Adaptive Signal Processing for Adaptive Control

Widrow, Bernard; Walach, Eugene

doi:10.1016/s1474-6670(17)62348-6

Cited by 293 publications

(427 citation statements)

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“…where λ is the maximum value that responding to S can attain given the applied reinforcer, and α mainly regulates the speed of learning (Widrow & Stearns, 1985). Equation (2) is capable, through repeated applications, of establishing a different response to each of many stimuli, provided the stimulus representations satisfy certain technical requirements ("linear separability", Hsiung & Mao, 1998;Haykin, 1999).…”

Section: Modelmentioning

confidence: 99%

“…Equation (2) is capable, through repeated applications, of establishing a different response to each of many stimuli, provided the stimulus representations satisfy certain technical requirements ("linear separability", Hsiung & Mao, 1998;Haykin, 1999). I refer the reader to the literature for further details (Blough, 1975;Haykin, 1999;Widrow & Stearns, 1985). Weights are assumed to have a value of zero at the 0 0.5…”

Section: Modelmentioning

confidence: 99%

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Retrospective Revaluation as Simple Associative Learning.

Ghirlanda

2005

Journal of Experimental Psychology: Animal Behavior Processes

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Backward blocking, unovershadowing, and backward conditioned inhibition are examples of retrospective revaluation phenomena that have been suggested to involve more than simple associative learning. Models of these phenomena have thus used additional concepts, for example, appealing to attentional effects or more elaborate learning mechanisms. The author shows that a suitable representation of stimuli, paired with a careful analysis of the discriminations faced by animals, leads to an account of these and other phenomena in terms of a simple elemental model of associative learning, with essentially the same learning mechanism as the R. A. Rescorla and A. R. Wagner (1972) model. The author concludes with a discussion of some implications for theories of learning.

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Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Retrospective Revaluation as Simple Associative Learning.

Ghirlanda

2005

Journal of Experimental Psychology: Animal Behavior Processes

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“…The essential and principal property of these systems is its time-varying, self-adjusting performance [1].…”

Section: Introductionmentioning

confidence: 99%

Análisis matemático al Método D'Hondt de asignación de escaños parlamentarios

Tupacyupanqui¹

2012

ings

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This paper presents a complete analysis of the cascade structure for adaptive transversal filters based on adaptive algorithms. The standard structure of the cascade transversal FIR filter is obtained by replacing the whole structure by small ones with the same impulse response but having a less number of taps than the original structure. Computer simulation result shows the validity, reliability and the limitations that the model could have in its capacity of prediction. The optimal values of the model are compared with those obtained by the standard least mean square and recursive least square adaptive algorithms in order to verify the convergence of the weights and determine how fast this structure achieves those weights. For this case the speed of the algorithm is determined by the number of iterations that the filter requires to reach the minimum square value of its learning curve.Keywords: Cascade structures, Finite impulse response filters, Transversal filters, Linear estimation, LMS algorithm, RLS algorithm. ResumenEste artículo presenta un análisis completo de la estructura en cascada para filtros transversales basados en algoritmos adaptativos. La estructura general de un filtro transversal FIR en cascada se obtiene reemplazando toda la estructura por pequeñas secciones independientes de filtros de la misma respuesta al impulso pero con un número inferior de pesos al original. Los resultados de la simulación en computadora muestran la validez, capacidad y las limitaciones que el modelo podría tener en su capacidad de predicción. Los valores óptimos del modelo son comparados con aquellos obtenidos por los algoritmos adaptativos convencionales de mínimo cuadrado medio y mínimo cuadrado medio recursivo para verificar la convergencia de los pesos y determinar qué tan rápido la estructura alcanza dichos pesos. En este caso la rapidez del algoritmo se determina en base al número de iteraciones que el filtro requiere para llegar al valor mínimo cuadrado de su curva de aprendizaje.Palabras clave: estructura en cascada, filtros de respuesta al impulso finita, filtros transversales, estimación lineal, algoritmo LMS, algoritmo RLS.

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“…Ključne riječi: adaptivni filtar, promjenjiva duljina koraka, selektivno parcijalno osvježavanje, pojasni adaptivni filtar Adaptive filtering has been, and still is, an area of active research that plays an active role in an ever increasing number of applications, such as noise cancellation, channel estimation, channel equalization and acoustic echo cancellation [1], [2], [3], [4], [5]. The least mean squares (LMS) and its normalized version (NLMS) are the workhorses of adaptive filtering.…”

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confidence: 99%

The New Normalized Subband Adaptive Filter Algorithms with Variable Step-Size

Abadi

Shafiee

2014

Automatika

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Original scientific paperThis paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm. In the proposed VSS-NSAF, the step-size changes in order to have largest decrease in the mean square deviation (MSD) for sequential iterations. To reduce the computational complexity of VSS-NSAF, the variable step-size selective partial update normalized subband adaptive filter (VSS-SPU-NSAF) is proposed. In this algorithm the filter coefficients are partially updated in each subband at every iteration. Simulation results show the good performance of the proposed algorithms in convergence speed and steady-state MSD.

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Adaptive Signal Processing for Adaptive Control

Cited by 293 publications

References 9 publications

Retrospective Revaluation as Simple Associative Learning.

Retrospective Revaluation as Simple Associative Learning.

Análisis matemático al Método D'Hondt de asignación de escaños parlamentarios

The New Normalized Subband Adaptive Filter Algorithms with Variable Step-Size

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