. 129
Author(s):  
Idriss Tazight ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Network ◽  
Search Space ◽  
Fuzzy Neural Networks ◽  
Pattern Recognition Problem ◽  
Fingerprint Classification ◽  
Right Loop ◽  
Fuzzy Neural ◽  
Singular Characteristics

The fingerprints are unique to each individual; they can be used as a means to distinguish one individual from another.Therefore they are used to identify a person. Fingerprint Classification is done to associate a given fingerprint to one of the existing classes, such as left loop, right loop, arch, tented arch and whorl. Classifying fingerprint images is a very complex pattern recognition problem, due to properties of intra-class diversitiesand inter-class similarities. Its objective is to reduce the responsetime and reducing the search space in an automatic identificationsystem fingerprint (AIS), in classifying fingerprints. In these papers we present a system of fingerprint classificationbased on singular characteristics for extracting feature vectorsand neural networks and fuzzy neural networks, SVM and Knearest neighbour for classifying.

Fault Diagnosis Expert System for Special Electronic Equipment Based on the Fuzzy Neural Network

2012 ◽  
Vol 241-244 ◽  
pp. 401-404
Author(s):  
Xue Zhong Yin ◽  
Jie Gui Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fuzzy Neural Network ◽  
Electronic System ◽  
Electronic Equipment ◽  
Diagnostic Model ◽  
Model Based ◽  
Proposed Model ◽  
Fuzzy Neural

In order to improve the efficiency and reliability of fault diagnosis for the special electronic equipment, an intelligent fault diagnostic model based on Fuzzy Neural Network (FNN) is proposed. Firstly, the fault diagnosis model based on the FNN Expert System (ES) is built. Secondly, the fault diagnosis expert system of the special electronic equipment based on this model is introduced. Finally, experiments show that the proposed model is correct and the FD system is effective. Moreover, the given method provides a new way of fault diagnosis for other modern electronic system.

scholarly journals Block Hierarchical Fuzzy-Neural Networks and Their Application to a Mobile Robot Control

1995 ◽  
Vol 7 (1) ◽  
pp. 12-20
Author(s):  
Jun Tang ◽  
◽  
Keigo Watanabe ◽  
Masatoshi Nakamura ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural Networks ◽  
Small Scale ◽  
Circular Path ◽  
Single Output ◽  
Fuzzy Neural ◽  
Input Variables

If some fuzzy sets in a fuzzy-neural network are assigned to each scalar input data, then the number of intermediate unit functions grows exponentially as the number of input variables to the fuzzy reasoning increases. Therefore, it is very important for multi-input/multi-out-put systems to effectively construct a small-scale fuzzy neural network. In this paper, four types of block hierarchical fuzzy-gaussian neural networks (FGNNs) are proposed for a control system of a mobile robot with two independent driving wheels by applying two inputs and single-output FGNN block, or single-input and singleoutput FGNN block. Such a block hierarchical FGNN consists of three layers. In other words, the first input layer consists of two FGNN blocks that independently generate torques for controlling the velocity and azimuth of the mobile robot. The second hidden layer determines their distributions to the final layer by using fixed connection weights. The final output layer also consists of two FGNN bl ks that automatically determine the out put scalers for the actual left- and right-wheel driving torques. The effectiveness of the proposed method is illustrated through some simulations of a circular path tracking control.

Study of the Engineering Vehicles of Automatic Transmission Shift Schedule Based on Fuzzy Neural Network

2012 ◽  
Vol 591-593 ◽  
pp. 1720-1723 ◽  
Author(s):  
Yong Jing Huang ◽  
Jin Yao ◽  
Jia Hua Han ◽  
Di Wu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Automatic Transmission ◽  
Learning Ability ◽  
Shift Schedule ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Set Up ◽  
Takagi Sugeno ◽  
Self Learning

By combining the powerful self-learning ability of the neural network and the characteristic that the fuzzy control is designed based on the strategical rules of knowledge and language, this paper put forward the strategy of engineering vehicles' automatic transmission shift. According to a large number of experimental data, as well as the drivers' experience and the experts' profession, this paper put forward the strategy of engineering vehicles’ automatic transmission shift. The neural network model is set up based on Takagi-Sugeno and the factual cases are used to train and exam by MATLB, the simulation result showed that this method is feasible and meet the shift requirement, as it can accelerate effectively the establishment of the rules and reduce the set up time. The shift schedule can reflect precisely the actual out put target gear and meet the shift requirement.

scholarly journals Synthesis of an Expert System for Assessing the Security of Computer Networks Based on a Fuzzy Neural Network

2020 ◽  
Vol 9 (7) ◽  
pp. 935-939
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Computer Networks ◽  
Fuzzy Neural Network ◽  
Computer Systems ◽  
The United States ◽  
Fuzzy Neural Networks ◽  
Practical Significance ◽  
United States Department ◽  
Fuzzy Neural

The aim of the article is to substantiate the principles of synthesis of an expert system for assessing the security of computer networks based on a fuzzy neural network, and this is an urgent scientific and technical task. Requirements for the operative security assessment of computer networks for data protection are analyzed. It was shown that data security should be provided by the network administrator or persons who need to use special decision support systems in assessing the security of computer networks. To solve this problem, factors that characterize the security of electronic systems, including computer systems, have been identified; the use of fuzzy neural networks is proposed as a mathematical apparatus for constructing an expert system; a technique for the synthesis of a fuzzy neural network for assessing the security of computer networks has been developed; an appropriate fuzzy neural network has been created and tested for adequacy; the prospects of the proposed methodology for creating an expert system for assessing the security of computer systems have been established. The scientific and practical significance of developing such a system lies in the fact that a fuzzy neural network is configured on a specific object in order to quickly determine one of the seven levels of security of computer networks that are used in the United States Department of Defense.

Tool Condition Monitoring in Machining by Fuzzy Neural Networks

1996 ◽  
Vol 118 (4) ◽  
pp. 665-672 ◽  
Author(s):  
S. Li ◽  
M. A. Elbestawi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Condition Monitoring ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Fuzzy Neural Networks ◽  
Tool Condition Monitoring ◽  
Cutting Conditions ◽  
Tool Condition ◽  
Fuzzy Neural

The Multiple Principal Component (MPC) Fuzzy Neural Network for tool condition monitoring in machining under varying cutting conditions is proposed. This approach is based on three major components of “soft computation,” namely fuzzy logic, neural network, and probability reasoning. The MPC classification fuzzy neural networks were built through training with learning data obtained from cutting tests performed in a reasonable range of cutting conditions. Several sensors were used for monitoring feature selection. Force, vibration, and spindle motor power signals were fused in multiple principal component directions to give a highly sensitive feature space. The tool conditions considered in the monitoring tests included sharp tool, tool breakage, slight wear, medium wear, and severe wear. The results showed success rates of approximate 94 percent in self-classification tests (i.e., the same data samples were used for both learning and classification), 84 percent in tests performed using different records for classification than those used for learning under the same cutting conditions, and about 80 percent in tests performed using samples obtained at different cutting conditions for classification than those used for learning within the same range of cutting conditions. The MPC fuzzy neural network classification strategy performed better than back-propagation trained feed-forward neural networks in these tests.

System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network

2011 ◽  
Vol 1 (3) ◽  
pp. 66-85 ◽  
Author(s):  
Tsung-Chih Lin ◽  
Yi-Ming Chang ◽  
Tun-Yuan Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear System ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural Networks ◽  
Training Data ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Recurrent Interval

This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification involving external disturbances. Only by using the current inputs and most recent outputs of the input layers, the system can be completely identified based on RIT2FNNs. In order to show that the interval IT2FNNs can handle the measurement uncertainties, training data are corrupted by white Gaussian noise with signal-to-noise ratio (SNR) 20 dB. Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs).

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