Fuzzy Neural Network (FNN) Pada Proses Identifikasi Penyakit ISPA

ISPA is a disease that can affect anyone from children, adolescents, adults, and even the elderly. The causes experienced by sufferers of this disease are quite simple, such as fever, runny nose, and cough. The discussion in this paper describes the process of ISPA disease identification by developing a Fuzzy Neural Network (FNN) model. The process will be optimized using Fuzzy Logic to form rules for the diagnostic process, then proceed with an Artificial Neural Network (ANN). This model can maximize the performance of ANN in the identification process so that the output given is quite precise and accurate. The results provided by Fuzzy Logic can describe the clarity of the rules in diagnosis by presenting several rules (rules) that are presented from the Fuzzyfication process to the Defuzzyfication process. The output obtained from the ANN process also shows quite perfect results with an average error value based on MSE of 0.00912 and accuracy value of 91.96%. With these results, it can be stated that the FNN model can be used in the ISPA diagnosis process so that the presentation of this paper aims to provide an alternative in the identification process

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

Matériaux & Techniques ◽

10.1051/mattech/2019002 ◽

2018 ◽

Vol 106 (6) ◽

pp. 603 ◽

Cited By ~ 2

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.

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Short-Term Load Forecasting by using Ann, Fuzzy Logic and Fuzzy Neural Network

International Journal of Engineering Research and ◽

10.17577/ijertv6is010255 ◽

2017 ◽

Vol V6 (01) ◽

Author(s):

Girraj Singh ◽

D. S. Chauhan ◽

Aseem Chandel ◽

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Fuzzy Neural Network ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting ◽

Fuzzy Neural

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Special Issue "Fuzzy/Neural Network Applications to Dynamics and Control of Mechanical Systems". Diagnosis System of Abnormality in Refuse Incineration Plant using Fuzzy Logic.

TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C ◽

10.1299/kikaic.58.1696 ◽

1992 ◽

Vol 58 (550) ◽

pp. 1696-1702

Author(s):

Hidetaka ONO

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Fuzzy Neural Network ◽

Special Issue ◽

Incineration Plant ◽

Network Applications ◽

Fuzzy Neural ◽

Dynamics And Control ◽

And Control ◽

Neural Network Applications

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Fuzzy-Neural Network Traffic Prediction Framework with Wavelet Decomposition

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1836-03 ◽

2003 ◽

Vol 1836 (1) ◽

pp. 16-20 ◽

Cited By ~ 27

Author(s):

Heng Xiao ◽

Hongyu Sun ◽

Bin Ran ◽

Youngtae Oh

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Fuzzy Logic ◽

Wavelet Decomposition ◽

Fuzzy Neural Network ◽

Wavelet Denoising ◽

Traffic Prediction ◽

Denoising Method ◽

Fuzzy Neural ◽

Special Factors

The framework of a traffic prediction model that could eliminate noise caused by random travel conditions is investigated. This model also can quantitatively calculate the influence of special factors. The framework combined several artificial intelligence technologies, such as wavelet transform, neural network, and fuzzy logic. The wavelet denoising method is emphasized and analyzed.

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Research on the Automotive Suspension Based on BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.341-342.478 ◽

2013 ◽

Vol 341-342 ◽

pp. 478-481

Author(s):

Tai Hao Li ◽

He Pan

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Bp Neural Network ◽

Fuzzy Neural Network ◽

Air Suspension ◽

The Neural Network ◽

Fuzzy Neural ◽

Intelligence Theory ◽

Neural Network Algorithm ◽

Automotive Suspension

This article uses the application of artificial intelligence theory to research on the air suspension system, constructing the structure of control system, and the study of the neural network algorithm is simulation for its study of results. The fusion of fuzzy logic and neural network consist of the fuzzy neural network, which has the advantages of fuzzy logic and neural network.

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