In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, ...In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, we obtain a law of iterated logarithm and a Chung type law of iterated logarithm for the maximum likelihood estimator (MLE) in the present model.展开更多
This paper proposes some regularity conditions, which result in the existence, strong consistency and asymptotic normality of maximum quasi-likelihood estimator （MQLE） in quasi-likelihood nonlinear models （QLNM） w...This paper proposes some regularity conditions, which result in the existence, strong consistency and asymptotic normality of maximum quasi-likelihood estimator （MQLE） in quasi-likelihood nonlinear models （QLNM） with random regressors. The asymptotic results of generalized linear models （GLM） with random regressors are generalized to QLNM with random regressors.展开更多
Since refrigeration,air-conditioning and heat pump systems account to 25–30%of all energy consumed in the world,there is a considerable potential to mitigate the Global Warming by increasing the efficiency of the rel...Since refrigeration,air-conditioning and heat pump systems account to 25–30%of all energy consumed in the world,there is a considerable potential to mitigate the Global Warming by increasing the efficiency of the related appliances.Magnetocaloric systems,i.e.refrigerators and heat pumps,are promising solutions due to their large theoretical Coefficient Of Performance(COP).However,there is still a long way to make such systems marketable.One barrier is the cost of the magnet and magnetocaloric materials,which can be overcome by decreasing the materials quantity,e.g.by optimizing the geometry with efficient dimensioning procedures.In this work,we have developed a machine learning method to predict the three most significant performance values of magnetocaloric heat pumps:temperature span,heating power and COP.We used 4 different regressors:ordinary least squares,ridge,lasso and K-Nearest Neighbors(KNN).By using a dataset generated by numerical calculations,we have arrived at minimum average relative errors of the temperature span,heating power and COP of 23%,29%and 31%,respectively.While the lasso regressor is more appropriate when using small datasets,the ordinary least squares regressor shows the best performance when using more samples.The best order of polynomials range between 3,for the heating power,to 5,for the COP.The worse performance in predicting the three performance values occurs when using the KNN regressor.Furthermore,the application of regressors to the dataset is more adequate to evaluate the temperature span rather than energetic performance values.展开更多
Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggest...Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.展开更多
文摘In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, we obtain a law of iterated logarithm and a Chung type law of iterated logarithm for the maximum likelihood estimator (MLE) in the present model.
基金Supported by National Natural Science Foundation of China (No. 10761011,10671139,10901135)Natural Science Foundation of Yunnan Province(No. 2008CD081)Special Foundation for Middle and Young Excellent Teachers of Yunnan University
文摘This paper proposes some regularity conditions, which result in the existence, strong consistency and asymptotic normality of maximum quasi-likelihood estimator （MQLE） in quasi-likelihood nonlinear models （QLNM） with random regressors. The asymptotic results of generalized linear models （GLM） with random regressors are generalized to QLNM with random regressors.
基金This work was supported by FCT-Portugal,project Network of Ex-treme Conditions Laboratories NECL-IFIMUP,NORTE-01-0145-FEDER-022096Project PTDC/EME-SIS/31575/2017-POCI-01-0145-FEDER-031575 is acknowledged.D.J.S.acknowledges his contract DL57/2016 reference SFRH-BPD-90571/2012.
文摘Since refrigeration,air-conditioning and heat pump systems account to 25–30%of all energy consumed in the world,there is a considerable potential to mitigate the Global Warming by increasing the efficiency of the related appliances.Magnetocaloric systems,i.e.refrigerators and heat pumps,are promising solutions due to their large theoretical Coefficient Of Performance(COP).However,there is still a long way to make such systems marketable.One barrier is the cost of the magnet and magnetocaloric materials,which can be overcome by decreasing the materials quantity,e.g.by optimizing the geometry with efficient dimensioning procedures.In this work,we have developed a machine learning method to predict the three most significant performance values of magnetocaloric heat pumps:temperature span,heating power and COP.We used 4 different regressors:ordinary least squares,ridge,lasso and K-Nearest Neighbors(KNN).By using a dataset generated by numerical calculations,we have arrived at minimum average relative errors of the temperature span,heating power and COP of 23%,29%and 31%,respectively.While the lasso regressor is more appropriate when using small datasets,the ordinary least squares regressor shows the best performance when using more samples.The best order of polynomials range between 3,for the heating power,to 5,for the COP.The worse performance in predicting the three performance values occurs when using the KNN regressor.Furthermore,the application of regressors to the dataset is more adequate to evaluate the temperature span rather than energetic performance values.
文摘Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.
