Variants of Fuzzy Neural Gas

2019 ◽  
pp. 261-270
Author(s):  
Tina Geweniger ◽  
Thomas Villmann
Keyword(s):  
Neural Gas ◽  
Fuzzy Neural

Fuzzy Neural Gas for Unsupervised Vector Quantization

2012 ◽  
pp. 350-358 ◽  
Author(s):  
Thomas Villmann ◽  
Tina Geweniger ◽  
Marika Kästner ◽  
Mandy Lange
Keyword(s):  
Vector Quantization ◽  
Neural Gas ◽  
Fuzzy Neural

scholarly journals Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Tina Geweniger ◽  
Lydia Fischer ◽  
Marika Kaden ◽  
Mandy Lange ◽  
Thomas Villmann
Keyword(s):  
Simulated Annealing ◽  
Vector Quantization ◽  
Self Organizing Maps ◽  
Fuzzy C Means ◽  
Neural Gas ◽  
Fuzzy Neural ◽  
Evaluation Measure ◽  
Fuzzy Vector Quantization ◽  
Fuzzy Vector ◽  
Self Organizing

We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluation measure for clusterings based on fuzzy vector quantization.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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.

From Point Cloud to Triangular Mesh by Growing Neural Gas

2014 ◽  
Vol 24 (3) ◽  
pp. 651-662
Author(s):  
Feng ZENG ◽  
Tong YANG ◽  
Shan YAO
Keyword(s):  
Point Cloud ◽  
Triangular Mesh ◽  
Growing Neural Gas ◽  
Neural Gas
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