This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was t...This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.展开更多
The aim of this study was to explore the possibility of applying Fourier transform infrared（FTIR） spec- troscopy as a medical diagnostic toot based on a neural network classifier for detecting and classifying cholan...The aim of this study was to explore the possibility of applying Fourier transform infrared（FTIR） spec- troscopy as a medical diagnostic toot based on a neural network classifier for detecting and classifying cholangiocar- cinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position（P）, intensity（/） and full width at half-maximum（FWHM）, were measured, calculated and subsequently com- pared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function（RBF） net- work model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between can- cerous and normal tissue, including I1640, I1550, 11460,/1400, I1250, I1120,/10g0, Ii040 and P1040. In the RBF training classi- fication, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.展开更多
基金The National High Technology Research and Development Program of China (863 Program) (No.2003AA517020)
文摘This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.
文摘The aim of this study was to explore the possibility of applying Fourier transform infrared（FTIR） spec- troscopy as a medical diagnostic toot based on a neural network classifier for detecting and classifying cholangiocar- cinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position（P）, intensity（/） and full width at half-maximum（FWHM）, were measured, calculated and subsequently com- pared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function（RBF） net- work model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between can- cerous and normal tissue, including I1640, I1550, 11460,/1400, I1250, I1120,/10g0, Ii040 and P1040. In the RBF training classi- fication, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.
