Neural Networks
Real time face and mouth recognition using radial basis function neural networks
Expert Systems with Applications: An International Journal
Back Propagation Neural Network for Short-term Electricity Load Forecasting with Weather Features
CINC '09 Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
A model updating strategy for predicting time series with seasonal patterns
Applied Soft Computing
Fault Diagnosis Based on K-Means Clustering and PNN
ICINIS '10 Proceedings of the 2010 Third International Conference on Intelligent Networks and Intelligent Systems
Expert Systems with Applications: An International Journal
Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics
Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics
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This paper proposes an effective fault detection and identification method for systems which perform in multiple processes. One such type of system investigated in this paper is COSMED K4b^2. K4b^2 is a standard portable electrical device designed to test pulmonary functions in various applications, such as athlete training, sports medicine and health monitoring. However, its actual sensor outputs and received data may be disturbed by Electromagnetic Interference (EMI), body artifacts, and device malfunctions/faults, which might cause misinterpretations of activities or statuses to people being monitored. Although some research is reported to detect faults in specific steady state, normal approach may yield false alarms in multi-processes applications. In this paper, a novel and comprehensive method, which merges statistical analysis and intelligent computational model, is proposed to detect and identify faults of K4b^2 during exercise monitoring. Firstly the principal component analysis (PCA) is utilized to acquire main features of measured data and then K-means is combined to cluster various processes for abnormalities detection. When faults are detected, a back propagation (BP) neural network is constructed to identify and isolate faults. The effectiveness and feasibility of the proposed model method is finally verified with experimental data.