Temat: fuzzy neural network

Skocz do pozycji: 1.

Tytuł:: Adaptive control of autonomous underwater vehicle based on fuzzy neural network
Autorzy:: Qin, Z.
Gu, J.
Tematy:: autonomous underwater vehicle
fuzzy neural network
adaptive control
stability; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/384507.pdf Link otwiera się w nowym oknie
Opis:: This paper presents an adaptive control method based on fuzzy neural network for Autonomous Underwater Vehicle (AUV). The Fuzzy Neural Network (FNN) could build the inverse model of AUV through on-line learning algorithm, which is free of fuzzy neural network structure knowledge and prior fuzzy inference rules. The adaptive controller for AUV based on FNN is proposed, and then the stability of the resulting AUV closed-loop control system is analyzed by Lyaponov stability theory. The validity of the proposed control method has been verified through computer simulation experiments.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Fuzzy-neural and evolutionary computation in identification of defects
Neuronowo-rozmyte oraz ewolucyjne obliczenia w identyfikacji defektów
Autorzy:: Burczyński, T.
Orantek, P.
Skrobol, A.
Tematy:: fuzzy neural network
evolutionary algorithm
defect
identification
boundary element method; Pokaż więcej
Wydawca:: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Powiązania:: https://bibliotekanauki.pl/articles/282003.pdf Link otwiera się w nowym oknie
Opis:: It is known that an elastic body contains some internal defects such as voids, cracks, additional masses, etc. This paper is devoted to a method based on computational intelligence for non-destructive defect identification. In the presented paper, an elastic body loaded statically is considered. The body contains an unknown number of internal defects. There are a lot of applications based on non-destructive methods. The Evolutionary Algorithm (EA) with the Boundary Element method (BEM) is a very effective tool in the identification of internal defects. In this method, the fitness function is calculated for each chromosome in each generation by the BEM. The number of chromosomes in each generation is quite large, and the number of generations is also large, so the time needed to carry out the identification is very long. Methods based on Artificial Neural Networks (ANN) find the position and shape of internal defects in a very short time. Because ANNs are usually trained using gradient methods, the risk that the solution is in a local optimum is one of disadvantages of such a method. There is also a problem when the ANN has to identify two or more different kinds of defects (cracks, voids and additional masses) in one body. In the present method, an EA is connected with the ANN in one system. This operational allows to avoid main disadvantages of these methods and to use their advantages. The evolutionary algorithm is applied to identify the number of defects and their parameters (position and size). The identification of a defect in the body is performed by minimizing the fitness function which is calculated as a difference between measured and computed displacements in some sensor points on the boundary of the investigated structure. The fitness function is computed using an Artificial Neural Network (ANN).
Obiekty techniczne jako układy mechaniczne zawierają różne defekty wewnętrzne takie jak pustki, pęknięcia itp. Artykuł jest poświęcony nieniszczącym metodom identyfikacji defektów opartym na inteligencji obliczeniowej. Rozważane jako ciało sprężyste znajdujące się pod wpływem obciążenia statycznego zawierające nieznaną liczbę defektów wewnętrznych. Istnieje wiele nieniszczących metod identyfikacji defektów wewnętrznych. Jedną z nich jest metoda oparta na Algorytmach Ewolucyjnych (AE) połączonych z Metodą Elementów Brzegowych (MEB). W tej metodzie dla każdego chromosomu w każdym pokoleniu obliczana jest za pomocą MEB funkcja przystosowania. Ponieważ liczba chromosomów w epoce oraz liczba epok jest dosyć duża, zatem czas potrzebny do przeprowadzenia identyfikacji jest znaczący. Metody bazujące na Sztucznych Sieciach Neuronowych (SSN) identyfikują położenie oraz kształt defektów wewnętrznych w bardzo krótkim czasie. SSN są zazwyczaj uczone z wykorzystaniem metod gradientowych. Isnieje zatem spore ryzyko, że uzyskane rozwiązanie utknęło w minimum lokalnym. Wykorzystując SSN napotykamy na spore trudności również w przypadku identyfikacji dwóch lub więcej różnych rodzajów defektów (pęknięć, pustek itp.), które występują jednocześnie w identyfikowanym układzie. W metodzie opisywanej w niniejszym artykule połączono AE oraz SSn w jeden system. Operacja ta pozwoli ustrzec się przed głównymi wadami i uwypuklić zalety obydwu metod. AE identyfikuje liczbę, położenie oraz wymiary defektów. Identyfikacja następuje przez minimalizację funkcji przystosowania, która jest mierzona jako różnica pomiędzy zmierzonymi i obliczonymi przemieszczeniami na brzegu modelu obiektu w punktach kontrolnych. Funkcja przystosowania jest obliczana z wykorzystaniem SSN.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Simulation and Analysis of Sintering Furnace Temperature Based on Fuzzy Neural Network Control
Autorzy:: Chaoxin, Zou
Rong, Li
Zhiping, Xie
Ming, Su
Jingshi, Zeng
Xu, Ji
Xiaoli, Ye
Ye, Wang
Tematy:: fuzzy neural network
furnace
sintering
temperature control
PID
sieć neuronowa rozmyta
piec
spiekanie
kontrola temperatury; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/1837792.pdf Link otwiera się w nowym oknie
Opis:: Aiming at the problems of delay and couple in the sintering temperature control system of lithium batteries, a fuzzy neural network controller that can solve complex nonlinear temperature control is designed in this paper. The influence of heating voltage, air inlet speed and air inlet volume on the control of temperature of lithium battery sintering is analyzed, and a fuzzy control system by using MATLAB toolbox is established. And on this basis, a fuzzy neural network controller is designed, and then a PID control system and a fuzzy neural network control system are established through SIMULINK. The simulation shows that the response time of the fuzzy neural network control system compared with the PID control system is shortened by 24s, the system stability adjustment time is shortened by 160s, and the maximum overshoot is reduced by 6.1%. The research results show that the fuzzy neural network control system can not only realize the adjustment of lithium battery sintering temperature control faster, but also has strong adaptability, fault tolerance and anti-interference ability.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Rozmyte sieci neuronowe jako modele diagnostyczne w układzie regulacji turbiny kondensacyjnej
Fuzzy Neural Networks for instrument fault diagnosis of condensation turbine control
Autorzy:: Pawlak, M.
Tematy:: układ regulacji
modelowanie rozmyte
sieć neuronowa
diagnostyka
tor pomiarowy
fuzzy neural network
condensation turbine
control
diagnosis; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Instytut Technologii Eksploatacji - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/256925.pdf Link otwiera się w nowym oknie
Opis:: W artykule przedstawiono wykorzystanie modelowania w układzie regulacji turbiny kondensacyjnej do celów diagnostycznych. Diagnostyce podlegają tory pomiarowe doprowadzone do systemu sterowania. Opisano sposób budowy modeli cząstkowych wykorzystywanych do detekcji uszkodzeń, które wykorzystują technikę rozmytych sieci neuronowych.
In the paper an application of fuzzy neural networks (FNN) for sensor fault diagnosis in condensation turbine control unit was given. The FNN are applied for fault detection processes. The FNN models of turbine power, live steam pressure and steam mass flow rate were created and verified. Satisfactory models performance indexes were obtained. The fault sensitivity of residuals was investigated and approved.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Simulation and Analysis of Sintering Furnace Temperature Based on Fuzzy Neural Network Control
Autorzy:: Chaoxin, Zou
Rong, Li
Zhiping, Xie
Ming, Su
Jingshi, Zeng
Xu, Ji
Xiaoli, Ye
Ye, Wang
Tematy:: fuzzy neural network
furnace
sintering
temperature control
PID
sieć neuronowa rozmyta
piec
spiekanie
kontrola temperatury; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/1837849.pdf Link otwiera się w nowym oknie
Opis:: Aiming at the problems of delay and couple in the sintering temperature control system of lithium batteries, a fuzzy neural network controller that can solve complex nonlinear temperature control is designed in this paper. The influence of heating voltage, air inlet speed and air inlet volume on the control of temperature of lithium battery sintering is analyzed, and a fuzzy control system by using MATLAB toolbox is established. And on this basis, a fuzzy neural network controller is designed, and then a PID control system and a fuzzy neural network control system are established through SIMULINK. The simulation shows that the response time of the fuzzy neural network control system compared with the PID control system is shortened by 24s, the system stability adjustment time is shortened by 160s, and the maximum overshoot is reduced by 6.1%. The research results show that the fuzzy neural network control system can not only realize the adjustment of lithium battery sintering temperature control faster, but also has strong adaptability, fault tolerance and anti-interference ability.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks
Autorzy:: Kowalski, Piotr A.
Słoczyński, Tomasz
Tematy:: fuzzy neural network
fuzzy flip-flop neuron
particle swarm optimization
training procedure
sieć neuronowa rozmyta
optymalizacja rojem cząstek
procedura szkoleniowa; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/2055168.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS
Autorzy:: Kumar, D. T.
Soleimani, H.
Kannan, G.
Tematy:: artificial neural network
adaptive network based fuzzy
inference system
closed loop supply chain
forecasting methods
fuzzy neural network
sztuczna sieć neuronowa
system wnioskowania
metoda prognozowania
sieć neuronowa rozmyta; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/329809.pdf Link otwiera się w nowym oknie
Opis:: Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Design of a novel control scheme for the operation of the doubly fed induction generator
Autorzy:: Kumar, Ram Krishan
Choudhary, Jayanti
Tematy:: DFIG
doubly fed induction generator
GACO
genetic algorithm with ant colony optimization
improved recurrent fuzzy neural network
IRFNN
modelling
TST; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Powiązania:: https://bibliotekanauki.pl/articles/59111802.pdf Link otwiera się w nowym oknie
Opis:: The advancement of ocean renewable energy through Tidal Stream Turbines (TSTs) necessitates the use of a variety of computer models to properly evaluate TST efficiency. The Doubly Fed Induction Generator (DFIG) is the most widely utilized Wind Turbine (WT) in the expanding global wind sector. Grid-tied wind energy systems often use the DFIG to meet conventional grid needs including power quality enhancement, grid stability, grid synchronization, power regulation, and fault ride-through. This paper demonstrates the design of a novel control scheme for the operation of the DFIG. The suggested control scheme consisted of an Improved Recurrent Fuzzy Neural Network (IRFNN) and Ant Colony Optimization with Genetic Algorithms (GACOs). A global control system is created and executed to monitor the changeover between the two operating modes. The plant enters a variable speed mode when the tidal speed is low enough, where the system is controlled to ensure that the turbo-generator module functions at peak power extraction efficiency for any specific tidal velocity. The findings demonstrate the system’s superior efficiency, with the highest power extraction provided despite variations in tidal stream input.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: An Expert System Coupled With a Hierarchical Structure of Fuzzy Neural Networks for Fault Diagnosis
Autorzy:: Calado, J. M. F.
Costa, I. S.
Tematy:: rozpoznanie błędu
wykrywanie błędu
system ekspertowy
sieć neuronowa rozmyta
fault diagnosis
fault detection
fault isolation
shallow knowledge
deep knowledge
expert system
fuzzy neural network
abrupt faults
incipient faults; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/908283.pdf Link otwiera się w nowym oknie
Opis:: An on-line fault diagnosis system, designed to be robust to the normal transient behaviour of the process, is described. The overall system consists of an expert system cascade with a hierarchical structure of fuzzy neural networks, corresponding to a multi-stage fault detection and isolation system. The fault detection is performed through the expert system by means of fault detection heuristic rules, generated from deep and shallow knowledge of the process under consideration. If a fault is detected, the hierarchical structure of fuzzy neural networks starts and it performs the fault isolation task. The structure of this diagnosis system was designed to allow for the diagnosis of single and multiple simultaneous abrupt and incipient faults from only single abrupt fault symptoms. Also, it combines the advantages of both fuzzy reasoning and neural networks learning capacity. A continuous binary distillation column has been used as a test bed of the current approach. Single, double and triple simultaneous abrupt faults, as well as incipient faults, have been considered. The preliminary results obtained show a good accuracy, even in the case of multiple faults.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Robustifying analysis of the direct adaptive control of unknown multivariable nonlinear systems based on a new neuro-fuzzy method
Autorzy:: Theodoridis, D. C.
Boutalis, Y.S.
Christodoulou, M. A.
Tematy:: nonlinear systems
control
neuro-fuzzy dynamical system
fuzzy systems
FS
fuzzy recurrent high order neural network
F-RHONN
adaptive regulator
parameter
“Hopping”
“Modified Hopping”
modeling errors
asymptotic regulation; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/91598.pdf Link otwiera się w nowym oknie
Opis:: In this paper, we are dealing with the problem of directly regulating unknown multivariable affine in the control nonlinear systems and its robustness analysis. The method employs a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Systems (FS) operating in conjunction with High Order Neural Networks. In this way the unknown plant is modeled by a fuzzy - recurrent high order neural network structure (F-RHONN), which is of the known structure considering the neglected nonlinearities. The development is combined with a sensitivity analysis of the closed loop in the presence of modeling imperfections and provides a comprehensive and rigorous analysis showing that our adaptive regulator can guarantee the convergence of states to zero or at least uniform ultimate boundedness of all signals in the closed loop when a not-necessarily-known modeling error is applied. The existence and boundedness of the control signal is always assured by employing a method of parameter “Hopping” and “Modified Hopping”, which appears in the weight updating laws. Simulations illustrate the potency of the method showing that by following the proposed procedure one can obtain asymptotic regulation despite the presence of modeling errors. Comparisons are also made to simple recurrent high order neural network (RHONN) controllers, showing that our approach is superior to the case of simple RHONN’s.
Dostawca treści:: Biblioteka Nauki

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