Ivo Bukovsky | University of South Bohemia (original) (raw)

Papers by Ivo Bukovsky

Acta Mechanica Slovaca, Oct 31, 2017

This paper presents a modification of reference-model adaptive control with a layered network of ... more This paper presents a modification of reference-model adaptive control with a layered network of higher-order neural units (HONUs) as adaptive state-feedback controllers. The degree of freedom of such neural controller is deemed here as the number of applied HONUs of a customizable polynomial order and as of their individually customizable input vectors. Furthermore, the control scheme is enhanced because potentially occurring disturbances of controlled variable can result in that the sample-by-sample adapted controllers may tend to adaptively force the plant output to merely follow the reference model output because input data are affected by the error of disturbance, while the overall control loop dynamics would be more accurately adapted if no perturbation occurs or if the reference model is actuated with actually measured variables. Furthermore, the controller weight updates usually involve some step-delayed computations that might be in fact recalculated with the latest updated weights, so the controller learning can be improved.

A three-layer perceptron ANN is designed to avoid difficulties during learning process. The resul... 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 gradiënt. Therefore, any optimization method which is based on local searching yields from this property of objective function. Due to multimodality of objective function in the case of ANN learning, Cuckoo Search heuristics with embedded Levy Flights was used both for V-shaped ANN learning and for the learning of MLP and RBF as referential ANNs. Time complexity and reliability of learning are demonstrated on simple example.

IGI Global eBooks, 2013

The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industr... more The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in industrial control applications for their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive nonlinear tools, but their nonlinear strength is naturally repaid with the local minima problem, overfitting, and high demands for application-correct neural architecture and optimization technique that often require skilled users. The QNU is the important midpoint between linear systems and highly nonlinear neural networks because the QNU is relatively very strong in nonlinear approximation; however, its optimization and performance have fast and convex-like nature, and its mathematical structure and the derivation of the learning rules is very comprehensible and efficient for implementation. These advantages of QNU are demonstrated by using real and theoretical examples.

The paper introduces linear dynamic-order extended time-delay dynamic neural unit (DOE TmD-DNU) w... more The paper introduces linear dynamic-order extended time-delay dynamic neural unit (DOE TmD-DNU) whose adaptation by the dynamic backpropagation learning rule is enhanced by the genetic algorithm. DOE TmD-DNU is a possible customization of novel class of artificial neurons called time-delay dynamic neural units (TmD-DNU). In standalone implementations, these artificial dynamic neural architectures can be viewed as analogies to continuous time-delay differential equations, where the equation parameters are unknown and are adaptable such as neural weights and other parameters of artificial neurons. Time delays on neural inputs of a unit and in the state feedback of a unit are also considered as unit’s adaptable neural parameters. These new neural units equipped with adaptable time delays can identify all parameters of a continuous time-delay dynamic system including unknown time delays both in the unit’s inputs as well as in its state variables. Incorporation of adaptable time delays into neural units significantly increases approximation capability of individual neural units. It results in simplification of a neural architecture and minimization of the number of neural parameters, and thus possibly in better understanding the obtained neural model. It has been shown, that stable adaptation of all parameters of TmD-DNU including time delays can be achieved by dynamic modification of backpropagation learning algorithm. However, sometimes the relatively slow convergence rate of the neural parameters and the convergence rather toward local minima of error function can be considered as drawbacks of the adaptation. This paper focuses the improvement of the backpropagation learning algorithm of TmD-DNU by the genetic algorithm and its application to heat transfer system modeling. The adaptation learning algorithm based on the simultaneous combination of dynamic backpropagation and genetic algorithm has been designed to accelerate the convergence of time-delay parameters of a neural unit and to achieve the global character of minimization of error function. The neural weights and parameters, except the time-delays, are adapted by dynamic modification of backpropagation learning algorithm, and those that represent time-delays can be adapted by the genetic algorithm. Results on system identification of an unknown system with dynamics of higher-order including unknown time delays are shown in comparison to achievements by common identification methods applied to the same system. The robust identification capabilities, the aspects of network implementation of TmD-DNU, and the prospects of their nonlinear versions, i.e. higher-order nonlinear time delay dynamic neural units (TmD-HONNU) are briefly discussed with respect to the learning technique presented in this paper.

IGI Global eBooks, May 25, 2010

Ecological Modelling, Oct 1, 2011

Neurocomputing, Oct 1, 2018

In this paper we study the performance of two original adaptive unsupervised novelty detection me... more In this paper we study the performance of two original adaptive unsupervised novelty detection methods (NDMs) on data with concept drift. Newly, the concept drift is considered as a challenging data imbalance that should be ignored by the NDMs, and only system changes and outliers represent novelty. The field of application for such NDMs is broad. For example, the method can be used as a supportive method for real-time system fault detection, for onset detection of events in biomedical signals, in monitoring of nonlinearly controlled processes, for event driven automated trading, etc.. The two newly studied methods are the error and learning based novelty detection (ELBND) and the learning entropy (LE) based detection. These methods use both the error and weight increments of a (supervised) learning model. Here, we study these methods with normalized least-mean squares (NLMS) adaptive filter, and while the NDMs were studied on various real life tasks, newly, we carry out the study on two types of data streams with concept drift to analyze the general ability for unsupervised novelty detection. The two data streams, one with system changes, second with outliers, represent different novelty scenarios to demonstrate the performance of the proposed NDMs with concept drifts in data. Both tested NDMs work as a feature extractor. Thus, a classification framework is used for the evaluation of the obtained features and NDM benchmarking, where two other NDMs, one based on the adaptive model plain error, second using the sample entropy (SE), are used as the reference for the comparison to the proposed methods. The results show that both newly studied NDMs are superior to the merely use of the plain error of adaptive model and also to the sample entropy based detection while they are robust against the concept drift occurrence.

IEEE Internet of Things Journal, 2023

Living Lab, one of the recent emerging smart city concepts, faces long-term sustainability challe... more Living Lab, one of the recent emerging smart city concepts, faces long-term sustainability challenges associated with its complexity and breadth of use. To be efficient, it must rely on comprehensive set of information distributed appropriately among all stakeholders to unleash its full innovation potential. This is especially true in the case of positive energy districts, where timely data dissemination is essential for prosumager decisions and their greedy behaviour. This paper interconnects intelligent information exchange, supported by ultra-low latency hybrid access network infrastructure, with the clever use of available fog computing resources to properly disseminate complex energy details to all participating entities. As the optimal distribution of information using proper task offloading is the convergence problem, we recalled higher-order neural units that helped maintain computational and energy efficiency in conjunction with the preservation of the overall system stability. We have achieved a reliable hourly energy consumption prediction with a computationally very lightweight alternative to commonly used deep neural network approaches that can be deployed on available smart appliances with ease. The application and simulation were performed on the dataset provided by one of Europe's smart city pioneers, where the prosumager positive energy district transition has already started.

This paper presents a study of lung tumormotion time-series prediction, first, with the use of co... more This paper presents a study of lung tumormotion time-series prediction, first, with the use of conventional static (feedforward) MLP neural network (with a single hidden perceptron layer) and, second, with the static quadratic neural unit (QNU), i.e., a class of polynomial neural network (or a higher-order neural unit). We also demonstrate that QNU can be trained in a very efficient and fast way for real time retraining due to its linear nature of optimization problem. The objective is the prediction accuracy of 1 [mm] for 1-second prediction horizon. So it is well applicable for radiation tracking therapy.

Acta Polytechnica, Jan 3, 2012

This paper presents a case study of non-Shannon entropy, i.e. Learning Entropy (LE), for instant ... 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 the contemporary learned governing law of a learning system and thus LE can detect changes of dynamics on individual samples of data. For comparison, the principle and the results are compared to the Sample Entropy approach. The promising results indicate the LE potentials for feature extraction enhancement for early detection of epileptic seizures on individual-data-sample basis.

Advances in intelligent systems and computing, 2016

This paper summarizes the fundamental construction of higher-order-neural-units (HONU) as a class... more This paper summarizes the fundamental construction of higher-order-neural-units (HONU) as a class of polynomial function based neural units, which are though non-linear discrete time models, are linear in their parameters. From this a relation will be developed, ultimately leading to a new definition for analysing the global stability of a HONU, not only as a model itself, but further as a means of justifying the global dynamic stability of the whole control loop under HONU feedback control. This paper is organised to develop the fundamentals behind this intrinsic relation of linear dynamic systems and HONUs accompanied by a theoretical example to illustrate the functionality and principles of the concept.

Advances in intelligent systems and computing, May 17, 2018

This paper introduces a novel ISS stability evaluation for a LNU based HONU-MRAC control loop whe... more This paper introduces a novel ISS stability evaluation for a LNU based HONU-MRAC control loop where an LNU serves as a plant and a HONU as a non-linear polynomial feedback controller. Till now, LNUs have proven their advantages as computationally efficient and effective approximators, further optimisers of linear and weakly non-linear dynamic systems. Due to the fundamental construction of an HONU-MRAC control loop featuring analogies with discrete-time non-linear dynamic models, two novel state space representations of the whole LNU based HONU-MRAC control loop are presented. Backboned by the presented state space forms, the ISS stability evaluation is derived and verified with theories of bounded-input-bounded-state (BIBS) and Lyapunov stability on a practical non-linear system example.