Adaptive noise cancellation for a class of nonlinear, dynamic reference channels

Stapleton, J.; Bass, S.

doi:10.1109/tcs.1985.1085678

Cited by 87 publications

(19 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This kind of constraints can also be handled by the present method. More precisely, taking into consideration of (55), and letting…”

Section: Remark 2 Both the Internal Matrices L T P E La N Dl T E T Xmentioning

confidence: 99%

Model Predictive Control for Block-oriented Nonlinear Systems with Input Constraints

Zhang¹

2011

Advanced Model Predictive Control

View full text Add to dashboard Cite

“…This kind of constraints can also be handled by the present method. More precisely, taking into consideration of (55), and letting…”

Section: Remark 2 Both the Internal Matrices L T P E La N Dl T E T Xmentioning

confidence: 99%

Model Predictive Control for Block-oriented Nonlinear Systems with Input Constraints

Zhang¹

2011

Advanced Model Predictive Control

View full text Add to dashboard Cite

“…In real situations, the environment between x(t) and c(t) or x(t) and c 1 (t) is so complex that c(t) or c 1 (t) is in fact a nonlinear function of x(t) [26][27][28]. Higher performance of echo cancellation by using a nonlinear filter can thus be expected.…”

Section: Echo Cancellationmentioning

confidence: 99%

Echo Cancellation in Cellphone Using Neurofuzzy Filter

Ansari

Gupta²

2010

2010 International Conference on Advances in Computer Engineering

View full text Add to dashboard Cite

Computational intelligence combines neural network, fuzzy systems and evolutionary computing. In this manuscript a neurofuzzy filter (NFF) is presented, which is based on fuzzy if-then rules (structure learning) and the tuning of the parameters of membership function (parameter learning). In the structure learning, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning, the consequent parameters are tuned optimally by either least mean square (LMS) or recursive least squares (RLS) algorithms and the pre condition parameters are tuned by backpropagation algorithm. Both the structure and parameter learning are performed simultaneously as the adaptation proceeds. Simulation for echo cancellation in cellphone is performed. Good performance is achieved by applying the NFF to echo cancellation on a cellphone.

show abstract

“…Applications of Volterra series include physiological modeling [2], automatic control [3], nonlinear device modeling [4], [5]. Volterra-based structures of polynomial filters have also proven very efficient in signal processing applications [6]- [9]. This letter explores the possibility of identifying nonlinear systems under equality constraints using discrete-time Volterra-Laguerre expansions.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Equality Constraints in the Identification of Volterra–Laguerre Models

Diouf

Telescu

Cloastre

et al. 2012

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

show abstract

Adaptive noise cancellation for a class of nonlinear, dynamic reference channels

Cited by 87 publications

References 10 publications

Model Predictive Control for Block-oriented Nonlinear Systems with Input Constraints

Model Predictive Control for Block-oriented Nonlinear Systems with Input Constraints

Echo Cancellation in Cellphone Using Neurofuzzy Filter

On the Use of Equality Constraints in the Identification of Volterra–Laguerre Models

Contact Info

Product

Resources

About