Nonlinear Recursive Least Square Research Articles

A Robust Nonlinear RLS Type Adaptive Filter for Second-Order-Intermodulation Distortion Cancellation in FDD LTE and 5G Direct Conversion Transceivers

Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stop-band isolation. This TxL signal in combination with the second-order nonlinearity of the receive mixer may lead to a baseband (BB) second-order intermodulation distortion (IMD2) with twice the transmit signal bandwidth. In direct conversion receivers, this nonlinear IMD2 interference may cause a severe signal-to-interference-plus-noise ratio degradation of the wanted receive signal. This contribution presents a nonlinear Wiener model recursive least-squares (RLS) type adaptive filter for the cancellation of the IMD2 interference in the digital BB. The included channel-select-, and DC-notch filter at the output of the proposed adaptive filter ensure that the provided IMD2 replica includes the receiver front-end filtering. A second, robust version of the nonlinear RLS algorithm is derived which provides numerical stability for highly correlated input signals which arise in e.g. LTE-A intra-band multi-cluster transmission scenarios. The performance of the proposed algorithms is evaluated by numerical simulations and by measurement data.

Online distributed fuzzy modeling of nonlinear PDE systems: Computation based on adaptive algorithms

With the emergence of novel model-based controllers for partial differential equation (PDE) systems, identifying the mathematical model of PDE systems has become a promising and complicated research topic. This paper suggests a new method to identify an adaptive Takagi–Sugeno (TS) fuzzy PDE model for nonlinear multi-input multi-output (MIMO) first-order PDE systems. The proposed approach is performed online based on the measured input and output data of the nonlinear PDE systems. Furthermore, the identification process will be obtained for the cases that the noise is either white or colored. For the case of white noise, a nonlinear recursive least square (NRLS) approach is applied to identify the nonlinear system. On the other hand, when the colored noise is exerted to the nonlinear PDE system, the fuzzy PDE model of the nonlinear PDE system and also nonlinear colored noise are identified based on the nonlinear extended matrix methods (NEMM). Moreover, the problem of identification for both colored and white noise cases is investigated when premise variables of membership functions are known or unknown. Finally, in order to illustrate the effectiveness and merits of the proposed methods, the identification method is applied to a practical nonisothermal Plug-Flow reactor (PFR) and a hyperbolic PDE system with Lotka–Volterra type applications. As it is expected, the evolutions of the error between the state variables for the obtained TS fuzzy PDE model and the output data converge to the zero in the steady-state conditions. Thus one concludes, the proposed identification algorithm can accurately adjust both consequents and antecedents parameters of TS fuzzy PDE model.

Online estimation of fouling development for SWRO system using real data

Fouling is one of the most serious problems in seawater desalination using reverse osmosis (SWRO) which is important to lower the production cost of desalinated water. For efficient operation of the SWRO plant, accurate models are crucial to decide the optimal operating condition and cleaning operation. The model for RO membrane system has been studied by many researchers and its usefulness has been increased as its accuracy has been improved. However, the prediction of fouling is difficult due to its extremely complicated mechanism of growth involving various foulants. In this study, the development of the fouling is predicted by monitoring the membrane resistance across the membrane and the friction coefficient through spiral-wound membrane from the operation data. Using nonlinear recursive least-squares (NRLS) method, the two properties are identified from the operating data based on the one-dimensional model of the SWRO system. By this method, the degree of fouling can be inferred from the two properties and potential cleaning time can be predicted. It can also assess the effectiveness of membrane cleaning. Using the actual data of an existing plant, the trends of resistance and friction coefficient could be monitored and the effect of cleaning pattern of the real plant can be evaluated by recursive parameter estimation online.

Nonlinear active noise control using EKF-based recurrent fuzzy neural networks

Active Noise Control (ANC) system is commonly designed and implemented using adaptation algorithm and adaptive control structure. In this paper we present theoretical and experimental result of active noise control system using Recurrent Fuzzy Neural Network (RFNN). RFNN is developed by combining fuzzy logic and neural networks, aimed at producing better control system performance than if we use neural network or fuzzy logic separately. Using a control structure with two multilayer feedforward RFNNs (one RFNN serves as a nonlinear controller while the other one operates as a nonlinear plant model), a recursive least-squares algorithm based on Adjoint Extended Kalman Filter approach is employed for the training of the controller network. Extended Kalman Filter (EKF) algorithm is introduced to develop a new algorithm with faster convergence speed by using nonlinear recursive-least square method. Experimental result using DSP demonstrates effectiveness of the proposed RFNN structure and algorithm to attenuate unwanted noise.

A nonlinear recursive least squares algorithm for crosstalk‐resistant noise canceler

AbstractIn this paper, a method of updating filter coefficients is proposed in which the RLS (Recursive Least Squares) algorithm is applied to a noise canceler with a filter for crosstalk removal. The RLS algorithm has an advantage over the Least Mean Square algorithm by virtue of better convergence. When the filter coefficients are updated including the filter for crosstalk removal, the error signal becomes nonlinear in terms of filter coefficients. Hence, a nonlinear RLS algorithm is derived in which such nonlinearity is taken into account. It is shown that the convergence is further improved in comparison with the conventional RLS algorithm. Finally, a method is proposed to reduce the noise contained in the transmitted voice signals of telephone by means of the present noise canceler and its effectiveness is evaluated. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(5): 36–44, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10039

Nonlinear RLS algorithm for amplitude estimation in class A noise

An adaptive nonlinear recursive least square (RLS) algorithm for amplitude estimation in class A noise is presented. For Gaussian input signal and class A noise, its mean and mean-square behaviours are studied. It is shown that the linear RLS and nonlinear RLS algorithm with the clipper function are stable in the mean and mean square. For non-Gaussian input, amplitude estimation in CDMA communication is presented. Simulation results show that the nonlinear RLS can provide good performance close to the Cramer–Rao bound and outperform the nonlinear LMS and the conventional RLS in impulse noise.