The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmosph...The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum(AAM) function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.展开更多
This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network （WNN） toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time...This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network （WNN） toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time intervals through training the WNN with even time-interval samples. The method builds successive new model with the width of sliding window remaining invariable so as to obtain a dynamic prediction method for gas emission quantity. Furthermore, the method performs prediction by a self-developed WNN toolbox. Experiments indicate that such a model can overcome the deficiencies of the traditional static prediction model and can fully make use of the feature extraction capability of wavelet base function to reflect the geological feature of gas emission quantity dynamically. The method is characterized by simplicity, flexibility, small data scale, fast convergence rate and high prediction precision. In addition, the method is also characterized by certainty and repeatability of the predicted results. The effectiveness of this method is confirmed by simulation results. Therefore, this method will exert practical significance on promoting the application of WNN.展开更多
Single image super-resolution(SISR)is a fundamentally challenging problem because a low-resolution(LR)image can correspond to a set of high-resolution(HR)images,while most are not expected.Recently,SISR can be achieve...Single image super-resolution(SISR)is a fundamentally challenging problem because a low-resolution(LR)image can correspond to a set of high-resolution(HR)images,while most are not expected.Recently,SISR can be achieved by a deep learning-based method.By constructing a very deep super-resolution convolutional neural network(VDSRCNN),the LR images can be improved to HR images.This study mainly achieves two objectives:image super-resolution(ISR)and deblurring the image from VDSRCNN.Firstly,by analyzing ISR,we modify different training parameters to test the performance of VDSRCNN.Secondly,we add the motion blurred images to the training set to optimize the performance of VDSRCNN.Finally,we use image quality indexes to evaluate the difference between the images from classical methods and VDSRCNN.The results indicate that the VDSRCNN performs better in generating HR images from LR images using the optimized VDSRCNN in a proper method.展开更多
Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Int...Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Intelligence(AI) based model,each training data set is usually a limited sample of possible patterns of the process and hence,might not show the behavior of whole population.Accordingly,in the present paper,wavelet-based denoising method was used to smooth hydrological time series.Thereafter,small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoised-jittered data sets.Finally,the obtained pre-processed data were imposed into Artificial Neural Network(ANN) and Adaptive Neuro-Fuzzy Inference System(ANFIS)models for daily runoff-sediment modeling of the Minnesota River.To evaluate the modeling performance,the outcomes were compared with results of multi linear regression(MLR) and Auto Regressive Integrated Moving Average(ARIMA)models.The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoffsediment modeling of the case study up to 34%and 25%in the verification phase,respectively.展开更多
基金国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.2006AA01A116)广东省自然科学基金(the Natural Science Foundation of Guangdong Province of China under Grant No.0610576)深圳市科技计划项目(No.200511)
基金Projects(U1231105,10878026)supported by the National Natural Science Foundation of China
文摘The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum(AAM) function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.
文摘This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network （WNN） toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time intervals through training the WNN with even time-interval samples. The method builds successive new model with the width of sliding window remaining invariable so as to obtain a dynamic prediction method for gas emission quantity. Furthermore, the method performs prediction by a self-developed WNN toolbox. Experiments indicate that such a model can overcome the deficiencies of the traditional static prediction model and can fully make use of the feature extraction capability of wavelet base function to reflect the geological feature of gas emission quantity dynamically. The method is characterized by simplicity, flexibility, small data scale, fast convergence rate and high prediction precision. In addition, the method is also characterized by certainty and repeatability of the predicted results. The effectiveness of this method is confirmed by simulation results. Therefore, this method will exert practical significance on promoting the application of WNN.
文摘Single image super-resolution(SISR)is a fundamentally challenging problem because a low-resolution(LR)image can correspond to a set of high-resolution(HR)images,while most are not expected.Recently,SISR can be achieved by a deep learning-based method.By constructing a very deep super-resolution convolutional neural network(VDSRCNN),the LR images can be improved to HR images.This study mainly achieves two objectives:image super-resolution(ISR)and deblurring the image from VDSRCNN.Firstly,by analyzing ISR,we modify different training parameters to test the performance of VDSRCNN.Secondly,we add the motion blurred images to the training set to optimize the performance of VDSRCNN.Finally,we use image quality indexes to evaluate the difference between the images from classical methods and VDSRCNN.The results indicate that the VDSRCNN performs better in generating HR images from LR images using the optimized VDSRCNN in a proper method.
基金financially supported by a grant from Research Affairs of Najafabad Branch,Islamic Azad University,Iran
文摘Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Intelligence(AI) based model,each training data set is usually a limited sample of possible patterns of the process and hence,might not show the behavior of whole population.Accordingly,in the present paper,wavelet-based denoising method was used to smooth hydrological time series.Thereafter,small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoised-jittered data sets.Finally,the obtained pre-processed data were imposed into Artificial Neural Network(ANN) and Adaptive Neuro-Fuzzy Inference System(ANFIS)models for daily runoff-sediment modeling of the Minnesota River.To evaluate the modeling performance,the outcomes were compared with results of multi linear regression(MLR) and Auto Regressive Integrated Moving Average(ARIMA)models.The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoffsediment modeling of the case study up to 34%and 25%in the verification phase,respectively.
