scholarly journals PREDICTION OF HIGH-PERFORMANCE FIBERREINFORCED POLYMER CONCRETE USING FUZZY NEURAL NETWORK PROTOTYPES

YMER Digital ◽  
2022 ◽  
Vol 21 (01) ◽  
pp. 192-205
Author(s):  
N Raghuraman ◽  
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Fuzzy Neural Network ◽  
Structural Response ◽  
Polymer Concrete ◽  
Model Parameters ◽  
Maintenance Time ◽  
Fuzzy Neural ◽  
Rc Building

RC building elements of Reinforcing and upgrading is essential to extend its maintenance time, to overcome first structural limitations, and to control the consequence of building construction or design flaws. The RC constructions are reinforced by using the FRP-fiber reinforced polymer. This study utilizes the FRP in concrete structures for instance a Jute, coir, and Sisal is explored for its reliability in improving ductility and strength related structural performance. FRP structural response of the model parameters is studied by measuring the numerical and experimental terms, for instance, Ductility, Deflection, Tensile-Strength, and Compression-Strength. The quality of the sample specimens is tested by using the Fuzzy Neural Network (FNN) system. At this time, compared with existing jobs, the propounded Fuzzy Neural Network model accomplishes the best presentation regarding all boundaries for the fiberreinforced specimen over different stacked conditions

Intelligent Prediction on the Quality of Cementing

2011 ◽  
Vol 84-85 ◽  
pp. 373-377
Author(s):  
Wei Zhang Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Influential Factor ◽  
Software Systems ◽  
Neural Network Modeling ◽  
Quality Prediction ◽  
Modeling Tools ◽  
Fuzzy Neural ◽  
Neural Network System

The present solutions of well cementing are mostly designed by designers’ experience and calculation which can not predict the engineering quality after application of the designs. Meanwhile some questions in the designs can not be solved before construction. On the basis of detailed evaluation of every influential factor according to construction and environmental conditions, this article provides cementing fuzzy neural network model by means of 2nsoftEditor neural network modeling tools, and the stable software systems with the combination of artificial neural network and fuzzy logic rules are expected to improve the credibility of cementing quality prediction. Construction practice shows that cementing quality prediction with application of fuzzy neural network system before cementing can greatly reduce the cementing costs and improve the cementing success ratio.

scholarly journals Optimization of parameters of neuro-fuzzy model

2020 ◽  
Vol 19 (1) ◽  
pp. 229
Author(s):  
Lyalya Bakievna Khuzyatova ◽  
Lenar Ajratovich Galiullin
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Model ◽  
Network Models ◽  
Knowledge Bases ◽  
Fuzzy Neural Networks ◽  
Neural Network Models ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

<p>The need for increasing the efficiency of the neuron-fuzzy model in the formation of knowledge bases is being updated. The task is to develop methods and algorithms for presetting and optimizing the parameters of a fuzzy neural network. To solve difficult formalized tasks, it is necessary to develop decision support systems - expert systems based on a knowledge base. ES developers are constantly faced with the problems of “extraction” and formalization of knowledge, as well as the search for new ways to obtain it. To do this, use the extraction, acquisition and formation of knowledge. Currently, the formation of knowledge bases is relevant for the creation of hybrid technologies - fuzzy neural networks that combine the advantages of neural network models and fuzzy systems. The analysis of the efficiency of the fuzzy neural network carried out in the work showed that the quality of training of the NN largely depends on the choice of the number of fuzzy granules for input drugs. In addition, to use fuzzy information formalized by the mathematical apparatus of fuzzy logic, procedures are required for selecting optimal forms and presetting the parameters of the corresponding membership functions (MF).</p>

Adaptive Fuzzy Neural Network of Cast-Rolling Hydraulic AGC

2012 ◽  
Vol 531-532 ◽  
pp. 773-777
Author(s):  
Zhong Chu Wang ◽  
Xin Zhao ◽  
Ran Bi
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Strategies ◽  
Dynamic Performance ◽  
Adaptive Controller ◽  
Model Parameters ◽  
Control Effect ◽  
Adaptive Fuzzy Neural Network ◽  
Fuzzy Neural ◽  
New Type

Cast-roll hydraulic AGC is the main control means of the strip t hickness. The control effect of traditional PID is poor for adjusting this kind of model parameters. Therefore, a new type of fuzzy neural network self-learning and adaptive controller is proposed, and analyze the composition and basic performance. The simulation results show that the new controller can effectively improve its response, what’s more it has a better dynamic performance more than another control strategies.

scholarly journals Evaluation of knitted suit fabric style based on fuzzy neural network

2020 ◽  
Vol 15 ◽  
pp. 155892502097182
Author(s):  
Xintong Li ◽  
Honglian Cong ◽  
Zhe Gao
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Shear Stiffness ◽  
Polyester Fabric ◽  
Production Quality ◽  
Fuzzy Neural ◽  
And Cluster Analysis

In order to better judge the fabric style of knitted suit fabrics and improve the production quality of knitted suit fabrics, we use principal component analysis and cluster analysis methods to process fabric samples and evaluation indicators, and use neural network technology to establish The fuzzy neural network model outputs comprehensive evaluation values to judge knitted suit fabrics. The results show that the predicted value of the model output is above 0.6. The style of knitted suit fabric is close to that of traditional woven suit fabric, the flexural stiffness is between 5 and 20 μN• m, the extensibility is between 10% and 20% and the shear stiffness is between 50 N/m. The value of wool and polyester fabric is basically above 0.7, and the style is similar to the woven suit fabric, followed by knitted suit fabrics of cotton and polyester.

scholarly journals Improving a fuzzy neural network for predicting storage usage and calculating customer value

2020 ◽  
Vol 19 (5) ◽  
pp. 292-301
Author(s):  
Reza Rabieyan ◽  
Philipp Pohl
Keyword(s):  
Neural Network ◽  
Resource Management ◽  
Neural Network Model ◽  
Crucial Role ◽  
Customer Value ◽  
Fuzzy Neural Network ◽  
Prediction Methods ◽  
Fuzzy Neural ◽  
Customer Services

Abstract Predicting the behavior of customers plays a crucial role in the quality of resource management and customer services. In this article, a fuzzy neural network model for predicting the customer storage usage is identified. The identified fuzzy neural network is improved and finally the result of the improved fuzzy neural network is compared with some other fuzzy neural network and other prediction methods.

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.

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