Convergence analysis of the OJAn MCA learning algorithm by the deterministic discrete time method
Theoretical Computer Science
A Self-Stabilizing Neural Algorithm for Total Least Squares Filtering
Neural Processing Letters
Noisy FIR identification as a quadratic eigenvalue problem
IEEE Transactions on Signal Processing
On the discrete-time dynamics of a class of self-stabilizing MCA extraction algorithms
IEEE Transactions on Neural Networks
A self-stabilizing MSA algorithm in high-dimension data stream
Neural Networks
Convergence analysis for feng's MCA neural network learning algorithm
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Widrow (1971) proposed the least mean squares (LMS) algorithm, which has been extensively applied in adaptive signal processing and adaptive control. The LMS algorithm is based on the minimum mean squares error. On the basis of the total least mean squares error or the minimum Raleigh quotient, we propose the total least mean squares (TLMS) algorithm. The paper gives the statistical analysis for this algorithm, studies the global asymptotic convergence of this algorithm by an equivalent energy function, and evaluates the performances of this algorithm via computer simulations