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A family of model predictive control algorithms with artificial neural networks

Maciej Ławryńczuk (2007)

International Journal of Applied Mathematics and Computer Science

This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used...

A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent

Christian Napoli, Giuseppe Pappalardo, Emiliano Tramontana (2016)

International Journal of Applied Mathematics and Computer Science

BitTorrent splits the files that are shared on a P2P network into fragments and then spreads these by giving the highest priority to the rarest fragment. We propose a mathematical model that takes into account several factors such as the peer distance, communication delays, and file fragment availability in a future period also by using a neural network module designed to model the behaviour of the peers. The ensemble comprising the proposed mathematical model and a neural network provides a solution...

A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling

Jimoh Olarewaju Pedro, Aarti Panday, Laurent Dala (2013)

International Journal of Applied Mathematics and Computer Science

The paper presents the development of modelling and control strategies for a six-degree-of-freedom, unmanned combat aerial vehicle with the inclusion of the centre of gravity position travel during the straight-leg part of an in-flight refuelling manoeuvre. The centre of gravity position travel is found to have a parabolic variation with an increasing mass of aircraft. A nonlinear dynamic inversion-based neurocontroller is designed for the process under investigation. Three radial basis function...

A Periodic Lotka-Volterra System

Tsvetkov, D. (1996)

Serdica Mathematical Journal

In this paper periodic time-dependent Lotka-Volterra systems are considered. It is shown that such a system has positive periodic solutions. It is done without constructive conditions over the period and the parameters.

A simplex trained neural network-based architecture for sensor fusion and tracking of target maneuvers

Yee Chin Wong, Malur K. Sundareshan (1999)


One of the major applications for which neural network-based methods are being successfully employed is in the design of intelligent integrated processing architectures that efficiently implement sensor fusion operations. In this paper we shall present a novel scheme for developing fused decisions for surveillance and tracking in typical multi-sensor environments characterized by the disparity in the data streams arriving from various sensors. This scheme employs an integration of a multilayer neural...

About the maximum information and maximum likelihood principles

Igor Vajda, Jiří Grim (1998)


Neural networks with radial basis functions are considered, and the Shannon information in their output concerning input. The role of information- preserving input transformations is discussed when the network is specified by the maximum information principle and by the maximum likelihood principle. A transformation is found which simplifies the input structure in the sense that it minimizes the entropy in the class of all information-preserving transformations. Such transformation need not be unique...

Adaptive control scheme based on the least squares support vector machine network

Tarek A. Mahmoud (2011)

International Journal of Applied Mathematics and Computer Science

Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine...

Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations

Mietek A. Brdyś, Marcin T. Brdyś, Sebastian M. Maciejewski (2016)

International Journal of Applied Mathematics and Computer Science

The paper considers the forecasting of the euro/Polish złoty (EUR/PLN) spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-dayahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space wavelet network model is, in contrast to econometric forecast combinations,...

Advances in model-based fault diagnosis with evolutionary algorithms and neural networks

Marcin Witczak (2006)

International Journal of Applied Mathematics and Computer Science

Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, the classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as evolutionary algorithms and neural networks become more and more popular in industrial applications of fault diagnosis. The main objective of this paper is to present recent developments regarding the application of evolutionary algorithms and neural...

An automatic segmentation method for scanned images of wheat root systems with dark discolourations

Jarosław Gocławski, Joanna Sekulska-Nalewajko, Ewa Gajewska, Marzena Wielanek (2009)

International Journal of Applied Mathematics and Computer Science

The analysis of plant root system images plays an important role in the diagnosis of plant health state, the detection of possible diseases and growth distortions. This paper describes an initial stage of automatic analysis-the segmentation method for scanned images of Ni-treated wheat roots from hydroponic culture. The main roots of a wheat fibrous system are placed separately in the scanner view area on a high chroma background (blue or red). The first stage of the method includes the transformation...

An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

Marcin Mrugalski (2013)

International Journal of Applied Mathematics and Computer Science

This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace....

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