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Wyszukujesz frazę "Extreme Learning Machine" wg kryterium: Temat


Tytuł:
Fast near-infrared palmprint recognition using nonnegative matrix factorization extreme learning machine
Autorzy:
Xu, X.
Zhang, X.
Lu, L.
Deng, W.
Zuo, K
Powiązania:
https://bibliotekanauki.pl/articles/173572.pdf
Data publikacji:
2014
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
extreme learning machine
palmprint recognition
superior speed
support vector machine (SVM)
Opis:
Support vector machine and artificial neural network are widely used in classification applications. Extreme learning machine (ELM) is a novel and efficient learning algorithm based on the generalized single hidden layer feed forward networks, which performs well in classification applications. The research results have shown the superiority of ELM with the existing classical algorithms: support vector machine (SVM) and back propagation neural network. In this study, we firstly propose a novel nonnegative matrix factorization extreme learning machine (NMFELM) to improve the performance of standard ELM method. Then we propose a novel near-infrared palmprint recognition approach based on NMFELM classifier. As the test data, we use the near-infrared palmprint database provided by Hong Kong Polytechnic University. The experimental results demonstrate that the proposed NMFELM method outperforms the standard ELM- and SVM-based methods.
Źródło:
Optica Applicata; 2014, 44, 2; 285-298
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prognozowanie zawartości PM2,5 w powietrzu atmosferycznym przy użyciu sieci ELM
PM2,5 particulate pollution forecasting using ELM
Autorzy:
Siwek, K.
Baranowski, M.
Grzywacz, T.
Powiązania:
https://bibliotekanauki.pl/articles/377680.pdf
Data publikacji:
2018
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
zanieczyszczenie powietrza atmosferycznego
PM2,5
sieci neuronowe
Extreme Learning Machine
ELM
Opis:
Artykuł przedstawia nowe podejście do predykcji zawartości pyłów zawieszonych w powietrzu atmosferycznym wykorzystujące sieć neuronową typu ELM (Extreme Learning Machine). Predykcja ta dotyczy obliczenia średniego poziomu zanieczyszczenia powietrza pyłem PM2,5 na warszawskim Ursynowie z jednodniowym wyprzedzeniem. Do zrozumienia wagi problemu zawarto zwięzły opis zagrożeń, jakie niesie za sobą zanieczyszczenie powietrza drobnym pyłem zawieszonym PM2,5. W pracy przedstawiono krótki opis sieci ELM oraz zaprezentowano uzyskane wyniki prognozy. Przeprowadzono analizę uzyskanych wyników. Omówiono zasadność prognozowania oraz wskazano możliwe środki zapobiegawcze i ochronne.
The article presents a new approach to atmospheric dust prediction using an ELM (Extreme Learning Machine) neural network. This prediction concerns the calculation of the average level of PM2,5 air pollution in Warsaw's Ursynów one day ahead. To understand the significance of the problem, a brief description of the hazards posed by PM2,5 air pollution is included. The work presents a short description of the ELM network and presents the obtained forecast results. The analysis of the obtained results was carried out. The validity of forecasting was discussed and possible preventive and protective measures were indicated.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2018, 96; 307-318
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Extreme Learning Machine (ELM) do modelowania dwuwymiarowych nieliniowości w układach napędowych
Appraisal of extreme Learning Machine Effective for 2d friction modeling Ocena efektywności techniki
Autorzy:
Jastrzębski, M.
Kabziński, J.
Wasiak, G.
Zawiślak, R.
Powiązania:
https://bibliotekanauki.pl/articles/1817926.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
modelowanie tarcia
regulacja adaptacyjna
modelowanie rozmyte
silnik liniowy
Extreme Learning Machine
ELM
Opis:
The aim of this article is to discuss suitability of extreme learning machine (ELM) approach for modeling multisource 2D friction for motion control purposes. The article describes a method of obtaining data used to modulate the resistance forces, taking into account specific to the case and machine external conditions. The features of multisource friction in mechatronic systems are defined, the main aspects of friction modeling by a Standard ELM are investigated and some modifications are proposed to make it more suitable for specific demands of the discussed task. This allows to formulate some general remarks concerning properties of ELM for function approximation.
Źródło:
Prace Naukowe Instytutu Maszyn, Napędów i Pomiarów Elektrycznych Politechniki Wrocławskiej. Studia i Materiały; 2015, 71, 35; 83--94
1733-0718
Pojawia się w:
Prace Naukowe Instytutu Maszyn, Napędów i Pomiarów Elektrycznych Politechniki Wrocławskiej. Studia i Materiały
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Acoustic Source Localization Using Kernel-based Extreme Learning Machine in Distributed Microphone Array
Autorzy:
Wang, Rong
Chen, Zhe
Yin, Fuliang
Powiązania:
https://bibliotekanauki.pl/articles/1953510.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
extreme learning machine
acoustic source localization
distributed microphone array
generalized cross correlation function
Opis:
Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.
Źródło:
Archives of Acoustics; 2021, 46, 1; 67-78
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial intelligence-based hybrid forecasting models for manufacturing systems
Autorzy:
Rosienkiewicz, Maria
Powiązania:
https://bibliotekanauki.pl/articles/1841698.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
artificial neural network
support vector machine
extreme learning machine
hybrid forecasting
production planning
maintenance
quality control
Opis:
The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 2; 263-277
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evaluating the performance of Extreme Learning Machine technique for ore grade estimation
Autorzy:
Abuntori, Clara Akalanya
Al-Hassan, Sulemana
Mireku-Gyimah, Daniel
Ziggah, Yao Yevenyo
Powiązania:
https://bibliotekanauki.pl/articles/1839059.pdf
Data publikacji:
2021
Wydawca:
Główny Instytut Górnictwa
Tematy:
extreme learning machine
artificial intelligence
artificial neural network
grade estimation
kriging
ELM
sztuczna inteligencja
sztuczna sieć neuronowa
Opis:
Due to the complex geology of vein deposits and their erratic grade distributions, there is the tendency of overestimating or underestimating the ore grade. These estimated grade results determine the profitability of mining the ore deposit or otherwise. In this study, five Extreme Learning Machine (ELM) variants based on hard limit, sigmoid, triangular basis, sine and radial basis activation functions were applied to predict ore grade. The motive is that the activation function has been identified to play a key role in achieving optimum ELM performance. Therefore, assessing the extent of influence the activation functions will have on the final outputs from the ELM has some scientific value worth investigating. This study therefore applied ELMas ore grade estimator which is yet to be explored in the literature. The obtained results from the five ELM variants were analysed and compared with the state-of-the-art benchmark methods of Backpropagation Neural Network (BPNN) and Ordinary Kriging (OK). The statistical test results revealed that the ELM with sigmoid activation function (ELM-Sigmoid) was the best among all the other investigated methods (ELM-Hard limit, ELM-Triangular basis, ELM-Sine, ELM-Radial Basis, BPNN and OK). This is because the ELM-sigmoid produced the lowest MAE (0.0175), MSE (0.0005) and RMSE (0.0229) with highest R2 (91.93%) and R (95.88%) respectively. It was concluded that ELM-Sigmoid can be used by field practitioners as a reliable alternative ore grade estimation technique.
Źródło:
Journal of Sustainable Mining; 2021, 20, 2; 56-71
2300-1364
2300-3960
Pojawia się w:
Journal of Sustainable Mining
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight
Autorzy:
Chen, Xuan
Przystupa, Krzysztof
Ye, Zhiwei
Chen, Feng
Wang, Chunzhi
Liu, Jinhang
Gao, Rong
Wei, Ming
Kochan, Orest
Powiązania:
https://bibliotekanauki.pl/articles/2087016.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
short-term electric load forecast
extreme learning machine
Lévy flight
tree-seed algorithm
Kernel principal component analysis
Opis:
In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.
Źródło:
Eksploatacja i Niezawodność; 2022, 24, 2; 153--162
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fabric Defect Detection and Classifier via Multi-Scale Dictionary Learning and an Adaptive Differential Evolution Optimized Regularization Extreme Learning Machine
Wykrywanie defektów tkaniny i ich klasyfikacja poprzez zastosowanie maszyny uczącej się (ADE-RELM)
Autorzy:
Zhou, Zhiyu
Wang, Chao
Gao, Xu
Zhu, Zefei
Hu, Xudong
Zheng, Xiao
Jiang, Likai
Powiązania:
https://bibliotekanauki.pl/articles/233999.pdf
Data publikacji:
2019
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:
defect detection
multi-scale dictionary learning
regularisation extreme learning machine
adaptive differential evolution
defekty tkaniny
skuteczność wykrywania defektów
maszyna ucząca się
Opis:
To develop an automatic detection and classifier model for fabric defects, a novel detection and classifier technique based on multi-scale dictionary learning and the adaptive differential evolution algorithm optimised regularisation extreme learning machine (ADE-RELM) is proposed. Firstly in order to speed up dictionary updating under the condition of guaranteeing dictionary sparseness, k-means singular value decomposition (KSVD) dictionary learning is used. Then multi-scale KSVD dictionary learning is presented to extract texture features of textile images more accurately. Finally a unique ADE-RELM is designed to build a defect classifier model. In the training ADE-RELM classifier stage, a self-adaptive mutation operator is used to solve the parameter setting problem of the original differential evolution algorithm, then the adaptive differential evolution algorithm is utilised to calculate the optimal input weights and hidden bias of RELM. The method proposed is committed to detecting common defects like broken warp, broken weft, oil, and the declining warp of grey-level and pure colour fabrics. Experimental results show that compared with the traditional Gabor filter method, morphological operation and local binary pattern, the method proposed in this paper can locate defects precisely and achieve high detection efficiency.
W celu opracowania automatycznego modelu wykrywania i klasyfikowania defektów tkanin, zaproponowano nowatorską technikę wykrywania i klasyfikowania opartą na zastosowaniu maszyny uczącej się (ADE-RELM). Proponowana metoda ma na celu wykrywanie powszechnych defektów, takich jak przerwana osnowa i wątek oraz zabrudzenia po oleju. Wyniki eksperymentalne pokazują, że w porównaniu z tradycyjną metodą filtrów Gabora, operacją morfologiczną i lokalnym wzorcem binarnym, proponowana w artykule metoda pozwala na precyzyjne zlokalizowanie defektów i osiąga wysoką skuteczność ich wykrywania.
Źródło:
Fibres & Textiles in Eastern Europe; 2019, 1 (133); 67-77
1230-3666
2300-7354
Pojawia się w:
Fibres & Textiles in Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of prototype selection algorithms used in construction of neural networks learned by SVD
Autorzy:
Jankowski, N.
Powiązania:
https://bibliotekanauki.pl/articles/330020.pdf
Data publikacji:
2018
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
radial basis function network
extreme learning machine
kernel method
prototype selection
machine learning
k nearest neighbours
radialna funkcja bazowa
metoda jądrowa
uczenie maszynowe
metoda k najbliższych sąsiadów
Opis:
Radial basis function networks (RBFNs) or extreme learning machines (ELMs) can be seen as linear combinations of kernel functions (hidden neurons). Kernels can be constructed in random processes like in ELMs, or the positions of kernels can be initialized by a random subset of training vectors, or kernels can be constructed in a (sub-)learning process (sometimes by k-means, for example). We found that kernels constructed using prototype selection algorithms provide very accurate and stable solutions. What is more, prototype selection algorithms automatically choose not only the placement of prototypes, but also their number. Thanks to this advantage, it is no longer necessary to estimate the number of kernels with time-consuming multiple train-test procedures. The best results of learning can be obtained by pseudo-inverse learning with a singular value decomposition (SVD) algorithm. The article presents a comparison of several prototype selection algorithms co-working with singular value decomposition-based learning. The presented comparison clearly shows that the combination of prototype selection and SVD learning of a neural network is significantly better than a random selection of kernels for the RBFN or the ELM, the support vector machine or the kNN. Moreover, the presented learning scheme requires no parameters except for the width of the Gaussian kernel.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 719-733
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A fast neural network learning algorithm with approximate singular value decomposition
Autorzy:
Jankowski, Norbert
Linowiecki, Rafał
Powiązania:
https://bibliotekanauki.pl/articles/330870.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
Moore–Penrose pseudoinverse
radial basis function network
extreme learning machine
kernel method
machine learning
singular value decomposition
deep extreme learning
principal component analysis
pseudoodwrotność Moore–Penrose
radialna funkcja bazowa
maszyna uczenia ekstremalnego
uczenie maszynowe
analiza składników głównych
Opis:
The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms. With fast learning we can examine more learning scenarios for a given problem, especially in the case of meta-learning. In this article we focus on the construction of a much faster learning algorithm and its modifications, especially for nonlinear versions of neural networks. The main idea of this algorithm lies in the usage of fast approximation of the Moore–Penrose pseudo-inverse matrix. The complexity of the original singular value decomposition algorithm is O(mn2). We consider algorithms with a complexity of O(mnl), where l < n and l is often significantly smaller than n. Such learning algorithms can be applied to the learning of radial basis function networks, extreme learning machines or deep ELMs, principal component analysis or even missing data imputation.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 581-594
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł

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