FUNCOM: A constrained learning algorithm for fuzzy neural networks

2000 ◽  
Vol 112 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Paris Mastorocostas ◽  
John Theocharis
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Constrained Learning

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Takagi Sugeno

A learning algorithm of fuzzy neural networks with triangular fuzzy weights

1995 ◽  
Vol 71 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Kitaek Kwon ◽  
Hideo Tanaka
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Fuzzy Weights

Optimization and Research of the Adaptive Wiener Filter Based on Fuzzy Neural Networks

2013 ◽  
Vol 411-414 ◽  
pp. 1660-1664
Author(s):  
Yan Jun Zhao ◽  
Li LIU
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Wiener Filter ◽  
Fuzzy Neural Networks ◽  
Central Value ◽  
Fuzzy Neural ◽  
Training Error ◽  
Neural Network Technology

This paper introduces fuzzy neural network technology into the adaptive filter and makes further research on its structure and algorithms. At first, fuzzy rules are determined and the network structure is built by means of dividing fuzzy subspaces. Secondly, membership functions are chosen layers are defined and the network is trained by adaptive learning algorithm. Thirdly, training error is the minimum with repeating debugging. Finally, linking weight, the central value and width of the network membership function is adjusted by using experience of experts. The optimal performance of Adaptive Wiener Filter is realized based on Fuzzy Neural Networks.

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.

scholarly journals Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems

2018 ◽  
pp. 114-133
Author(s):  
Paulo Vitor de Campos Souza
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Compact Set ◽  
Regularization Theory ◽  
Promising Alternative ◽  
Fuzzy Neural ◽  
Regression Problems ◽  
Learning Machine

This paper presents a novel learning algorithm for fuzzy logic neuron based on neural networks and fuzzy systems able to generate accurate and transparent models. The learning algorithm is based on ideas from Extreme Learning Machine [36], to achieve a low time complexity, and regularization theory, resulting in sparse and accurate models. A compact set of incomplete fuzzy rules can be extracted from the resulting network topology. Experiments considering regression problems are detailed. Results suggest the proposed approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability.

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