Abstract
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.