| Summary: | 碩士 === 國立清華大學 === 電機工程學系 === 102 === In recent years, the biomedical application of electronic nose sensor system has
been noticed, for example, this thesis will focus on the recognition of pneumonia data
from patients. However, the sensitivity of sensor array is not high enough so that the
captured data is somewhat overlapped. In order to analyze these data further, this
thesis proposes some methods to classify them with probabilistic model, such as
CRBM. Continuous Restricted Boltzmann Machine (CRBM) is a generative
probabilistic model that can cluster and classify, and that can reconstruct data
distribution from training data. Therefore, there are 3 possible ways to classify
pneumonia data by CRBM. First, as a clusterer, CRBM can re-project data into higher
-dimensional space or lower-dimensional space so that the data will be classified more
easily. Secondly, as a classifier, CRBM uses an additional neuron as label to learn
class of training data. Finally, as a generative model, CRBM can re-generate the data
distribution of training data following its energy function so that we can estimate the
probability density in the space. After estimating the probability density, the Bayesian
Classifier can classify with it.
In addition, this thesis proposes a setup to test 3
rd
CRBM analog chip. Since
training mechanism was not designed for the this chip, so we use the data acquisition
(DAQ) system and FPGA card to implement training algorithm of CRBM. This is the
so-called Chip-in-a-Loop training. The performance of this training mechanism will
be evalutated.
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