Construction of COD Simulation Model for Activated Sludge Process by Recursive Fuzzy Neural Network.

2001 ◽  
Vol 34 (3) ◽  
pp. 369-375 ◽  
Author(s):  
SHUTA TOMIDA ◽  
TAIZO HANAI ◽  
HIROYUKI HONDA ◽  
TAKESHI KOBAYASHI
Keyword(s):  
Neural Network ◽  
Activated Sludge ◽  
Simulation Model ◽  
Fuzzy Neural Network ◽  
Activated Sludge Process ◽  
Fuzzy Neural

Construction of COD simulation model for activated sludge process by fuzzy neural network

1999 ◽  
Vol 88 (2) ◽  
pp. 215-220 ◽  
Author(s):  
Shuta Tomida ◽  
Taizo Hanai ◽  
Naoyasu Ueda ◽  
Hiroyuki Honda ◽  
Takeshi Kobayashi
Keyword(s):  
Neural Network ◽  
Activated Sludge ◽  
Simulation Model ◽  
Fuzzy Neural Network ◽  
Activated Sludge Process ◽  
Fuzzy Neural

Determination of Operating Conditions in Activated Sludge Process Using Fuzzy Neural Network and Genetic Algorithm

2001 ◽  
Vol 34 (8) ◽  
pp. 1033-1039 ◽  
Author(s):  
HIROKI YOSHIKAWA ◽  
TAIZO HANAI ◽  
SHUTA TOMIDA ◽  
HIROYUKI HONDA ◽  
TAKESHI KOBAYASHI
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Activated Sludge ◽  
Fuzzy Neural Network ◽  
Operating Conditions ◽  
Activated Sludge Process ◽  
Fuzzy Neural

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.

Inverse Control of Cable-driven Parallel Mechanism Using Type-2 Fuzzy Neural Network

2010 ◽  
Vol 36 (3) ◽  
pp. 459-464 ◽  
Author(s):  
Cheng-Dong LI ◽  
Jian-Qiang YI ◽  
Yi YU ◽  
Dong-Bin ZHAO
Keyword(s):  
Neural Network ◽  
Parallel Mechanism ◽  
Fuzzy Neural Network ◽  
Inverse Control ◽  
Fuzzy Neural
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