Melt index prediction based on fuzzy neural networks and PSO algorithm with online correction strategy

AIChE Journal ◽  
2011 ◽  
Vol 58 (4) ◽  
pp. 1194-1202 ◽  
Author(s):  
Xinggao Liu ◽  
Chengye Zhao
Keyword(s):  
Neural Networks ◽  
Pso Algorithm ◽  
Fuzzy Neural Networks ◽  
Melt Index ◽  
Online Correction ◽  
Fuzzy Neural

An improved Mamdani Fuzzy Neural Networks Based on PSO Algorithm and New Parameter Optimization

2016 ◽  
Vol 1 (1) ◽  
pp. 201 ◽  
Author(s):  
Lei Meng ◽  
Shoulin Yin ◽  
Xinyuan Hu
Keyword(s):  
Neural Networks ◽  
Parameter Optimization ◽  
Gradient Descent ◽  
Fuzzy Rule ◽  
Pso Algorithm ◽  
Fuzzy Neural Networks ◽  
Descent Method ◽  
Local Optimum ◽  
Gradient Descent Method ◽  
Fuzzy Neural

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.

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

scholarly journals Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Dongyeop Kang ◽  
Sangmoon Lee
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Memory Control
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