A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T–S fuzzy neural network

2009 ◽  
Vol 36 (2) ◽  
pp. 2377-2390 ◽  
Author(s):  
W.K. Wong ◽  
X.H. Zeng ◽  
W.M.R. Au
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Expert System ◽  
Fuzzy Neural Network ◽  
Decision Support Tool ◽  
Support Tool ◽  
Knowledge Based ◽  
Fuzzy Neural ◽  
Attribute Evaluation

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>

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