Wyniki wyszukiwania - BazTech

1

Neuronowy generator wirtualnej zmiennej stanu w regulatorze prędkości układu dwumasowego

Stanisławski Radosław, Malarczyk Mateusz, Szrek Jarosław, Żychlewicz Mateusz, Kamiński Marcin

Przegląd Elektrotechniczny

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2023

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R. 99, nr 5

196--201

PL

W publikacji przedstawiona została modyfikacja klasycznego regulatora stanu, która zakłada wprowadzenie radialnej sieci neuronowej (Radial Basis Function Neural Network). Celem jest wygenerowanie sygnału, który zostanie wprowadzony do wektora zmiennych stanu sprzężeń zwrotnych. Obiektem regulacji jest napęd elektryczny z połączeniem sprężystym. W artykule uwzględniono opis teoretyczny proponowanego rozwiązania, a także zaprezentowano wyniki badań symulacyjnych struktury sterowania. Badania przeprowadzone dla rzeczywistego układu napędowego stanowią dodatkową weryfikację analizowanego regulatora stanu.

EN

In this paper, a state feedback controller enhanced by a Radial Basis Function Neural Network is presented. The main goal of the network is calculation of a virtual signal used in state vector and applied as feedback. The plant considered in the article is an electrical drive with a flexible joint. The mathematical description of the proposed control scheme and the numerical tests can be found in the manuscript. Experimental analysis is performed as an additional verification of the proposed state controller.

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Radial basis function neural network and salp swarm algorithm for paddy leaf diseases classification in Thanjavur, Tamilnadu geographical region

Thirugnanasambandam Gayathri Devi, Rajkumar Ganesan, Srinivasan Anandan, Sudha Selvarajan

Acta Geophysica

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2022

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Vol. 70, no 6

2917--2932

EN

Farming is an essential sustenance for the progressive population. The development of our country depends on the farmers. Plants endure by many diseases due to environmental factors. So, the farmers need to detect plant diseases at an early stage for appreciable yield. In the beginning, the observing and examining plant disease are examined physically by the expertise in the farming field, which requires a considerable measure of work/ and requires over the top handling time. Now, machine learning concepts eliminate conventional protruding and time-consuming techniques. This paper focuses on a novel method for detecting and identifying paddy leaf diseases at the early stages in Thanjavur region using radial basis function neural network (RBFNN) classifier. Further, it is optimized with salp swarm algorithm (SSA) technique. The proposed method utilizes the data from the TNAU agritech portal, IRRI knowledge bank, UCI machine learning repository databases, which have healthy and diseased images. This work illustrates four categories (Bacterial Blast, Bacterial Blight, Leaf Tungro and Brown Spot) of infected paddy images along with the normal set of images. Initially the preprocessing is performed for the acquired images then K-means segmentation algorithm segregates the image. Gray level co-occurrence matrix extracts the Texture features from the segmented image and the RBFNN classifier performs the disease classification and improves the detection accuracy by optimizing the data using SSA. The investigational results of the proposed methodology exhibit the performance in terms of accuracy of disease detection is 98.47%. However, radial basis function neural network (RBFNN) achieves the diseases detection accuracy of 97.85% and support-vector machine (SVM) classifier achieves a disease detection accuracy of 97.07%. This paper proposes a method of paddy leaf disease recognition and classification using RBFNN and salp swarm algorithm. It also suggests and identifies an image analysis by framing a set of conditions for disease affected plants. The results show that the most satisfactory outcome can be gained to verify the yield of proposed methods with least effort.

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3D crosswell electromagnetic inversion based on radial basis function neural network

Fang Sinan, Zhang Zhansong, Chen Wei, Pan Heping, Peng Jun

Acta Geophysica

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2020

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Vol. 68, no. 3

711--721

EN

Crosswell electromagnetic (EM) method has fundamentally improved the horizontal detection ability of well logging and will become an increasingly promising approach for the secondary exploration of hydrocarbon reservoir. We applied orthogonal least squares (OLS) radial basis function neural network (RBFNN) based on improved Gram–Schmidt (G–S) procedure to three-dimensional (3D) crosswell EM inversion problems. In the inversion process of the simplifed crosswell model with single-grid conductivity anomalies and normal oil reservoir, compared the inversion results of other fve neural networks, OLS-RBFNN was proved to have the best global optimization ability and the fastest sample learning speed and the average inversion error of low conductivity anomalies model (4%) and oil reservoir model (9%) can meet the inversion requirements of crosswell EM method. Only the OLS-RBFNN could achieve ideal inversion results in the most concerned central area of crosswell model, and the inversion accuracy of this algorithm will be more outstanding when the model becomes more complex. Merely using the three-component time-domain crosswell EM data of two wells, the inversion of 3D medium conductivity in the crosswell dominant exploration area can be efectively realized through the nonlinear approximation of the OLS-RBFNN.

4

Zastosowanie radialnej sieci neuronowej jako adaptacyjnego regulatora prędkości napędu dwumasowego

Kamiński M., Orłowska-Kowalska T.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 5

41--44

PL

Artykuł prezentuje zastosowanie sieci neuronowej adaptowanej on-line w pętli regulacji prędkości układu napędowego z połączeniem sprężystym. W algorytmie zastosowano model neuronowy z radialnymi funkcjami aktywacji. W celu aktualizacji wartości wag oraz centrów regulatora adaptacyjnego zastosowano algorytm gradientowy. W tej części obliczeń poprawki dla aktualnych parametrów regulatora neuronowego są mnożone przez stałe determinujące stopień oddziaływania zastosowanej metody adaptacji. Charakterystycznym rozwiązaniem, prezentowanym w niniejszym artykule, jest zastosowanie modelu rozmytego w celu wyznaczenia wspomnianych współczynników skalujących. Zaprojektowany regulator został przetestowany w symulacjach oraz wykonano badania eksperymentalne z wykorzystaniem procesora sygnałowego karty dSPACE1103.Uzyskane wyniki prezentują precyzję sterowania analizowanego regulatora neuronowego oraz jego odporność na zmiany parametrów obiektu.

EN

Article presents application of neural network trained on-line in speed control loop of electrical drive with elastic connection. In algorithm neural model with radial activation function was implemented. For updates of weights and centers of adaptive controller gradient method was used. In this part of calculations correction values for adaptive controller are calibrated. Specific solution, described in paper, is application of fuzzy model for determination of scaling coefficients. Designed controller was tested in simulations and then experiment was prepared (using dSPACE1103 card). Achieved results present high precision of control and also robustness against changes of the drive parameters.