Cluster-Based Self-organizing Neuro-fuzzy System with Hybrid Learning Approach for Function Approximation

2005 ◽  
pp. 1186-1189
Author(s):  
Chunshien Li ◽  
Kuo-Hsiang Cheng ◽  
Chih-Ming Chen ◽  
Jin-Long Chen
Keyword(s):  
Fuzzy System ◽  
Function Approximation ◽  
Hybrid Learning ◽  
Learning Approach ◽  
Neuro Fuzzy ◽  
Self Organizing

scholarly journals Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task

2021 ◽  
Vol 14 (1) ◽  
pp. 123-129
Author(s):  
Yevgeniy Bodyanskiy ◽  
Anastasiia Deineko ◽  
Iryna Pliss ◽  
Olha Chala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Hybrid Learning ◽  
Medical Diagnostics ◽  
Speed Of Information Processing ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Overlapping Classes

Background: The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed. Methods: Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced. Results: The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes. Conclusion: The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.

Pseudoerror-Based Self-Organizing Neuro-Fuzzy System

2004 ◽  
Vol 12 (6) ◽  
pp. 812-819 ◽  
Author(s):  
C. Li ◽  
C.-Y. Lee ◽  
K.-H. Cheng
Keyword(s):  
Fuzzy System ◽  
Neuro Fuzzy ◽  
Self Organizing
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