Temat: genetic fuzzy system - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Advanced supervision of oil wells based on soft computing techniques
Autorzy:: Camargo, E.
Aguilar, J.
Powiązania:: https://bibliotekanauki.pl/articles/91828.pdf
Data publikacji:: 2014
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: intelligent model of supervision
evolutionary computation
fuzzy system
oil industry
operational diagnosis
petroleum wells
gas lift method
multilayer fuzzy system
genetic algorithm
Opis:: In this work is presented a hybrid intelligent model of supervision based on Evolutionary Computation and Fuzzy Systems to improve the performance of the Oil Industry, which is used for Operational Diagnosis in petroleum wells based on the gas lift (GL) method. The model is composed by two parts: a Multilayer Fuzzy System to identify the operational scenarios in an oil well and a genetic algorithm to maximize the production of oil and minimize the flow of gas injection, based on the restrictions of the process and the operational cost of production. Additionally, the first layers of the Multilayer Fuzzy System have specific tasks: the detection of operational failures, and the identification of the rate of gas that the well requires for production. In this way, our hybrid intelligent model implements supervision and control tasks.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 3; 215-225
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: 2.

Tytuł:: PD terminal sliding mode control using fuzzy genetic algorithm for mobile robot in presence of disturbances
Autorzy:: Benaziza, W.
Slimane, N.
Mallem, A.
Powiązania:: https://bibliotekanauki.pl/articles/384371.pdf
Data publikacji:: 2018
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: mobile robot
fast terminal function
PD sliding surface
fuzzy system
genetic algorithm
Lyapunov stability
robot mobilny
system rozmyty
algorytm genetyczny
Stabilność Lapunova
Opis:: This paper presents a new approach in the field of trajectory tracking for nonholonomic mobile robot in presence of disturbances. The proposed control design is constructed by a kinematic controller, based on PD sliding surface using fuzzy sliding mode for the angular and linear velocities disturbances, in order to tend asymptotically the robot posture error to zero. Thereafter a dynamic controller is presented using as a sliding surface design, a fast terminal function (FTF) whose parameters are generated by a genetic algorithm in order to converge the velocity errors to zero in finite time and guarantee the asymptotic stability of the system using a Lyapunov candidate. The elaborated simulation works in the case of different trajectories confirm the robustness of the proposed approach.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 2; 52-60
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: 3.

Tytuł:: Approximation of phenol concentration using novel hybrid computational intelligence methods
Autorzy:: Pławiak, P.
Tadeusiewicz, R.
Powiązania:: https://bibliotekanauki.pl/articles/907935.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: soft computing
neural network
genetic algorithm
fuzzy system
evolutionary neural system
pattern recognition
chemometrics
przetwarzanie miękkie
sieć neuronowa
algorytm genetyczny
system rozmyty
rozpoznawanie obrazu
chemometria
Opis:: This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg–Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 165-181
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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