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.

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 Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaoxu Chen ◽  
Linyuan Wang ◽  
Zhiyu Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Corrosion Rate ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Internal Corrosion ◽  
Fuzzy Neural

Aiming at the characteristics of the nonlinear changes in the internal corrosion rate in gas pipelines, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a principal component analysis (PCA) algorithm and a dynamic fuzzy neural network (D-FNN) to address the problems above. The principal component analysis algorithm is used for dimensional reduction and feature extraction, and a dynamic fuzzy neural network model is utilized to perform the prediction. The study implementing the PCA-D-FNN is further accomplished with the corrosion data from a real pipeline, and the results are compared among the artificial neural networks, fuzzy neural networks, and D-FNN models. The results verify the effectiveness of the model and algorithm for inner corrosion rate prediction.

scholarly journals Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaochen Zhang ◽  
Hongli Gao ◽  
Haifeng Huang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Performance Degradation ◽  
Ball Screw ◽  
Degradation Model ◽  
Quantum Genetic Algorithm ◽  
Feature Vectors ◽  
Fuzzy Neural

To evaluate the performance of ball screw, screw performance degradation assessment technology based on quantum genetic algorithm (QGA) and dynamic fuzzy neural network (DFNN) is studied. The ball screw of the CINCINNATIV5-3000 machining center is treated as the study object. Two Kistler 8704B100M1 accelerometers and a Kistler 8765A250M5 three-way accelerometer are installed to monitor the degradation trend of screw performance. First, screw vibration signal features are extracted both in time domain and frequency domain. Then the feature vectors can be obtained by principal component analysis (PCA). Second, the initialization parameters of the DFNN are optimized by means of QGA. Finally, the feature vectors are inputted to DFNN for training and then get the screw performance degradation model. The experiment results show that the screw performance degradation model could effectively evaluate the performance of NC machine screw.

Forecasting Seismic Damage in Frame-Shear Wall Structure Based on Fuzzy Neural Network

2012 ◽  
Vol 594-597 ◽  
pp. 1692-1695 ◽  
Author(s):  
Wei Jiang ◽  
Bing Xin Gu
Keyword(s):  
Neural Network ◽  
Seismic Behavior ◽  
Fuzzy Neural Network ◽  
Comprehensive Evaluation ◽  
Shear Wall ◽  
Seismic Damage ◽  
Wall Structure ◽  
Fuzzy Neural

The author did numerical experient on the seismic behavior of fram-surpported shear wall structure,then proposed a method of fuzzy neural network forrecast besed on second-time comprehensive evaluation for training and forecast with all these experiment mertiarials.The result shows that this method can forecast the seismic behavior of fram-surpported shear wall structure fairly well, which is avilable to this kind of sructure selection.

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>

Evaluating the Environmental Cost Internalization Based on the Fuzzy Neural Network

2014 ◽  
Vol 1073-1076 ◽  
pp. 495-499
Author(s):  
Xiang Song Meng ◽  
Yi Yao Zhu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Environmental Cost ◽  
Fuzzy Evaluation ◽  
Analysis Model ◽  
Cost Index ◽  
Fuzzy Neural ◽  
Environment Cost

Internalization of environment cost assessment measures the level of an enterprise’s environmental cost internalization. It’s also the basis of carrying out recycling economic in an enterprise. First of all, we established an environmental cost analysis model, in line with which we build the internalization of environment cost index system. Then adopting comprehensive evaluation method basing on fuzzy neural network can help us assess the effect brought by the internalization of environment cost. Finally, we conducted an experiment which comparing fuzzy neural network with the fuzzy evaluation of environment cost objectively. So we can think it’s an effective method.

Fuzzy Classification of Gear Fault Using Principal Component Analysis-Based Fuzzy Neural Network

2020 ◽  
Author(s):  
Kai Zhou ◽  
J. Tang
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Fuzzy Classification ◽  
Vibration Data ◽  
Gear Fault ◽  
Fuzzy Neural

Abstract Condition assessment of machinery components such as gears is important to maintain their normal operations and thus can bring benefit to their life circle management. Data-driven approaches haven been a promising way for such gear condition monitoring and fault diagnosis. In practical situation, gears generally have a variety of fault types, some of which exhibit continuous severities of fault. Vibration data collected oftentimes are limited to reflect all possible fault types. Therefore, there is practical need to utilize the data with a few discrete fault severities in training and then infer fault severities for the general scenario. To achieve this, we develop a fuzzy neural network (FNN) model to classify the continuous severities of gear faults based on the experimental measurement. Principal component analysis (PCA) is integrated with the FNN model to capture the main features of the time-series vibration signals with dimensional reduction for the sake of computational efficiency. Systematic case studies are carried out to validate the effectiveness of proposed methodology.

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