In this article, we focus on adaptive linear regression methods and propose a new technique. The article begins with a review of the online passive aggressive algorithm (OPAA), an adaptive linear regression algorithm from the machine learning literature. We highlight the strengths and weaknesses of OPAA and compare it with other popular adaptive regression techniques such as moving window and recursive least squares, recursive partial least squares, and justin-time or locally weighted regression. Modifications to OPAA are proposed to make it more robust and better suited for industrial soft-sensor applications. The new algorithm is called smoothed passive aggressive algorithm (SPAA), and like OPAA, it follows a cautious parameter update strategy but is more robust. The trade-off between SPAA's computation complexity and accuracy can be easily controlled by manipulating just two tuning parameters. We also demonstrate that the SPAA framework is quite flexible and a number of variants are easily formulated. Application of SPAA to estimate the time-varying parameters of a numerically simulated autoregressive with exogenous terms (ARX) model and to predict the Reid vapor pressure of the bottoms flow from an industrial column demonstrates its superior performance over OPAA and comparable performance with the other popular algorithms.
Considering the advantages of multiple inputs multiple outputs (MIMO) over the single input single output system (SISO), authors have developed various techniques to implement the designs for various applications. In this review paper, a brief investigation has been done on the effort done in last decade. In wireless MIMO both the transmitting and the receiving end is equipped with multiple antenna elements. Various authors have exposed the philosophy behind MIMO system. In that they have elaborated that the transmitting antennas and receiving antennas are jointly combined in such a way that fading will decrease and the bit transfer rate will increase so at the receiving end we will get high signal to noise ratio (SNR). At the system level, careful design of MIMO signal processing and coding algorithms can lead to an increase in the capacity and coverage.
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