Automated identification of novel amphetamines using a pure neural network and neural networks coupled with principal component analysis

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.

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Study of Fault Diagnosis of Hydro-Generator Unit Via Ga Nonlinear Principal Component Analysis Neural Network and Bayesian Neural Networks

2006 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2006.258546 ◽

2006 ◽

Author(s):

Qiao-ling Ji ◽

Wei-min Qi ◽

Wei-you Cai ◽

Yuan-chu Cheng

Keyword(s):

Neural Network ◽

Neural Networks ◽

Principal Component Analysis ◽

Fault Diagnosis ◽

Principal Component ◽

Component Analysis ◽

Nonlinear Principal Component Analysis ◽

Bayesian Neural Networks ◽

Generator Unit

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Diagnosis of process faults with neural networks and principal component analysis

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

10.1243/0954408001530164 ◽

2000 ◽

Vol 214 (2) ◽

pp. 131-143 ◽

Cited By ~ 10

Author(s):

J B Gomm ◽

M Weerasinghe ◽

D Williams

Keyword(s):

Neural Network ◽

Neural Networks ◽

Principal Component Analysis ◽

Fault Diagnosis ◽

Principal Component ◽

Process Variable ◽

Component Analysis ◽

Fault Detection And Diagnosis ◽

Processing Plant ◽

Large Network

Industrial plants often have many process variable measurements available, which can be monitored for fault detection and diagnosis. Using all these variables as inputs to an artificial neural network for fault diagnosis can result in an impractically large network, with consequent long training times and high computational requirement during use. Principal component analysis (PCA) is investigated in this paper for generating a reduced number of variables to be used as neural network inputs for fault diagnosis. The main application described is to a real industrial nuclear fuel processing plant. A simulated chemical process was also used to assist the development of the techniques. Results in both applications demonstrate satisfactory fault diagnosis performance with a reduction in the number of neural network parameters of approximately 50 per cent using PCA. The paper also includes some introductory material on PCA and neural networks, and their application to process fault diagnosis.

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