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A hierarchical decomposition of decision process Petri nets for modeling complex systems

Julio Clempner (2010)

International Journal of Applied Mathematics and Computer Science

We provide a framework for hierarchical specification called Hierarchical Decision Process Petri Nets (HDPPNs). It is an extension of Decision Process Petri Nets (DPPNs) including a hierarchical decomposition process that generates less complex nets with equivalent behavior. As a result, the complexity of the analysis for a sophisticated system is drastically reduced. In the HDPPN, we represent the mark-dynamic and trajectory-dynamic properties of a DPPN. Within the framework of the mark-dynamic...

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...

An algorithm for reducing the dimension and size of a sample for data exploration procedures

Piotr Kulczycki, Szymon Łukasik (2014)

International Journal of Applied Mathematics and Computer Science

The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of a smaller dimension, while retaining as much as possible the same distances between particular elements. Elements of the transformation matrix are computed using the metaheuristics of parallel fast simulated annealing. Moreover, elimination of or a decrease in importance...

Approximation of large-scale dynamical systems: an overview

Athanasios Antoulas, Dan Sorensen (2001)

International Journal of Applied Mathematics and Computer Science

In this paper we review the state of affairs in the area of approximation of large-scale systems. We distinguish three basic categories, namely the SVD-based, the Krylov-based and the SVD-Krylov-based approximation methods. The first two were developed independently of each other and have distinct sets of attributes and drawbacks. The third approach seeks to combine the best attributes of the first two.

Closed-loop structure of decouplable linear multivariable systems

Javier Ruiz, Jorge Luis Orozco, Ofelia Begovich (2005)


Considering a controllable, square, linear multivariable system, which is decouplable by static state feedback, we completely characterize in this paper the structure of the decoupled closed-loop system. The family of all attainable transfer function matrices for the decoupled closed-loop system is characterized, which also completely establishes all possible combinations of attainable finite pole and zero structures. The set of assignable poles as well as the set of fixed decoupling poles are determined,...

Decomposition of a second-order linear time-varying differential system as the series connection of two first order commutative pairs

Mehmet Emir Koksal (2016)

Open Mathematics

Necessary and sufficiently conditions are derived for the decomposition of a second order linear time- varying system into two cascade connected commutative first order linear time-varying subsystems. The explicit formulas describing these subsystems are presented. It is shown that a very small class of systems satisfies the stated conditions. The results are well verified by simulations. It is also shown that its cascade synthesis is less sensitive to numerical errors than the direct simulation...

Decomposition of the symptom observation matrix and grey forecasting in vibration condition monitoring of machines

Czesław Cempel (2008)

International Journal of Applied Mathematics and Computer Science

With the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be symptoms of machine conditions. On this basis, we can form a symptom observation matrix (SOM) intended for condition monitoring and wear trend (fault) identification. On the other hand, we know that contemporary complex machines may have many modes of failure, called faults. The paper presents a method of the extraction of the information...

Disturbance decoupling of nonlinear MISO systems by static measurement feedback

Richard Pothin, Claude H. Moog, Xiao Hua Xia (2002)


This paper highlights the role of the rank of a differential one-form in the solution of such nonlinear control problems via measurement feedback as disturbance decoupling problem of multi-input single output (MISO) systems. For the later problem, some necessary conditions and sufficient conditions are given.

Efficient numerical algorithms for balanced stochastic truncation

Peter Benner, Enrique Quintana-Ortí, Gregorio Quintana-Ortí (2001)

International Journal of Applied Mathematics and Computer Science

We propose an efficient numerical algorithm for relative error model reduction based on balanced stochastic truncation. The method uses full-rank factors of the Gramians to be balanced versus each other and exploits the fact that for large-scale systems these Gramians are often of low numerical rank. We use the easy-to-parallelize sign function method as the major computational tool in determining these full-rank factors and demonstrate the numerical performance of the suggested implementation of...

Extracting second-order structures from single-input state-space models: Application to model order reduction

Jérôme Guillet, Benjamin Mourllion, Abderazik Birouche, Michel Basset (2011)

International Journal of Applied Mathematics and Computer Science

This paper focuses on the model order reduction problem of second-order form models. The aim is to provide a reduction procedure which guarantees the preservation of the physical structural conditions of second-order form models. To solve this problem, a new approach has been developed to transform a second-order form model from a state-space realization which ensures the preservation of the structural conditions. This new approach is designed for controllable single-input state-space realizations...

Further results on sliding manifold design and observation for a heat equation

Enrique Barbieri, Sergey Drakunov, J. Fernando Figueroa (2000)


This article presents new extensions regarding a nonlinear control design framework that is suitable for a class of distributed parameter systems with uncertainties (DPS). The control objective is first formulated as a function of the distributed system state. Then, a control is sought such that the set in the state space where this relation is true forms an integral manifold reachable in finite time. The manifold is called a Sliding Manifold. The Sliding Mode controller implements a theoretically...

H control design for an adaptive optics system

Nikolaos Denis, Douglas Looze, Jim Huang, David Castañon (1999)


In this paper we first present a full order H controller for a multi- input, multi-output (MIMO) adaptive optics system. We apply model reduction techniques to the full order H controller and demonstrate that the closed-loop (CL) system with the reduced order H controller achieves the same high level of performance. Upon closer examination of the structure of the reduced order H controller it is found that the dynamical behavior of the reduced order H controller can be accurately approximated by...

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