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.

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.

Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone

2020 ◽  
Vol 17 (6) ◽  
pp. 2755-2762
Author(s):  
Pramoda Patro ◽  
Krishna Kumar ◽  
G. Suresh Kumar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Problem ◽  
Fuzzy Neural Network ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Fuzzy Neural ◽  
Approximate Function ◽  
Age Prediction

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.

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