Artificial neural networks based on principal component analysis, fuzzy systems and fuzzy neural networks for preliminary design of rubble mound breakwaters

2010 ◽  
Vol 32 (4) ◽  
pp. 425-433 ◽  
Author(s):  
Can Elmar Balas ◽  
M. Levent Koç ◽  
Rıfat Tür
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Fuzzy Systems ◽  
Principal Component ◽  
Component Analysis ◽  
Fuzzy Neural Networks ◽  
Preliminary Design ◽  
Fuzzy Neural ◽  
Rubble Mound Breakwaters

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 Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 989 ◽  
Author(s):  
Agus Budi Dharmawan ◽  
Gregor Scholz ◽  
Shinta Mariana ◽  
Philipp Hörmann ◽  
Igi Ardiyanto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Light Emitting Diode ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Accuracy ◽  
Cell Detection ◽  
Artificial Neural ◽  
Feature Values

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.

Classification and identification of brands of iron ores using laser-induced breakdown spectroscopy combined with principal component analysis and artificial neural networks

2020 ◽  
Vol 12 (10) ◽  
pp. 1316-1323 ◽  
Author(s):  
Yawen Yang ◽  
Chen Li ◽  
Shu Liu ◽  
Hong Min ◽  
Chenglin Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Iron Ores ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Ann Models

In this work, PCA-ANN models of LIBS spectra were developed to classify and identify iron ores according to the production countries and brands.

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