Incremental learning of dynamic fuzzy neural networks for accurate system modeling

2009 ◽  
Vol 160 (7) ◽  
pp. 972-987 ◽  
Author(s):  
Xingsheng Deng ◽  
Xinzhou Wang
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
System Modeling ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural

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

scholarly journals Intelligent Clustering and Dynamic Incremental Learning to Generate Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 679
Author(s):  
Muhammad Anwar Ma’sum
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Data Classification ◽  
Fuzzy Neural Networks ◽  
Benchmark Dataset ◽  
Synthetic Dataset ◽  
Special Treatment ◽  
Modal Data ◽  
Fuzzy Neural ◽  
Benchmark Datasets

Classification in multi-modal data is one of the challenges in the machine learning field. The multi-modal data need special treatment as its features are distributed in several areas. This study proposes multi-codebook fuzzy neural networks by using intelligent clustering and dynamic incremental learning for multi-modal data classification. In this study, we utilized intelligent K-means clustering based on anomalous patterns and intelligent K-means clustering based on histogram information. In this study, clustering is used to generate codebook candidates before the training process, while incremental learning is utilized when the condition to generate a new codebook is sufficient. The condition to generate a new codebook in incremental learning is based on the similarity of the winner class and other classes. The proposed method was evaluated in synthetic and benchmark datasets. The experiment results showed that the proposed multi-codebook fuzzy neural networks that use dynamic incremental learning have significant improvements compared to the original fuzzy neural networks. The improvements were 15.65%, 5.31% and 11.42% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively, for incremental version 1. The incremental learning version 2 improved by 21.08% 4.63%, and 14.35% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively. The multi-codebook fuzzy neural networks that use intelligent clustering also had significant improvements compared to the original fuzzy neural networks, achieving 23.90%, 2.10%, and 15.02% improvements on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively.

scholarly journals An Incremental Learning of Concept Drifts Using Evolving Type-2 Recurrent Fuzzy Neural Networks

2017 ◽  
Vol 25 (5) ◽  
pp. 1175-1192 ◽  
Author(s):  
Mahardhika Pratama ◽  
Jie Lu ◽  
Edwin Lughofer ◽  
Guangquan Zhang ◽  
Meng Joo Er
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Concept Drifts

AUTOMATED NONLINEAR SYSTEM MODELING WITH MULTIPLE FUZZY NEURAL NETWORKS AND KERNEL SMOOTHING

2010 ◽  
Vol 20 (05) ◽  
pp. 429-435 ◽  
Author(s):  
WEN YU ◽  
XIAOOU LI
Keyword(s):  
Neural Networks ◽  
Kernel Smoothing ◽  
System Modeling ◽  
Fixed Time ◽  
Fuzzy Neural Networks ◽  
Time Interval ◽  
Nonlinear System Modeling ◽  
Structure Selection ◽  
Fuzzy Neural ◽  
Identification Approach

This paper, presents a novel identification approach using fuzzy neural networks. It focuses on structure and parameters uncertainties which have been widely explored in the literatures. The main contribution of this paper is that an integrated analytic framework is proposed for automated structure selection and parameter identification. A kernel smoothing technique is used to generate a model structure automatically in a fixed time interval. To cope with structural change, a hysteresis strategy is proposed to guarantee finite times switching and desired performance.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Hotbox level detection of railway vehicle using fuzzy neural networks

2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Railway Vehicle ◽  
Fuzzy Neural
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