Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer Perceptron Architectures

2008 ◽  
pp. 23-57 ◽  
Author(s):  
Sung-Kwun Oh ◽  
Witold Pedrycz
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Multi Layer Perceptron ◽  
Rule Based ◽  
Analysis And Design ◽  
Fuzzy Neural ◽  
Networks Analysis

scholarly journals Sistemas neurais híbridos para reconhecimento de padrões em narizes artificiais

2005 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Cleber Zanchettin ◽  
Teresa Bernarda Ludermir
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Delay ◽  
Fuzzy Neural Networks ◽  
Multi Layer Perceptron ◽  
Fuzzy Neural

Este trabalho investiga a utilização de Sistemas Híbridos Inteligentes no sistema de reconhecimento de padrões de um nariz artificial. São abordadas as arquiteturas conexionistas Multi-Layer Perceptron e Time Delay Neural Network; e as arquiteturas híbridas Feature-weighted Detector e Evolving Fuzzy Neural Networks. Além dos classificadores, um filtro Wavelet é avaliado como método de pré-processamento para os sinais de odores. Foram analisados sinais gerados por um nariz artificial, composto por um conjunto de sensores de polímeros condutores, exposto a duas bases de odores distintas.

SIMPLIFIED FUZZY INFERENCE RULE-BASED GENETICALLY OPTIMIZED HYBRID FUZZY NEURAL NETWORKS

2008 ◽  
Vol 16 (02) ◽  
pp. 245-274 ◽  
Author(s):  
BYOUNG-JUN PARK ◽  
WITOLD PEDRYCZ ◽  
SUNG-KWUN OH
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Inference Rule ◽  
Least Square Method ◽  
Fuzzy Neural Networks ◽  
Least Square ◽  
Rule Based ◽  
Fuzzy Partition ◽  
Fuzzy Neural ◽  
Input Variables

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the condition part of the rule-based structure of the gHFNN. The conclusion part of the gHFNN is designed using PNNs. We distinguish between two types of the simplified fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the conclusion part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, we experimented with three representative numerical examples. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when compared with other neurofuzzy models.

scholarly journals Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

2011 ◽  
Vol 15 (1) ◽  
pp. 185-196 ◽  
Author(s):  
Y.-M. Chiang ◽  
L.-C. Chang ◽  
M.-J. Tsai ◽  
Y.-F. Wang ◽  
F.-J. Chang
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Fuzzy Neural Networks ◽  
Water Levels ◽  
Inference System ◽  
Rule Based ◽  
Pumping Stations ◽  
Fuzzy Neural ◽  
On Line ◽  
Model Efficiency

Abstract. Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

scholarly journals Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

2010 ◽  
Vol 7 (5) ◽  
pp. 6725-6756 ◽  
Author(s):  
Y.-M. Chiang ◽  
L.-C. Chang ◽  
M.-J. Tsai ◽  
Y.-F. Wang ◽  
F.-J. Chang
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Fuzzy Neural Networks ◽  
Water Levels ◽  
Inference System ◽  
Rule Based ◽  
Pumping Stations ◽  
Fuzzy Neural ◽  
On Line ◽  
Model Efficiency

Abstract. Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagatiom fuzzy neural network (CFNN) for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

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.

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