In this paper, several design approaches for deriving the monovariable PID controllers, such as auto-tuning, self-tuning, pattern recognition and fuzzy logic, are reviewed and implemented. Some experiments are conduct...
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In this paper, several design approaches for deriving the monovariable PID controllers, such as auto-tuning, self-tuning, pattern recognition and fuzzy logic, are reviewed and implemented. Some experiments are conducted to observe the ability of the controllers in a level process under setpoint changes. The experimental results show that the PID control strategies are capable of giving a good closed-loop dynamic for the level control system in wide range operation.
We examine threshold-based transmission strategies for distributed opportunistic medium access in a scenario with fairly general probabilistic interference conditions. Specifically, collisions between concurrent trans...
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Partial update algorithms such as sequential LMS have been proposed to reduce computation cost. To further decrease the computation cost for low-resource real-time echo cancellation by oversampled subband adaptive fil...
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Effectiveness of adaptive extragradient algorithms for network economics problems is demonstrated with the modified model of blood supply chain network. Informational system for comparing behavior of algorithms for so...
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We propose a general framework to online learning for classification problems with time-varying potential functions in the adversarial setting. This framework allows to design and prove relative mistake bounds for any...
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ISBN:
(纸本)9781617823800
We propose a general framework to online learning for classification problems with time-varying potential functions in the adversarial setting. This framework allows to design and prove relative mistake bounds for any generic loss function. The mistake bounds can be specialized for the hinge loss, allowing to recover and improve the bounds of known online classification algorithms. By optimizing the general bound we derive a new online classification algorithm, called NAROW, that hybridly uses adaptive- and fixed- second order information. We analyze the properties of the algorithm and illustrate its performance using synthetic dataset.
Although spike trains are very telling of neuronal processing, they are also very removed from the time and macroscopic scales of behavior. Therefore, the spike train methodology begs an answer to the question of how ...
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ISBN:
(纸本)1598290355
Although spike trains are very telling of neuronal processing, they are also very removed from the time and macroscopic scales of behavior. Therefore, the spike train methodology begs an answer to the question of how to optimally bridge the time scale of spikes events (milliseconds) with the time scale of behavior (seconds). Most often, the relatively rudimentary method of binning is used, but much of the resolution of the representation is lost, suggesting that better, model-based methodologies need to be developed. The fundamental problem in spike-based BMI modeling is how to find more effective ways to work directly with spikes for modeling that overcome the difficulties of the conventional approaches. Here we have shown how a Monte Carlo sequential estimation framework could be used as a probabilistic approach to reconstruct the kinematics directly from the multichannel neural spike trains. We investigated an information theoretical tuning depth to evaluate the neuron tuning properties. To describe the functional relationship between neuron firing and movement, a parametric LNP model was utilized. With the knowledge gained from the neuron physiology tuning analysis, a novel signal processing algorithm based on a Monte-Carlo sequential estimation could be applied directly to point processes to convert the decoding role of a brain-machine interface into a problem of state sequential estimation. The Monte Carlo sequential estimation modifies the amplitude of the observed discrete neural spiking events by the probabilistic measurement contained in the posterior density. The Monte Carlo sequential estimation provided a better approximation of the posterior density than point process adaptive filtering with a Gaussian assumption. Compared with the Kalman filter applied to a cursor control task, the preliminary kinematics reconstruction using the Monte Carlo sequential estimation framework showed better orrelation coefficients between the desired and estimated trajecto
In this paper, we propose adaptive algorithms for system identification of sparse systems. We introduce a L1-norm penalty to improve the performance of affine projection algorithms. This strategy results in two new al...
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ISBN:
(纸本)9781849196611
In this paper, we propose adaptive algorithms for system identification of sparse systems. We introduce a L1-norm penalty to improve the performance of affine projection algorithms. This strategy results in two new algorithms, the zero- attracting APA (ZA-APA) and the reweighted zero-attracting AP (RZA-APA). The ZA-APA is derived via the combination of a L1-norm penalty on the coefficients into a standard APA cost function, which generates a zero attractor in the update function. The zero attractor promotes sparsity in the filter coefficients during the update process, and therefore accelerates convergence when identifying sparse systems. We show that the ZA-APA can achieve a lower mean square error than the standard LMS and AP algorithms. To further improve the performance, the RZA-APA is developed using a reweighted zero attractor. The performance of the RZA-APA is superior to that of the ZA-APA numerically. Simulation results demonstrate the advantages of the proposed adaptive algorithms in both convergence rate and steady-state behavior under sparsity assumptions on the true coefficient vector. The RZA-APA is also shown to be robust when the number of non-zero taps increases.
A comparative study of the multichannel Affine Projection (AP), the Fast Transversal Filter (FTF), the filtered-X LMS (FXLMS) and the Recursive Least Squares (RLS) algorithms is presented for active noise control (ANC...
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Many medical devices are using photoplethysmography (PPG) signals to estimate cardiac rate (CR), respiratory rate (RR), blood pressure (BP) and blood oxygen (SpO2). Photoplethysmography demonstrated its great potentia...
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With the continuous changes in the economic environment and the intensification of market competition, the accuracy of accounting budget forecasts has become increasingly important for corporate strategic planning and...
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