A hybrid model for predicting spatial distribution of soil organic matter in a bamboo forest based on general regression neural network and interative algorithm

A general regression neural network model,combined with an interative algorithm(GRNNI)using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and variability of soil organic matter(SOM)in a bamboo forest.The auxiliary environmental variables were:elevation,slope,mean annual temperature,mean annual precipitation,and normalized difference vegetation index.The prediction accuracy of this model was assessed via three accuracy indices,mean error(ME),mean absolute error(MAE),and root mean squared error(RMSE)for validation in sampling sites.Both the prediction accuracy and reliability of this model were compared to those of regression kriging(RK)and ordinary kriging(OK).The results show that the prediction accuracy of the GRNNI model was higher than that of both RK and OK.The three accuracy indices(ME,MAE,and RMSE)of the GRNNI model were lower than those of RK and OK.Relative improvements of RMSE of the GRNNI model compared with RK and OK were 13.6%and 17.5%,respectively.In addition,a more realistic spatial pattern of SOM was produced by the model because the GRNNI model was more suitable than multiple linear regression to capture the nonlinear relationship between SOM and the auxiliary environmental variables.Therefore,the GRNNI model can improve both prediction accuracy and reliability for determining spatial distribution and variability of SOM.

Joint receiving mechanism based on blind equalization with variable step size for M-QAM modulation

The problem of inter symbol interference（ ISI） in wireless communication systems caused by multipath propagation when using high order modulation like M-Q AMis solved. Since the wireless receiver doesn＇t require a training sequence,a blind equalization channel is implemented in the receiver to increase the throughput of the system. To improve the performances of both the blind equalizer and the system,a joint receiving mechanismincluding variable step size（ VSS） modified constant modulus algorithms（ MC-MA） and modified decision directed modulus algorithms（ MD DMA） is proposed to ameliorate the convergence speed and mean square error（ MSE） performance and combat the phase error when using high order QAM modulation. The VSS scheme is based on the selection of step size according to the distance between the output of the equalizer and the desired output in the constellation plane. Analysis and simulations showthat the performance of the proposed VSS-MCMA-MD DMA mechanismis better than that of algorithms with a fixed step size. In addition,the MCMA-MDDMA with VSS can performthe phase recovery by itself.