A new uncertain linear regression model based on equation deformation

When the observed data are imprecise, the uncertain regression model is more suitable for the linear regression analysis. Least squares estimate can fully consider the given data and minimize the sum of squares of residual error, and can effectively solve the linear regression equation of imprecisely observed data. On the basis of uncertainty theory, this paper presents an equation deformation method for solving unknown parameters in uncertain linear regression equations. We first establish the equation deformation method of one-dimensional linear regression model, and then extend it to the case of multiple linear regression model. We also combine the equation deformation method with Cramer's rule and matrix, and propose the Cramer's rule and matrix elementary transformation method to solve the unknown parameters of the uncertain linear regression equation. Numerical examples show that the equation deformation method can effectively solve the unknown parameters of the uncertain linear regression equation.

Automate Model Transformation From CIM to PIM up to PSM in Model-Driven Architecture

The CIM, PIM, and PSM models are the main levels of the MDA approach. Model transformation is an important step in the MDA process. Indeed, in MDA there are two elementary transformation kinds: CIM to PIM transformation and PIM to PSM transformation. However, most searches propose approaches transforming PIM to PSM, since there are multiple points in common between PIM and PSM. Nevertheless, transforming CIM to PIM is rarely addressed in research because these two levels are mainly different. However, there is not a synthesis work that makes it possible to carry out a model transformation from CIM to PIM towards PSM until obtaining the code. This synthesis methodology allows controlling models transformation from CIM to PIM to PSM, indeed, up to obtaining code according the MDA. This approach makes it possible to limit the intervention of computer scientists in the life cycle of software development. Indeed, this methodology allows modeling only CIM, the business process, and then obtains the source code through successive semi-automatic transformations.

LDPC Decoder of High Speed Multi-Rate DVB-S2 Based on FPGA

A multi-rate LDPC decoder architecture for DVB-S2 codes based on FPGA is proposed. Through elementary transformation on the parity check matrices of DVB-S2 LDPC codes, a new matrix whose left is a QC sub-matrix and right is Transformation of Staircase lower triangular (TST) sub-matrix is obtained. The QC and TST are designed separately, therefore the successful experience of the most popular Quasi-Cyclic (QC) LDPC decoder architecture can be drawn on. While for TST sub-matrix, the variable nodes updating only need to be considered and the check nodes updating is realized compatibility with QC sub-matrix. Based on the proposed architectures, a multi-rate LDPC decoder implemented on Xilinx XC7VX485T FPGA can achieve the maximum decoding throughput of 2.5 Gbit/s at the 20 iterations when the operating frequency is 250 MHz, which demonstrates the highest throughput compared with the state-of-the-art works.

Elementary Transformation of Generalised Inverse Ai- and Least Square Solution of Contradictory Equations Set

A solution for least square generalised inverse Ai- is found by elementary transformation of block matrix, and is further used to solve the contradictory equations set in Article [1].

Design of an Optimal Preview Controller for Linear Discrete-Time Descriptor Noncausal Multirate Systems

The linear discrete-time descriptor noncausal multirate system is considered for the presentation of a new design approach for optimal preview control. First, according to the characteristics of causal controllability and causal observability, the descriptor noncausal system is constructed into a descriptor causal closed-loop system. Second, by using the characteristics of the causal system and elementary transformation, the descriptor causal closed-loop system is transformed into a normal system. Then, taking advantage of the discrete lifting technique, the normal multirate system is converted to a single-rate system. By making use of the standard preview control method, we construct the descriptor augmented error system. The quadratic performance index for the multirate system is given, which can be changed into one for the single-rate system. In addition, a new single-rate system is obtained, the optimal control law of which is given. Returning to the original system, the optimal preview controller for linear discrete-time descriptor noncausal multirate systems is derived. The stabilizability and detectability of the lifted single-rate system are discussed in detail. The optimal preview control design techniques are illustrated by simulation results for a simple example.