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.

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.

Design of Credit Risk Evaluation System Based on the Fuzzy Neural Network and Expert System

2014 ◽  
Vol 543-547 ◽  
pp. 4523-4527
Author(s):  
Hong Min Zhang
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Expert System ◽  
Credit Risk ◽  
Index System ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Enterprise Risk ◽  
Fuzzy Neural ◽  
Set Up

Credit risk is the main risk that Chinese commercial banks are facing. Taking into account three categories of risk factors, namely risk factors of enterprise, risk factors of commercial bank and risk factors of macroscopic economy, an index system was set up. Then, according to the index system and the characteristics of fuzzy neural network and expert system, a credit risk rating system based on fuzzy neural network and expert system was proposed.

scholarly journals Optimization of parameters of neuro-fuzzy model

2020 ◽  
Vol 17 (3) ◽  
pp. 1206
Author(s):  
Lyalya Bakievna Khuzyatova ◽  
Lenar Ajratovich Galiullin
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support Systems ◽  
Fuzzy Neural Network ◽  
Support Systems ◽  
Fuzzy Model ◽  
Knowledge Bases ◽  
Fuzzy Neural Networks ◽  
Neural Network Models ◽  
Fuzzy Neural

<p>The questions and problems of the formation of knowledge bases of intelligent man-machine decision support systems are considered. The neuron-fuzzy model used in the work is described. The need for increasing the efficiency of the neuron-fuzzy model in the formation of knowledge bases is being updated. The task is to develop methods and algorithms for presetting and optimizing the parameters of a fuzzy neural network. To solve difficult formalized tasks, it is necessary to develop decision support systems - expert systems based on a knowledge base. ES developers are constantly faced with the problems of “extraction” and formalization of knowledge, as well as the search for new ways to obtain it. To do this, use the extraction, acquisition and formation of knowledge. Currently, the formation of knowledge bases is relevant for the creation of hybrid technologies - fuzzy neural networks that combine the advantages of neural network models and fuzzy systems. The analysis of the efficiency of the fuzzy neural network carried out in the work showed that the quality of training of the NN largely depends on the choice of the number of fuzzy granules for input drugs. In addition, to use fuzzy information formalized by the mathematical apparatus of fuzzy logic, procedures are required for selecting optimal forms and presetting the parameters of the corresponding membership functions (MF).</p>

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.

Fuzzy Neural Network Models for Knowledge Discovery

2009 ◽  
pp. 103-119
Author(s):  
Arun Kulkarni ◽  
Sara McCaslin
Keyword(s):  
Neural Network ◽  
Knowledge Discovery ◽  
Fuzzy Neural Network ◽  
Network Models ◽  
Fuzzy Neural Networks ◽  
Classification Rules ◽  
Neural Network Models ◽  
Environment Change ◽  
Fuzzy Neural ◽  
Multispectral Satellite Images

This chapter introduces fuzzy neural network models as means for knowledge discovery from databases. It describes architectures and learning algorithms for fuzzy neural networks. In addition, it introduces an algorithm for extracting and optimizing classification rules from a trained fuzzy neural network. As an illustration, multispectral satellite images have been analyzed using fuzzy neural network models. The authors hope that fuzzy neural network models and the methodology for generating classification rules from data samples provide a valuable tool for knowledge discovery. The algorithms are useful in a variety of data mining applications such as environment change detection, military reconnaissance, crop yield prediction, financial crimes and money laundering, and insurance fraud detection.

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.

scholarly journals Robust Stability Analysis of Fuzzy Neural Network with Delays

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Kaihong Zhao ◽  
Yongkun Li
Keyword(s):  
Neural Network ◽  
Robust Stability ◽  
Fuzzy Neural Network ◽  
Equilibrium Points ◽  
Sufficient Conditions ◽  
Fuzzy Neural Networks ◽  
Time Varying ◽  
Stability Of Equilibrium ◽  
Robust Stability Analysis ◽  
Fuzzy Neural

We investigate local robust stability of fuzzy neural networks (FNNs) with time-varying and S-type distributed delays. We derive some sufficient conditions for local robust stability of equilibrium points and estimate attracting domains of equilibrium points except unstable equilibrium points. Our results not only show local robust stability of equilibrium points but also allow much broader application for fuzzy neural network with or without delays. An example is given to illustrate the effectiveness of our results.

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