least squaresreinforcement learningtemporal differenceWe propose a new reinforcement learning method in the framework of Recursive Least Squares-Temporal Difference (RLS-TD). Instead of using the standard mechanism of eligibility traces (resulting in RLS-TD()), we propose to use the forgetting factor ...
3. Main Contribution In this paper, a new control algorithm based on recursive least squares (RLS) is proposed, which uses the variable forgetting factor to obtain the optimal convergence rate. This method offers a faster convergence rate and robustness with system parameters than the fixed-step ...
A novel recursive MOESP subspace identification algorithm based on forgetting factor. Con- trol Theory Appl 2009; 26: 69-72.H. Yang, S. Y. Li. A novel recursive MOESP subspace iden- tification algorithm based on forgetting factor. Journal of Control Theory and Applications, vol. 26, no....
英文: A kind of arithmetic of recursive least squares (RLS) method with forgetting factor combined with model matching of zero frequency was adopted to identify the object online. 中文: 采用了带遗忘因子的递推最小二乘和基于零频率的模型匹配的联合辨识算法对空调系统进行在线辨识。 更详细... 英文...
Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor for systems with bounded noise F. Huang,“Asymptotically convergent modified recursive least squares with data dependent updating and forgetting factor for systems with bounded noise,” IEEE... S ...
ForgettingFactor— Forgetting factor for parameter estimation 1 (default) | scalar in the range (0, 1] EnableAdapation— Option to enable or disable parameter estimation true (default) | false DataType— Floating point precision of parameters Read-only: 'double' (default) | 'single' ProcessNoise...
ForgettingFactor— Forgetting factor for parameter estimation 1 (default) | scalar in the range (0, 1] EnableAdapation— Option to enable or disable parameter estimation true (default) | false DataType— Floating point precision of parameters 'double' (default) | 'single' ProcessNoiseCovariance—...
obj = recursiveOE; obj.ForgettingFactor = 0.99; B Estimated coefficients of polynomial B(q), returned as a vector of real values specified in order of ascending powers of q-1. B is a read-only property and is initially empty after you create the object. It is populated after you use ...
1.A kind of arithmetic of recursive least squares(RLS) method with forgetting factor combined with model matching of zero frequency was adopted to identify the object online.采用了带遗忘因子的递推最小二乘和基于零频率的模型匹配的联合辨识算法对空调系统进行在线辨识。
rls = dsp.RLSFilter(11,'ForgettingFactor', 0.98); [y,e] = rls(x,d); w = rls.Coefficients; Plot the results The output signal matches the desired signal, making the error between the two close to zero. plot(1:1000, [d,y,e]); title('System Identification of an FIR filter'); ...