Title :
Intelligent control application on sample identification
Author_Institution :
Dept. of Electr. & Comput. Eng., Wayne State Univ., Detroit, MI
Abstract :
An intelligent control implementation is proposed for sample differentiation with Raman spectroscopy, which can be used to characterize various samples for decision-making and medical diagnosis. Raman spectra are weak signals whose features are inevitably affected by numerous noises during the calibration process. These noises must be eliminated to an acceptable level. Fuzzy control has been widely used to solve uncertainty, imprecision and vague phenomena, so fuzzy logic can be used for noise filtering. The resulting intrinsic Raman spectrum has been trained using artificial neural networks. Both unsupervised learning and supervised learning are to be conducted in this preliminary research on sample identification. For unsupervised training, principal component analysis (PCA) is exploited, which is based on Hebbian rule and single value decomposition (SVD) approach, respectively. For supervised training, radial basis function (RBF) is presented. A complete procedure for sample identification consists of Raman spectra calibration, noise filtering, unsupervised classification and supervised neural network training. A systematic intelligent control approach is formulated in consequence for sample identification. The long-term objective is to create a real-time approach for sample analysis using a Raman spectrometer directly mounted at the end-effector of the medical robots to enhance robotic remote surgery
Keywords :
Hebbian learning; Raman spectra; Raman spectroscopy; filtering theory; fuzzy control; fuzzy logic; intelligent control; neural nets; principal component analysis; radial basis function networks; singular value decomposition; unsupervised learning; Hebbian rule; Raman spectra calibration; Raman spectroscopy; artificial neural networks; calibration process; decision-making; end-effector; fuzzy control; fuzzy logic; intelligent control application; medical diagnosis; medical robots; noise filtering; principal component analysis; radial basis function; research oil sample identification; robotic remote surgery; single value decomposition; supervised learning; supervised neural network training; supervised training; unsupervised classification; unsupervised learning; Calibration; Decision making; Filtering; Intelligent control; Medical diagnosis; Medical robotics; Principal component analysis; Raman scattering; Signal processing; Spectroscopy;
Conference_Titel :
Computational Cybernetics, 2004. ICCC 2004. Second IEEE International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-7803-8588-8
DOI :
10.1109/ICCCYB.2004.1437660