Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.

Recursive Least Squares with Linear Constraints

Abstract: Recursive Least Squares (RLS) algorithms have wide-spread applications in many areas, such as real-time signal processing, control and communications. This paper shows that the unique solutions to linear-equality constrained and the unconstrained LS problems, respectively, always have exactly the same recursive form. Their only difference lies in the initial values. Based on this, a recursive algorithm for the linear-inequality constrained LS problem is developed. It is shown that these RLS solutions converge to the true parameter that satisfies the constraints as the data size increases. A simple and easily implementable initialization of the RLS algorithm is proposed. Its convergence to the exact LS solution and the true parameter is shown. The RLS algorithm, in a theoretically equivalent form by a simple modification, is shown to be robust in that the constraints are always guaranteed to be satisfied no matter how large the numerical errors are. Numerical examples are provided to demonstrate the validity of the above results.

Load Forecasting and Operation Strategy Design for CCHP Systems Using Forecasted Loads

Abstract: Operation strategy of combined cooling, heating, and power (CCHP) systems is designed to collect users’ load information to determine the energy input to the system and power flow inside the system. Most of the current operation strategies are designed by assuming that accurate loads during the next time interval are already known. To solve the problem of unknown loads in practical applications, using an autoregressive moving average with exogenous inputs model, whose parameters are identified by an ordinary least squares–two-stage recursive least squares (TSRLS) algorithm, cooling, heating, and electrical loads in the future time intervals are forecasted. The identification procedure uses the dew-point temperature as the instrumental variable for the exogenous variable (dry-bulb temperature), to better characterize the relationship between exogenous and endogenous variables. TSRLS helps to reduce the space and time complexity. A poststrategy is also proposed to compensate for the inaccurate forecasting. A case study is conducted to verify the feasibility and effectiveness of the proposed methods.

Comparison of weighted least squares and extended Kalman filtering methods for dynamic identification of robots

Abstract: This paper presents a comparison of two methods for robot dynamic identification which include the weighted least squares estimation and the extended Kalman filtering. Comparative experimental results and discussion are presented for a SCARA robot.

Identification of Time Varying Systems and Application of System Identification to Signal Processing

Abstract: Part IA new approach to identification of time varying systems is presented, and evaluated using computer simulations. The new approach is built upon the similarities between recursive least squares identification and Kalman filtering.The parameter variations are modelled as process noise in a state space model and then identified using adaptive Kalman filtering. A method for adaptive Kalman filtering is derived and analysed. The simulations indicate that this new approach is superior to previous methods based on adjusting the forgetting factor. This improvement is however gained at the price of a signification increase in computational complexity.Part IIIn this part we apply parameter estimation to the problem of transmission line protection.One approach based on recursive least squares identification is presented. The method has ben tested using simulated data generated by the program EMTP.Another approach based on the theory of travelling waves is also discussed.Part IIIIn this part a method for input estimation or deconvolution is presented. The basis of the method is to use a parametrized model the input signal. To use the method we should thus be able to express the input signal as a function of some unknown parameters and time. The algorithms simultaneously estimates the parameters of the input signal and the parameters of the system transfer function. The presentation here is restricted to transfer functions of all pole type, i.e. ARX-models. The method can be extended to handle zeros in the transfer function. The computational burden would however increase significantly. The algorithm uses efficient numerical methods, as for instance QR-factorization thorugh Householder transformation.The algorithm is in this paper applied to a problem in speech coding. It has been observed that the quality of synthesized speech can be improved, if a more detailed model than an impulse train is used for the pitch pulses, see Fant (1980). It is here shown how the method presented in this paper can be used to estimate the system parameters of the speech production and the parameters of the glottal pulse simultaneously.