Plant identification via adaptive combination of transversal filters

Arenas‐García, Jerónimo; Martínez‐Ramón, Manel; Navia-Vázquez, Ángel; Figueiras-Vidal, Anı́bal R.

doi:10.1016/j.sigpro.2005.11.008

Cited by 94 publications

(54 citation statements)

References 17 publications

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“…The ensembles are combined in such a manner that the advantages of both of them are kept: the rapid convergence from the fast low diversity ensemble, and the reduced steadystate error from the highly diverse one. In analogy of a well-known neurological fact: human brains combine fast and coarse reactions against abrupt changes in the environment, with an early processing at the amygdala, and more elaborated but slower responses taken in the neocortex at a conscious level [8].…”

Section: A Solution For Concept Driftmentioning

confidence: 99%

Generalized CMAC adaptive ensembles for concept-drifting data streams

Gonzalez-Serrano

Figueiras-Vidal

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper we propose to use an adaptive ensemble learning framework with different levels of diversity to handle streams of data in non-stationary scenarios in which concept drifts are present. Our adaptive system consists of two ensembles, each one with a different level of diversity (from high to low), and, therefore, with different and complementary capabilities, that are adaptively combined to obtain an overall system of improved performance. In our approach, the ensemble members are generalized CMACs, a linear-in-the-parameters network. The ensemble of CMACs provides a reasonable trade-off between expressive power, simplicity, and fast learning speed.At the end of the paper, we provide a performance analysis of the proposed learning framework on benchmark datasets with concept drifts of different levels of severity and speed.

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Section: A Solution For Concept Driftmentioning

confidence: 99%

Generalized CMAC adaptive ensembles for concept-drifting data streams

Gonzalez-Serrano

Figueiras-Vidal

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Numerous different methods exist, ranging from heuristic algorithms [3], [20] to theory based approaches, e.g. [11].…”

Section: Sliding Window Bayesian Model Averagingmentioning

confidence: 99%

“…Typically, filters with different update pace are merged to benefit from each filter's specific change responsiveness respectively steady state behaviour [3].…”

Section: Introductionmentioning

confidence: 99%

Receding horizon prediction by bayesian combination of multiple predictors

Ståhl

Johansson

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Abstract-This paper presents a novel online approach of merging multiple different predictors of time-varying dynamics into a single optimized prediction. Different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on two different cases of data with shifting dynamics; one example of prediction using several approximate models of a linear system and one case of glucose prediction on a non-linear physiologically based simulated type I diabetes data using several parallel linear predictors. The performance of the combined prediction significantly reduced the total prediction error compared to each predictor in each example.

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“…Such adaptive scheme has been successfully applied to other acoustic applications, such as acoustic echo cancellation [18] [19] and ANC [20][21] [16]. As it is shown in these works, any other algorithm can be used instead of the LMS-type algorithms, as appropriate.…”

Section: Convex Combination Of Adaptive Filtersmentioning

confidence: 99%

“…The Fx-NLMS weights are updated at each iteration from (2.4) and (2.18) according to 19) where w(n) is the adaptive weight vector of L w -length. x f (n) is a vector containing the last L w samples of the input signal x(n) filtered through the L h -length estimated impulse responseĥ.…”

Section: Adaptive Equalizationmentioning

confidence: 99%

Linear and nonlinear room compensation of audio rendering systems

Criado¹

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Common audio systems are designed with the intent of creating real and immersive scenarios that allow the user to experience a particular acoustic sensation that does not depend on the room he is perceiving the sound. However, acoustic devices and multichannel rendering systems working inside a room, can impair the global audio effect and thus the 3D spatial sound.In order to preserve the spatial sound characteristics of multichannel rendering techniques, adaptive filtering schemes are presented in this dissertation to compensate these electroacoustic effects and to achieve the immersive sensation of the desired acoustic system. Adaptive filtering offers a solution to the room equalization problem that is doubly interesting. First of all, it iteratively solves the room inversion problem, which can become computationally complex to obtain when direct methods are used. Secondly, the use of adaptive filters allows to follow the time-varying room conditions.In this regard, adaptive equalization (AE) filters try to cancel the echoes due to the room effects. In this work, we consider this problem and propose effective and robust linear schemes to solve this equalization problem by using adaptive filters. To do this, different adaptive filtering schemes are introduced in the AE context. These filtering schemes are based on three strategies previously introduced in the literature: the convex combination of filters, the biasing of the filter weights and the block-based filtering. More specifically, and motivated by the sparse nature of the acoustic impulse response and its corresponding optimal inverse filter, we introduce different adaptive equalization algorithms.In addition, since audio immersive systems usually require the use of multiple transducers, the multichannel adaptive equalization problem should be also taken into account when new single-channel approaches are presented, in the sense that they can be straightforwardly extended to the ii Abstract multichannel case.On the other hand, when dealing with audio devices, consideration must be given to the nonlinearities of the system in order to properly equalize the electroacoustic system. For that purpose, we propose a novel nonlinear filtered-x approach to compensate both room reverberation and nonlinear distortion with memory caused by the amplifier and loudspeaker devices.Finally, it is important to validate the algorithms proposed in a realtime implementation. Thus, some initial research results demonstrate that an adaptive equalizer can be used to compensate room distortions. ResumenLos sistemas de audio actuales están diseñados con la idea de crear escenarios reales e inmersivos que permitan al usuario experimentar determinadas sensaciones acústicas que no dependan de la sala o situación donde se esté percibiendo el sonido. Sin embargo, los dispositivos acústicos y los sistemas multicanal funcionando dentro de salas, pueden perjudicar el efecto global sonoro y de esta forma, el sonido espacial 3D.Para poder preservar las características espaciales ...

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Plant identification via adaptive combination of transversal filters

Cited by 94 publications

References 17 publications

Generalized CMAC adaptive ensembles for concept-drifting data streams

Generalized CMAC adaptive ensembles for concept-drifting data streams

Receding horizon prediction by bayesian combination of multiple predictors

Linear and nonlinear room compensation of audio rendering systems

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