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Tytuł:
A hybrid algorithm for solving inverse problems in elasticity
Autorzy:
Barabasz, B.
Gajda-Zagórska, E.
Migórski, S.
Paszyński, M.
Schaefer, R.
Smołka, M.
Powiązania:
https://bibliotekanauki.pl/articles/331427.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
inverse problem
hierarchic genetic strategy
hybrid optimization
automatic hp adaptive finite element method
zagadnienie odwrotne
strategia genetyczna
optymalizacja hybrydowa
metoda elementów skończonych
Opis:
The paper offers a new approach to handling difficult parametric inverse problems in elasticity and thermo-elasticity, formulated as global optimization ones. The proposed strategy is composed of two phases. In the first, global phase, the stochastic hp-HGS algorithm recognizes the basins of attraction of various objective minima. In the second phase, the local objective minimizers are closer approached by steepest descent processes executed singly in each basin of attraction. The proposed complex strategy is especially dedicated to ill-posed problems with multimodal objective functionals. The strategy offers comparatively low computational and memory costs resulting from a double-adaptive technique in both forward and inverse problem domains. We provide a result on the Lipschitz continuity of the objective functional composed of the elastic energy and the boundary displacement misfits with respect to the unknown constitutive parameters. It allows common scaling of the accuracy of solving forward and inverse problems, which is the core of the introduced double-adaptive technique. The capability of the proposed method of finding multiple solutions is illustrated by a computational example which consists in restoring all feasible Young modulus distributions minimizing an objective functional in a 3D domain of a photo polymer template obtained during step and flash imprint lithography.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 4; 865-886
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
Autorzy:
Dziwiński, Piotr
Bartczuk, Łukasz
Paszkowski, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1837533.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
hybrid methods
Particle Swarm Optimization
Genetic Algorithm
fuzzy systems
multimodal function
Opis:
The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A synthesis of adaptive, low-power real-time embedded systems for ARM big.LITTLE technology
Autorzy:
Ciopiński, L.
Deniziak, S.
Powiązania:
https://bibliotekanauki.pl/articles/114109.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
self-adaptive system
real-time embedded system
adaptive scheduler
developmental genetic programming
ARM big.LITTLE
Opis:
In this paper, we present a method of a synthesis of adaptive schedulers for real-time embedded systems. We assume that the system is implemented using a multi-core embedded processor with low-power processing capabilities. First, the developmental genetic programming is used to generate the scheduler and the initial schedule. Then during the system execution, the scheduler modifies the schedule whenever the execution time of the recently finished task has been shorter or longer than expected. The goal of rescheduling is to minimize the power consumption while all time constraints will be satisfied. We present a real-life example as well as some experimental results showing the advantages of the method.
Źródło:
Measurement Automation Monitoring; 2015, 61, 7; 340-342
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptacyjna metoda wyznaczania ekonomicznych m-diagnozowalnych struktur opiniowania diagnostycznego typu PMC
An adaptive method of determining m-diagnosable diagnosis structures under the PMC model
Autorzy:
Strzelecki, Ł.
Renczewski, K.
Powiązania:
https://bibliotekanauki.pl/articles/273369.pdf
Data publikacji:
2008
Wydawca:
Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:
diagnostyka systemowa
diagnozowalność
struktury opiniowania diagnostycznego
przeliczanie
algorytm genetyczny
m-diagnosable diagnosis structures
determining structures
adaptive method
genetic algorithm
PMC
Opis:
W artykule rozpatrzono problemy występujące przy komputerowym wyznaczaniu ekonomicznych struktur opiniowania diagnostycznego (SOD) typu PMC. Przedstawiono adaptacyjną metodę umożliwiającą efektywne wyznaczanie optymalnych SOD spełniających wybrane kryteria, zapisane w postaci macierzy (uogólnionych) kosztów.
In the paper the problem of determining m-diagnosable diagnosis structures under the PMC model was investigated. On the base of same selected properties of m-diagnosable diagnosis structures a new (genetic) algorithm for computer generating such structures with a good time complexity was proposed.
Źródło:
Biuletyn Instytutu Automatyki i Robotyki; 2008, R. 14, nr 25, 25; 139-153
1427-3578
Pojawia się w:
Biuletyn Instytutu Automatyki i Robotyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptacyjny regulator kursu statku z zastosowaniem metody backstepping
Adaptive ship course controller with use of the backstepping method
Autorzy:
Witkowska, A.
Śmierzchalski, R.
Powiązania:
https://bibliotekanauki.pl/articles/157277.pdf
Data publikacji:
2012
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
autopilot
sterowanie adaptacyjne
backstepping
projektowanie prawa sterowania
adaptive control
nonlinear control
backstepping method
genetic algorithm
Opis:
W układach morskich aplikacja nieliniowych technik adaptacyjnych do sterowania ruchem statku jest obecnie niezbędna aby uwzględnić występujące niepewności strukturalne i parametryczne. Jest to szczególnie istotne, ponieważ dynamika statku zależy od dużej masy i wpływu zakłóceń środowiskowych zarówno addytywnych jak i multiplikatywnych, wywołanych przez fale, wiatry i prądy oceaniczne. Artykuł obejmuje zagadnienie projektowania adaptacyjnego układu regulacji kursem statku morskiego. Zaproponowany algorytm sterowania opiera się na adaptacyjnej metodzie backstepping z prawem adaptacji parametrów modelu statku wyprowadzonym w oparciu o teorię II funkcji Lapunowa. W artykule została przeprowadzona analiza poprawności oraz jakości zaprojektowanego regulatora adaptacyjnego. Zbadano zdolność śledzenia zmian wartości zadanej kursu oraz zbieżność do rzeczywistych wartości parametrów. Istotnym problemem w metodzie backstepping jest uwzględnienie na etapie projektowania, urządzenia wykonawczo-sterującego jakim jest maszyna sterowa, ze względu na nieliniową dynamikę opisaną za pomocą nieliniowości z nasyceniem. Algorytmy sterowania dostępne zarówno w literaturze jak i zastosowaniach morskich zazwyczaj zaniedbują dynamikę maszyny sterowej. Przy opracowaniu algorytmu sterowania przyjęty został uproszczony model matematyczny dynamiki statku, natomiast badania symulacyjne wykonano z pełnowymiarowym modelem statku typu B-481.
The paper deals with issues concerning the advanced ship course control system. The control algorithm suggested in the work is based on the backstepping method and a genetic algorithm. The ship course adaptive controller configuration was designed with use of the backstepping procedure [see section 2]. The controller version (17) includes the adaptation block of ship model parameters (23)-(25). The adaptation of ship model parameters to the updating law derived on a basis of II Function Lyapunov theory enables us to obtain good adaptation properties of the system in the presence of the so-called parametric uncertainties, e.g. connected with the influence of environmental disturbances, such as wind or a sea wave. The adjustable parameters of the obtained nonlinear steering structures (Fig. 1.) were tuned up in order to optimize the system operation. A genetic algorithm was used for optimization. The quality of operation of the obtained steering structures was analyzed by conducting simulation experiments on a simplified (1)-(4) and complex simulation model of a ship of type B-481, [see Sections 3,4]. The results of computer simulation experiments showed the high quality of control and system stability of such steering (Figs. 2, 3, 4). The considered system realizes basic tasks, such as: stabilization of the system, the tuning of controller amplification, adaptation of the system to changing environmental conditions.
Źródło:
Pomiary Automatyka Kontrola; 2012, R. 58, nr 8, 8; 750-753
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive approaches to parameter control in genetic algorithms and genetic programming
Autorzy:
Spalek, J.
Gregor, M.
Powiązania:
https://bibliotekanauki.pl/articles/117900.pdf
Data publikacji:
2011
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
adaptive approach
genetic algorithms
genetic programming
Opis:
The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks such as automated design of control systems, where the space of solutions is non-trivial and may contain discontinuities. Several adaptive mechanisms for control of the search algorithm's parameters are proposed, investigated and compared to each other. It is shown that the proposed mechanisms are useful in preventing the search from getting trapped in local extremes of the fitness landscape.
Źródło:
Applied Computer Science; 2011, 7, 1; 38-56
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive robust PID sliding control of a liquid level system based on multi-objective genetic algorithm optimization
Autorzy:
Mahmoodabadi, M. J.
Taherkhorsandi, M.
Talebipour, M.
Powiązania:
https://bibliotekanauki.pl/articles/206697.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
sliding mode control
PID control
adaptive control
genetic algorithm
multi-objective optimization
liquid level system
Opis:
Adaptive robust PID sliding mode control optimized by means of multi-objective genetic algorithm is presented in this paper to control a three-tank liquid level system with external disturbances. While PID constitutes a reliable and stable controller, when compared to sliding mode control (SMC); robustness and tracking performance of SMC are higher than those of the PID control. To use the unique features of both controllers, optimal sliding mode control is executed in terms of a supervisory controller to enhance the performance of optimal adaptive PID control and to provide the necessary control inputs. After the design of the control law, control coefficients of all four involved controllers are optimized by using the multi-objective genetic algorithm so as to minimize errors and the input of the controller. Simulations illustrate that the adaptive robust PID sliding controller based on multi-objective genetic algorithm optimization provides a superior response in comparison to the results obtained separately by PID control, sliding mode control, and adaptive PID control, respectively.
Źródło:
Control and Cybernetics; 2017, 46, 3; 227-246
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive switching of mutation rate for genetic algorithms and genetic programming
Autorzy:
Spalek, J.
Gregor, M.
Powiązania:
https://bibliotekanauki.pl/articles/118223.pdf
Data publikacji:
2011
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
genetic algorithms
genetic programming
adaptive mechanism
Opis:
The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks such as automated design of control systems, where the space of solutions is non-trivial and may contain discontinuities. An adaptive value-switching mechanism for mutation rate control is proposed. It is shown that the proposed mechanism is useful in preventing the search from getting trapped in local extremes of the fitness landscape.
Źródło:
Applied Computer Science; 2011, 7, 1; 30-37
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system
Autorzy:
Kumar, V. E.
Jerome, J.
Powiązania:
https://bibliotekanauki.pl/articles/141105.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
inverted pendulum
LQR controller
particle swarm optimization (PSO)
genetic algorithm
adaptive inertia weight factor
state feedback control
Opis:
This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.
Źródło:
Archives of Electrical Engineering; 2014, 63, 3; 345-365
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial intelligence in technical diagnostics
Sztuczna inteligencja w diagnostyce technicznej
Autorzy:
Korbicz, J.
Powiązania:
https://bibliotekanauki.pl/articles/327534.pdf
Data publikacji:
2008
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
detekcja uszkodzeń
odporność
próg adaptacyjny
sieć neuronowa
sieć neuronowo-rozmyta
programowanie genetyczne
diagnostyka techniczna
fault detection
robustness
adaptive threshold
neural networks
neuro-fuzzy networks
genetic programming
technical diagnostics
Opis:
The paper deals with the problems of robust fault detection using soft computing techniques, particularly neural networks (Group Method of Data Handling, GMDH), neuro-fuzzy networks (Takagi-Sugeno (T-S) model) and genetic programming. The model-based approach to Fault Detection and Isolation (FDI) is considered. The main objective is to show how to employ the bounded-error approach to determine the uncertainty defined as a confidence range for the model output, the adaptive thresholds can be defined. Finally, the presented approaches are tested on a servoactuator being an FDI benchmark in the DAMADICS project.
W artykule rozpatruje się problemy odpornej detekcji uszkodzeń z wykorzystaniem technik obliczeń inteligentnych, a w szczególności sieci neuronowych (Group Method of Data Handling, GMDH), sieci neuronowo-rozmytych (model Takagi-Sugeno) oraz programowania genetycznego. Rozpatruje się układ detekcji i lokalizacji uszkodzeń z modelem. Głównym celem jest pokazanie jak zastosować metodę ograniczonego błędu do wyznaczenia niepewności modeli neuronowych i rozmytych. Pokazano, że korzystając z wyznaczonych niepewnych modeli obliczeń inteligentnych zdefiniowanych w postaci przedziałów ufności dla wyjścia modelu można zdefiniować adaptacyjny próg decyzyjny. W ostatniej części efektywność rozpatrywanych podejść ilustrowana jest na przykładzie układu diagnostyki inteligentnego urządzenia siłownik-ustawnik-zawór z projektu DAMADICS.
Źródło:
Diagnostyka; 2008, 2(46); 7-16
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Biologically inspired methods for control of evolutionary algorithms
Autorzy:
Stańczak, J.
Powiązania:
https://bibliotekanauki.pl/articles/206262.pdf
Data publikacji:
2003
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
algorytm genetyczny
adaptacja
adaptacyjny algorytm ewolucyjny
genetic algorithms
adaptation
adaptive ewolutionary algorithms
Opis:
In this paper two methods for evolutionary algorithm control are proposed. The first one is a new method of tuning tlie probabilities of genetic operators. It is assumed in the presented approach that every member of the optimized population conducts his own ranking of genetic operators' qualities. This ranking enables computing the probabilities of execution of genetic operators. This set of probabilities is a basis of experience of every individual and according to this basis the individual chooses the operator in every iteration of the algorithm. Due to this experience one can maximize the chances of his offspring to survive. The second part of the paper deals with a self-adapting method of selection of individuals to a subsequent generation. Methods of selection applied in the evolutionary algorithms are usually inspired by nature and prefer solutions where the main role is played by randomness, competition and struggle among individuals. In the case of evolutionary algorithms, where populations of individuals are usually small, this causes a premature convergence to local minima. In order to avoid this drawback I propose to apply an approach based rather on an agricultural technique. Two new methods of object selection are proposed: a histogram selection and a mixed selection. The methods described were tested using examples based on scheduling and TSP.
Źródło:
Control and Cybernetics; 2003, 32, 2; 411-433
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative Study of Optimised Artificial Intelligence Based First Order Sliding Mode Controllers for Position Control of a DC Motor Actuator
Autorzy:
Nyong-Bassey, B. E.
Akinloye, B.
Powiązania:
https://bibliotekanauki.pl/articles/385114.pdf
Data publikacji:
2016
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
adaptive fuzzy control
DC motor position control
genetic algorithm
particle swarm optimization (PSO)
sliding mode control
Opis:
This paper aims at critically reviewing various sliding mode control measures applied to Permanent Magnet DC Motor actuator for position control. At first, a hybrid sliding mode controller was examined with its advantages and disadvantages. Then, the smooth sliding mode controller in the same manner. The shortcomings of the two methods were overcome by proper switch design and also using tanh-sinh hyperbolic function. The sliding mode controller switches on when either disturbance or noise is detected. Genetic Algorithm Computational tuning technique is employed to optimize the gains of the controllers for optimal response.The performance of the proposed controller architecture, as well as the reviewed controllers, have been compared for performance evaluation with respect to several operating conditions. This includes load torque disturbance injection, noise injection in a feedback loop, motor nonlinearity exhibited by parameters variation, and a step change in reference input demand.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2016, 10, 3; 58-71
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Design and development of 3-stage determination of damage location using Mamdani-Adaptive Genetic-Sugeno model
Autorzy:
Sahu, S.
Kumar, P. B.
Parhi, D. R.
Powiązania:
https://bibliotekanauki.pl/articles/281979.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
damage
Mamdani FIS
Sugeno FIS
Adaptive Genetic Algorithm
vibration
natural frequencies
Opis:
Damage detection in structural elements like beams is one of important research areas for health monitoring. Initiation of a fault in the form of a crack or any damage puts a limitation on the service life of a structural member. So, in this paper, a method is proposed which uses the advantages of soft computing techniques like Fuzzy Inference Systems (Mamdani and Sugeno) and Adaptive Genetic Algorithm for three stage refinement of the data base generated using dynamic responses from a cracked fixed-free aluminum alloy beam element. For the crack element reference, a finite element model of a single transverse crack has been considered. The proposed method describes both Mamdani and Sugeno Fuzzy Inference Systems for training of damage parameters. In the Adaptive Genetic Algorithm, a statistics based method has been incorporated to limit the randomness of the search process. Finally, the results from the Mamdani-Adaptive Genetic-Sugeno model (MAS) are validated with the results from the experimental analysis.
Źródło:
Journal of Theoretical and Applied Mechanics; 2017, 55, 4; 1325-1339
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Design of a linear quadratic regulator based on genetic model reference adaptive control
Autorzy:
Abdullah, Abdullah I.
Mahmood, Ali.
Thanoon, Mohammad A.
Powiązania:
https://bibliotekanauki.pl/articles/27314263.pdf
Data publikacji:
2022
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
model reference adaptive control
gradient approach
Linear Quadratic Regulator
genetic algorithm
Opis:
The conventional control system is a controller that controls or regulates the dynamics of any other process. From time to time, a conventional control system may not behave appropriately online; this is because of many factors like a variation in the dynamics of the process itself, unexpected changes in the environment, or even undefined parameters of the system model. To overcome this problem, we have designed and implemented an adaptive controller. This paper discusses the design of a controller for a ball and beam system with Genetic Model Reference Adaptive Control (GMRAC) for an adaptive mechanism with the MIT rule. Parameter adjustment (selection) should occur using optimization methods to obtain an optimal performance, so the genetic algorithm (GA) will be used as an optimization method to obtain the optimum values for these parameters. The Linear Quadratic Regulator (LQR) controller will be used as it is one of the most popular controllers. The performance of the proposed controller with the ball and beam system will be carried out with MATLAB Simulink in order to evaluate its effectiveness. The results show satisfactory performance where the position of the ball tracks the desired model reference.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 3; 75--81
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Designing a ship course controller by applying the adaptive backstepping method
Autorzy:
Witkowska, A.
Śmierzchalski, R.
Powiązania:
https://bibliotekanauki.pl/articles/331255.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
autopilot
sterowanie adaptacyjne
sterowanie nieliniowe
algorytm genetyczny
adaptive control
nonlinear control
backstepping
genetic algorithms
Opis:
The article discusses the problem of designing a proper and efficient adaptive course-keeping control system for a seagoing ship based on the adaptive backstepping method. The proposed controller in the design stage takes into account the dynamic properties of the steering gear and the full nonlinear static maneuvering characteristic. The adjustable parameters of the achieved nonlinear control structure were tuned up by using the genetic algorithm in order to optimize the system performance. A realistic full-scale simulation model of the B-481 type vessel including wave and wind effects was applied to simulate the control algorithm by using time domain analysis.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 985-997
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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