Matous Cejnek

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This paper recalls the practical calculation of Learning Entropy (LE) for novelty detection, extends it for various gradient techniques and discusses its use for multivariate dynamical systems with ability of distinguishing between data perturbations or system-function perturbations. LG has been recently introduced for novelty detection in time series via(More)
During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several(More)
This paper presents recently introduced concept of Learning Entropy (LE) for time series and recalls the practical form of its evaluation in real time. Then, a technique that estimates the increased risk of prediction inaccuracy of adaptive predictors in real time using LE is introduced. On simulation examples using artificial signal and real respiratory(More)
This paper introduces a novel approach to novelty detection of every individual sample of data in a time series. The novelty detection is based on the knowledge learned by neural networks and the consistency of data with contemporary governing law. In particular, the relationship of prediction error with the adaptive weight increments by gradient decent is(More)
The paper presents new approach to dementia detection in time series of measured EEG. The proposed method introduced in this paper evaluates EEG signal according to included novelty. This novelty is evaluated using prediction error and increment of adaptive weights obtained during adaptive prediction of individual EEG channels. Normalization of learning(More)
This paper presents method with two modifications how to transform data in real-time for better performance of normalized least mean squares (NLMS) algorithm. The method centers input vector for adaptive filter online according to temporary or historical statistical attributes of the input vector. The method is derived for an adaptive filter with NLMS(More)
This paper presents a case study of non-Shannon entropy, i.e. Learning Entropy (LE), for instant detection of onset of epileptic seizures in individual EEG time series. Contrary to entropy methods of EEG evaluation that are based on probabilistic computations, we present the LE-based approach that evaluates the conformity of individual samples of data to(More)
The paper presents a study of an adaptive approach to lateral skew control for an experimental railway stand. The preliminary experiments with the real experimental railway stand and simulations with its 3-D mechanical model, indicates difficulties of model-based control of the device. Thus, use of neural networks for identification and control of lateral(More)
A three-layer perceptron ANN is designed to avoid difficulties during learning process. The resulting V-shaped Artificial Neural Network has universal approximation property and its learning is based on the minimization of least squares sum. The main advantage of this approach is in the absence of flat domains with a small norm of objective function(More)
This paper presents a study of higher-order neural units as polynomial adaptive filters with multiple-learning-rate gradient descent for 3-D lung tumor motion prediction. The method is compared with single-learning rate gradient descent approaches with and without learning rate normalization. Experimental analysis is done with linear and quadratic neural(More)
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