Identification of systems using binary sensors via Support Vector Machines

Goudjil, Abdelhak; Pouliquen, Mathieu; Pigeon, Eric; Gehan, Olivier; M’Saad, M.

doi:10.1109/cdc.2015.7402729

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…, N . For the original updates (15) in Algorithm 2 which uses the delayed information c u, j(n,j) , the situation can be considered as a case of the asynchronous stochastic approximation procedure of [38,Sec. 5.6].…”

Section: E Proof Of Theorem 32mentioning

confidence: 99%

On the identification of FIR systems with binary input and output observations

Leong

Weyer

Nair

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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This paper considers the identification of FIR systems, where information about the inputs and outputs of the system undergoes quantization into binary values before transmission to the estimator. In the case where the thresholds of the input and output quantizers can be adapted, but the quantizers have no computation and storage capabilities, we propose identification schemes which are strongly consistent for Gaussian distributed inputs and noises. This is based on exploiting the correlations between the quantized input and output observations to derive nonlinear equations that the true system parameters must satisfy, and then estimating the parameters by solving these equations using stochastic approximation techniques. If, in addition, the input and output quantizers have computational and storage capabilities, strongly consistent identification schemes are proposed which can handle arbitrary input and noise distributions. In this case, some conditional expectation terms are computed at the quantizers, which can then be estimated based on binary data transmitted by the quantizers, subsequently allowing the parameters to be identified by solving a set of linear equations. The algorithms and their properties are illustrated in simulation examples.

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Section: E Proof Of Theorem 32mentioning

confidence: 99%

On the identification of FIR systems with binary input and output observations

Leong

Weyer

Nair

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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“…A technique founded on binary observation has been employed in a wide range of applications in industries and telecommunication, such as in chemical process sensors for a vacuum, the asynchronous transfer mode (ATM), pressure switches, liquid levels, and industrial sensors for brushless DC motors, shifting using a wire in automotive applications, and switching sensors for exhaust-gas oxygen ABS [7,[25][26][27]. One of the most important problems in this wide range of applications is parameter estimation for systems with binary (or quantized) outputs [7,8,[28][29][30][31][32]. Some examples of this field include sensor networks, telecommunications, and networked control systems.…”

Section: Introductionmentioning

confidence: 99%

Channel Identification Based on Cumulants, Binary Measurements, and Kernels

Oualla

Fateh

Darif

et al. 2021

Systems

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In this paper, we discuss the problem of channel identification by using eight algorithms. The first three algorithms are based on higher-order cumulants, the next three algorithms are based on binary output measurement, and the last two algorithms are based on reproducing kernels. The principal objective of this paper is to study the performance of the presented algorithms in different situations, such as with different sizes of the data input or different signal-to-noise ratios. The presented algorithms are applied to the estimation of the channel parameters of the broadband radio access network (BRAN). The simulation results confirm that the presented algorithms are able to estimate the channel parameters with different accuracies, and each algorithm has its advantages and disadvantages for a given situation, such as for a given SNR and data input. Finally, this study provides an idea of which algorithms can be selected in a given situation. The study presented in this paper demonstrates that the cumulant-based algorithms are more adequate if the data inputs are not available (blind identification), but the kernel- and binary-measurement-based methods are more adequate if the noise is not important (SNR≥16 dB).

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“…This can be directly grasped from the case of binary-valued quantized observation in which Z = {0, 1} [15][16][17]. With the rapid advancement in micro-sensors, communication technologies and many frontier fields, the system identification with quantized observations has received considerable research attention [16][17][18][19][20][21][22][23]. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [23] used supervised learning algorithms, such as support vector machines, to deal with the identification of systems based on the binary output measurements.…”

Section: Introductionmentioning

confidence: 99%

Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations

Guo

Diao

2019

Sci. China Inf. Sci.

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This paper addresses the identification of finite impulse response (FIR) systems with both quantized and event-triggered observations. An event-triggered communication scheme for the binary-valued output quantization is introduced to save communication resources. Combining the empirical-measure-based identification technique and the weighted least-squares optimization, an algorithm is proposed to estimate the unknown parameter by full use of the received data and the not-triggered condition. Under quantized inputs, it is shown that the estimate can strongly converge to the real values and the estimator is asymptotically efficient in terms of the Cramér-Rao lower bound. Further, the limit of the average communication rate is derived and the tradeoff between this limit and the estimation performance is discussed. Moreover, the case of multi-threshold quantized observations is considered. Numerical examples are included to illustrate the obtained main results.

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Identification of systems using binary sensors via Support Vector Machines

Cited by 17 publications

References 10 publications

On the identification of FIR systems with binary input and output observations

On the identification of FIR systems with binary input and output observations

Channel Identification Based on Cumulants, Binary Measurements, and Kernels

Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations

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