Temat: Fuzzy Particle Swarm Optimization - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Jacking and energy consumption control over network for jack-up rig: simulation and experiment
Autorzy:: Do, Viet-Dung
Dang, Xuan-Kien
Tran, Tien-Dat
Pham, Thi Duyen-Anh
Powiązania:: https://bibliotekanauki.pl/articles/32912855.pdf
Data publikacji:: 2022
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:: networked control system
environmental forces
energy consumption
Fuzzy Particle Swarm Optimization
jacking system
time-delay
Opis:: Oil and gas projects differ from regular investment projects in that they are frequently large-scale, categorised as vital national projects, highly technological, and associated with significant risks. Drilling rigs are a crucial component of the oil and gas sector and the majority of the systems and equipment aboard drilling rigs are operated automatically. Consequently, it is crucial to address the topic of an advanced control theory for off-shore systems. Network technology connected to control is progressively being used to replace outdated technologies, together with other contemporary technologies. In this study, we examine how to adapt a networked control jacking system to the effects of internal and external disturbances with a time delay, using a Fuzzy controller (FC)-based particle swarm optimisation. To demonstrate the benefit of the proposed approach, the developed Fuzzy Particle Swarm Optimisation (FPSO) controller is compared with the fuzzy controller. Finally, the results from simulations and experiments utilising Matlab software and embedded systems demonstrate the suitability of the proposed approach.
Źródło:: Polish Maritime Research; 2022, 3; 89-98
1233-2585
Pojawia się w:: Polish Maritime Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm
Autorzy:: Tian, Niu
Huang, Songwei
He, Lifang
Du, Lingpan
Yang, Sheping
Huang, Bin
Powiązania:: https://bibliotekanauki.pl/articles/24085898.pdf
Data publikacji:: 2023
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:: fuzzy control
contraction-expansion factor
particle swarm optimization
Opis:: The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link.The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansionfactor. The control performance is directly related to the contraction-expansionfactor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Itssimulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.
Źródło:: Physicochemical Problems of Mineral Processing; 2023, 59, 3; art. no. 169942
1643-1049
2084-4735
Pojawia się w:: Physicochemical Problems of Mineral Processing
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing
Autorzy:: Valdez, Fevrier
Kawano, Yunkio
Melin, Patricia
Powiązania:: https://bibliotekanauki.pl/articles/384329.pdf
Data publikacji:: 2020
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: genetic algorithms
particle swarm optimization (PSO)
fuzzy logic
parallel processing
Opis:: In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 55-64
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Particle swarm optimization based fuzzy clustering approach to identify optimal number of clusters
Autorzy:: Chen, M.
Ludwig, S. A.
Powiązania:: https://bibliotekanauki.pl/articles/91549.pdf
Data publikacji:: 2014
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: optimization
fuzzy clustering
cluster analysis
particle swarm optimization (PSO)
PSO
fuzzy Sammon mapping
Sammon mapping
Opis:: Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 43-56
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

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

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Skocz do pozycji: 6.

Tytuł:: Particle Swarm Optimization Fuzzy Systems for the Age Reduction Imperfect Maintenance Model
Autorzy:: Li, Che-Hua
Powiązania:: https://bibliotekanauki.pl/articles/301843.pdf
Data publikacji:: 2008
Wydawca:: Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:: imperfect maintenance
preventive maintenance
reliability
fuzzy modeling
particle swarm optimization (PSO)
Opis:: This research includes two topics: (1) the modeling of periodic preventive maintenance policies over an infi nite time span for repairable systems with the reduction of the degradation rate after performing an imperfect preventive maintenance (PM) activity; (2) the parameter estimation of failure distribution and the restoration effect of PM from the proposed PM policy for deteriorating systems. The concept of the improvement factor method is applied to measure the restoration effect on the degradation rate for a system after each PM. An improvement factor is presented as a function of the system's age and the cost of each PM. A periodic PM model is then developed. The optimal PM interval and the optimal replacement time for the proposed model can be obtained by minimizing the objective functions of the cost rate through the algorithms provided by this research. An example of using Weibull failure distribution is provided to investigate the proposed model. The method is proposed to estimate the parameters of the failure process and the improvement effect after each PM by analyzing maintenance and failure log data. In this method, a PSO-based method is proposed for automatically constructing a fuzzy system with an appropriate number of rules to approach the identifi ed system. In the PSO-based method, each individual in the population is constructed to determine the number of fuzzy rules and the premise part of the fuzzy system, and then the recursive least-squares method is used to determine the consequent part of the fuzzy system constructed by the corresponding individual. Consequently, an individual corresponds to a fuzzy system. Subsequently, a fi tness function is defi ned to guide the searching procedure to select an appropriate fuzzy system with the desired performance. Finally, two identifi cation problems of nonlinear systems are utilized to illustrate the effectiveness of the proposed method for fuzzy modeling.
Źródło:: Eksploatacja i Niezawodność; 2008, 4; 28-34
1507-2711
Pojawia się w:: Eksploatacja i Niezawodność
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Particle swarm optimization for solving a class of type-1 and type-2 fuzzy nonlinear equations
Autorzy:: Sadiqbatcha, S.
Jafarzadeh, S.
Ampatzidis, Y.
Powiązania:: https://bibliotekanauki.pl/articles/91663.pdf
Data publikacji:: 2018
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: type-1 fuzzy sets
type-2 fuzzy sets
polynomial
exponential equation
particle swarm optimization (PSO)
Opis:: This paper proposes a modified particle swarm optimization (PSO) algorithm that can be used to solve a variety of fuzzy nonlinear equations, i.e. fuzzy polynomials and exponential equations. Fuzzy nonlinear equations are reduced to a number of interval nonlinear equations using alpha cuts. These equations are then sequentially solved using the proposed methodology. Finally, the membership functions of the fuzzy solutions are constructed using the interval results at each alpha cut. Unlike existing methods, the proposed algorithm does not impose any restriction on the fuzzy variables in the problem. It is designed to work for equations containing both positive and negative fuzzy sets and even for the cases when the support of the fuzzy sets extends across 0, which is a particularly problematic case.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 2; 103-110
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

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

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Skocz do pozycji: 9.

Tytuł:: A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks
Autorzy:: Kowalski, Piotr A.
Słoczyński, Tomasz
Powiązania:: https://bibliotekanauki.pl/articles/2055168.pdf
Data publikacji:: 2021
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: fuzzy neural network
fuzzy flip-flop neuron
particle swarm optimization
training procedure
sieć neuronowa rozmyta
optymalizacja rojem cząstek
procedura szkoleniowa
Opis:: The aim of the presented study is to investigate the application of an optimization algorithm based on swarm intelligence to the configuration of a fuzzy flip-flop neural network. Research on solving this problem consists of the following stages. The first one is to analyze the impact of the basic internal parameters of the neural network and the particle swarm optimization (PSO) algorithm. Subsequently, some modifications to the PSO algorithm are investigated. Approximations of trigonometric functions are then adopted as the main task to be performed by the neural network. As a result of the numerical verification of the problem, a set of rules are developed that can be helpful in constructing a fuzzy flip-flop type neural network. The obtained results of the computations significantly simplify the structure of the neural network in relation to similar conditions known from the literature.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 577--586
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence
Autorzy:: Li, C.
Chiang, T. W.
Powiązania:: https://bibliotekanauki.pl/articles/331280.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: zbiór rozmyty
system neuronowo-rozmyty
optymalizacja rojem cząstek
szereg czasowy
complex fuzzy set
complex neuro fuzzy system
hierarchical multi swarm
particle swarm optimization (PSO)
recursive least squares estimator
time series forecasting
Opis:: Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued and characterized within the unit disc of the complex plane. The application of CFSs to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical Multi-Swarm PSO (HMSPSO) and the well known Recursive Least Squares Estimator (RLSE). Three examples of financial time series are used to test the proposed approach, whose experimental results outperform those of other methods.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 787-800
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 11.

Tytuł:: An Analytical Study for the Role of Fuzzy Logic in Improving Metaheuristic Optimization Algorithms
Autorzy:: Vij, Sonakshi
Jain, Amita
Tayal, Devendra
Castillo, Oscar
Powiązania:: https://bibliotekanauki.pl/articles/385121.pdf
Data publikacji:: 2018
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: fuzzy logic
metaheuristics
evolutionary computing
genetic algorithm
particle swarm optimization (PSO)
ant colony optimization
fuzzy evolutionary algorithm
fuzzy cuckoo
fuzzy simulated annealing
fuzzy swarm intelligence
fuzzy differential evolution
tabu
fuzzy mutation
fuzzy natural selection
fuzzy fitness function
big bang big crunch
fuzzy bacterial
neuro fuzzy logic
logika rozmyta
metaheurystyka
obliczenia ewolucyjne
algorytm genetyczny
optymalizacja roju cząstek
optymalizacja kolonii mrówek
Opis:: The research applications of fuzzy logic have always been multidisciplinary in nature due to its ability in handling vagueness and imprecision. This paper presents an analytical study in the role of fuzzy logic in the area of metaheuristics using Web of Science (WoS) as the data source. In this case, 178 research papers are extracted from it in the time span of 1989-2016. This paper analyzes various aspects of a research publication in a scientometric manner. The top cited research papers, country wise contribution, topmost organizations, top research areas, top source titles, control terms and WoS categories are analyzed. Also, the top 3 fuzzy evolutionary algorithms are extracted and their top research papers are mentioned along with their topmost research domain. Since neuro fuzzy logic poses feasible options for solving numerous research problems, hence a section is also included by the authors to present an analytical study regarding research in it. Overall, this study helps in evaluating the recent research patterns in the field of fuzzy metaheuristics along with envisioning the future trends for the same. While on one hand this helps in providing a new path to the researchers who are beginners in this field as they can start exploring it through the analysis mentioned here, on the other hand it provides an insight to professional researchers too who can dig a little deeper in this field using knowledge from this study.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 4; 11-27
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 12.

Tytuł:: A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
Autorzy:: Soltani, M.
Chaari, A.
Ben Hmida, F.
Powiązania:: https://bibliotekanauki.pl/articles/330134.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: model rozmyty Takagi-Sugeno
algorytm grupowania
metoda najmniejszych kwadratów
optymalizacja rojem cząstek
Takagi-Sugeno fuzzy models
noise clustering algorithm
fuzzy c-regression model
orthogonal least squares
particle swarm optimization (PSO)
Opis:: This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 617-628
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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