Fuzzy Neural Network Models for Supervised Classification: Multispectral Image Analysis

1999 ◽  
Vol 14 (4) ◽  
pp. 42-51 ◽  
Author(s):  
Arun D. Kulkarni ◽  
Kamlesh Lulla
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Supervised Classification ◽  
Fuzzy Neural Network ◽  
Network Models ◽  
Multispectral Image ◽  
Neural Network Models ◽  
Fuzzy Neural

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.

scholarly journals Optimization of parameters of neuro-fuzzy model

2020 ◽  
Vol 19 (1) ◽  
pp. 229
Author(s):  
Lyalya Bakievna Khuzyatova ◽  
Lenar Ajratovich Galiullin
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Model ◽  
Network Models ◽  
Knowledge Bases ◽  
Fuzzy Neural Networks ◽  
Neural Network Models ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

<p>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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