Based on dichotomous coordinate descent (DCD) iterations and with the use of the variable forgetting factor (VFF), a widely linear (WL) l1-norm recursive least squares (RLS) adaptive filtering algorithm is proposed fo...
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Based on dichotomous coordinate descent (DCD) iterations and with the use of the variable forgetting factor (VFF), a widely linear (WL) l1-norm recursive least squares (RLS) adaptive filtering algorithm is proposed for sparse underwater acoustic channelequalization. In the proposed l1-norm WL-RLS algorithm with VFF, the WL model is employed to exploit the second order statistics of the non-circular signals and the VFF is employed to improve the tracking ability of the RLS algorithms. DCD iterations are incorporated in the proposed l1-norm WL-RLS-DCD algorithm with VFF to reduce the computing complexity. Moreover, the proposed algorithms are employed by the direct adaptive decision feedback equalizer (DA-DFE). Numerical results indicate that compared with the conventional RLS, l1-norm RLS, WL-RLS with VFF, l1-norm WL-RLS-DCD algorithms, the proposed algorithms achieve a better performance in terms of the convergence rate, mean square errorand symbol error ratein the DA-DFE receiver. Experimental results also show that the proposed algorithms can promote the DA-DFE receiver to obtain a better performance in the sparse time-varying underwater acoustic communication system. Even though the transmitted signals are circular quadrature phase-shift keying (QPSK) through the underwater acoustic channel, the proposed adaptive RLS algorithms can still obtain a better performance.
Data streams online modeling and prediction is an important research direction in the field of data mining. In practical applications, data streams are often of nonstationary nature and containing outliers, hence an o...
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Data streams online modeling and prediction is an important research direction in the field of data mining. In practical applications, data streams are often of nonstationary nature and containing outliers, hence an online learning algorithm with dynamic tracking capability as well as anti-outlier capability is urgently needed. With this in mind, this paper proposes a novel robust adaptive online sequential extreme learning machine (RA-OSELM) algorithm for the online modeling and prediction of nonstationary data streams with outliers. The RA-OSELM is developed from the famous online sequential extreme learning machine algorithm, but it uses a more robust M-estimation loss function to replace the conventional least square loss function so as to suppress the incorrect online update of the learning algorithm with respect to outliers, and hence enhances its robustness in the presence of outliers. Moreover, the RA-OSELM adopts a variable forgetting factor method to automatically track the dynamic changes of the nonstationary data streams and timely eliminate the negative impacts of the outdated data, so it tends to produce satisfying tracking results in nonstationary environments. The performances of RA-OSELM are evaluated and compared with other representative algorithms with synthetic and real data sets, and the experimental results indicate that the proposed algorithm has better adaptive tracking capability with stronger robustness than its counterparts for predicting nonstationary data streams with outliers.
Exponentially weighted moving average (EWMA) is a commonly used model-based algorithm for semiconductor manufacturing process Run-to-Run (R2R) control. However, it's very difficult to set up the mathematical model...
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ISBN:
(纸本)9781467313988
Exponentially weighted moving average (EWMA) is a commonly used model-based algorithm for semiconductor manufacturing process Run-to-Run (R2R) control. However, it's very difficult to set up the mathematical modeling for the actual semiconductor manufacturing process. Therefore, data-based methods such as Recursive Least Squares(RLS) have received wide attention nowadays. This paper proposes a variable forgetting factor RLS R2R control approach for semiconductor manufacturing process with and without drift disturbance. The variable forgetting factor resolves the drift disturbance well, and this method is much superior to generic RLS algorithm in convergence speed and tracking effect. It has both a strong ability to track parameters, and a small convergence estimate error. Simulation results prove the feasibility and accuracy of the algorithm.
Accurate state-of-charge (SOC) estimation is essential to battery management system. The widely adopted estimation methods based on Kalman Filter (KF) fail to take the variable environmental conditions into considerat...
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ISBN:
(纸本)9781509061990
Accurate state-of-charge (SOC) estimation is essential to battery management system. The widely adopted estimation methods based on Kalman Filter (KF) fail to take the variable environmental conditions into consideration, which may result in a poor accuracy. This paper proposes a novel estimation model based on KF method to estimate SOC of Lithium-ion battery. In the proposed model, the noise variances are optimized for the system current state at each iteration, a variable forgetting factor is introduced to improve the algorithm's convergence and accuracy of estimation, and the artificial neural network (ANN) is applied for the measurement equation of KF. The experiments, based on Lithium-ion Battery set of NASA, show that the proposed SOC estimation model is valid and can improve the algorithm performance and accuracy and robustness.
The state of charge(SOC) and state of health(SOH) are essential indicators for estimating the performance of lithium-ion *** most of the existing methods to estimate SOC and SOH through step-by-step calculation may br...
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The state of charge(SOC) and state of health(SOH) are essential indicators for estimating the performance of lithium-ion *** most of the existing methods to estimate SOC and SOH through step-by-step calculation may bring obstacles to real-time prediction of battery *** adapt the complex and dynamic situation of the batteries and estimate SOC and SOH in an accurate and fast manner,a novel multi-time scale joint online estimation method is *** order to quickly identify the battery model and estimate the battery state,SOC and SOH are evaluated on a multi time scale framework based on extended Kalman filter(EKF).To improve the accuracy of the equivalent circuit model(ECM),a variable forgetting factor recursive least square(VFFRLS) method is introduced to identify the internal parameters in the battery model.A fuzzy variable time scale EKF(FVEKF) is proposed to estimate SOC and SOH online,where the fuzzy inference engine change the time scale to increase the convergence speed especially in complex stress *** from the University of Maryland is adopted to testify the effectiveness and efficiency of the *** results demonstrate that the method has better estimation accuracy and efficiency comparing to traditional joint estimation method,and meet the requirements of real-time estimation.
In this paper,the problem of the parameter estimation for Wiener systems with saturation nonlinearity is *** gradient(SG) and modified stochastic gradient identification algorithms are *** to the special structure of ...
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In this paper,the problem of the parameter estimation for Wiener systems with saturation nonlinearity is *** gradient(SG) and modified stochastic gradient identification algorithms are *** to the special structure of the saturation nonlinearity,a switching function is employed to transform the expression of the nonlinearity so that all the unknown parameters are included in one *** the system model is transformed as a regression *** order to obtain the unknown intermediate variables,the auxiliary model technique is applied to construct an auxiliary model and then the unknown inner variables in the information vector are replaced with the outputs of the auxiliary *** improve the convergence rate of the SG algorithm,an auxiliary model-based variable forgetting factor SG algorithm is *** assigning a variable forgetting factor,the proposed algorithm can generate more accurate parameter estimates and owns faster convergence rate than the traditional SG *** numerical simulation example is provided to further verify the effectiveness of the proposed algorithms.
Soft Sensors (SSs) have been widely investigated and employed as inferential sensing systems for providing on-line estimations of industrial processes' variables. However, industrial processes suffer from differen...
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Soft Sensors (SSs) have been widely investigated and employed as inferential sensing systems for providing on-line estimations of industrial processes' variables. However, industrial processes suffer from different complex characteristics (e.g. time-variance and non-linearity), being very difficult for the SS models to perform well over time. This paper proposes a SS model using an on-line Extreme Learning Machine (ELM) with Directional forgettingfactor (DFF) which is able to provide on-line estimations of variables in industrial processes. The main contribution is that the proposed ELM model has the ability of adapting its architecture over time. For this purpose, it is used the Bordering Method and the Reverse Bordering Method. Experiments demonstrate the performance of the proposed method over the state-of-the-art methods.
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