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Tytuł:
2D Cadastral Coordinate Transformation using extreme learning machine technique
Autorzy:
Ziggah, Y. Y.
Issaka, Y.
Laari, P. B.
Hui, Z.
Powiązania:
https://bibliotekanauki.pl/articles/145372.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
transformacja współrzędnych
sieci neuronowe
dane geodezyjne
sieć radialna
coordinate transformation
extreme learning machine
backpropagation neural network
radial basis function neural network
geodetic datum
Opis:
Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.
Źródło:
Geodesy and Cartography; 2018, 67, 2; 321-343
2080-6736
2300-2581
Pojawia się w:
Geodesy and Cartography
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vehicles Classification Using the HRBF Neural Network
Klasyfikacja pojazdów z wykorzystaniem sieci neuronowej HRBF
Autorzy:
Wantoch-Rekowski, R.
Powiązania:
https://bibliotekanauki.pl/articles/305921.pdf
Data publikacji:
2011
Wydawca:
Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:
sieci neuronowe
klasyfikacja sieci
zbiór uczący
Hyper Radial Basis Function network HRBF
neural networks
networks classification
learning set
HRBF
Opis:
The paper presents the problem of using a neural network for military vehicle classification on the basis of ground vibration. One of the main elements of the system is a unit called the geophone. This unit allows to measure the amplitude of ground vibration in each direction for a certain period of time. The value of the amplitude is used to fix the characteristic frequencies of each vehicle. If we want to fix the main frequency it is necessary to use the Fourier transform. In this case the fast Fourier transform FFT was used. Since the neural network (Hyper Radial Basis Function network) was used, a learning set has to be prepared. Please find the attached results of using the HRBF neural network, which include: examples of learning, validation and test sets, the structure of the networks and the learning algorithm, learning and testing results.
W opracowaniu przedstawiono zagadnienie wykorzystania sieci neuronowej do klasyfikacji określonych typów pojazdów na podstawie analizy amplitudy drgań gruntu. Jednym z elementów systemu do pomiaru amplitudy drgań gruntu jest geofon. Umożliwia on pomiar amplitudy drgań gruntu w wybranym kierunku dla określonego przedziału czasu. Wartość wyznaczonej amplitudy wykorzystywana jest do wyznaczenia charakterystycznych częstotliwości drgań dla poszczególnych pojazdów. Do wyznaczenia charakterystycznych częstotliwości wykorzystywana jest transformata Fouriera FFT. Do klasyfikacji wykorzystana została sieć neuronowa z radialną funkcją aktywacji, dlatego też wymagane jest przygotowanie odpowiedniego zbioru uczącego. W opracowaniu przedstawiono wyniki użycia sieci HRBF. Przedstawiono strukturę oraz zawartość zbioru uczącego.
Źródło:
Biuletyn Instytutu Systemów Informatycznych; 2011, 7; 47-52
1508-4183
Pojawia się w:
Biuletyn Instytutu Systemów Informatycznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification
Autorzy:
Wang, Yixuan
Yuan, LiPing
Khishe, Mohammad
Moridi, Alaveh
Mohammadzade, Fallah
Powiązania:
https://bibliotekanauki.pl/articles/1953523.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
classifiers
radial basis function neural network
sine-cosine algorithm
sonar
Opis:
Radial basis function neural networks (RBF NNs) are one of the most useful tools in the classification of the sonar targets. Despite many abilities of RBF NNs, low accuracy in classification, entrapment in local minima, and slow convergence rate are disadvantages of these networks. In order to overcome these issues, the sine-cosine algorithm (SCA) has been used to train RBF NNs in this work. To evaluate the designed classifier, two benchmark underwater sonar classification problems were used. Also, an experimental underwater target classification was developed to practically evaluate the merits of the RBF-based classifier in dealing with high-dimensional real world problems. In order to have a comprehensive evaluation, the classifier is compared with the gradient descent (GD), gravitational search algorithm (GSA), genetic algorithm (GA), and Kalman filter (KF) algorithms in terms of entrapment in local minima, the accuracy of the classification, and the convergence rate. The results show that the proposed classifier provides a better performance than other compared classifiers as it classifies the sonar datasets 2.72% better than the best benchmark classifier, on average.
Źródło:
Archives of Acoustics; 2020, 45, 4; 753-764
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of two interpolation methods for empirical mode decomposition based evaluation of radiographic femur bone images
Autorzy:
Udhayakumar, G.
Sujatha, C. M.
Ramakrishnan, S.
Powiązania:
https://bibliotekanauki.pl/articles/306313.pdf
Data publikacji:
2013
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
anisotropy
bone mineral density
hierarchical b-spline
intrinsic mode function
radial basis function multiquadratic
trabecular soft bone
texture analysis
anizotropia
gęstość mineralna kości
analiza tekstury
Opis:
Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode decomposition method and the choice of appropriate interpolation depends on specific structure of the problem. In this work, two interpolation methods of bi-dimensional empirical mode decomposition are analyzed to characterize the trabecular femur bone architecture of radiographic images. The trabecular bone regions of normal and osteoporotic femur bone images (N = 40) recorded under standard condition are used for this study. The compressive and tensile strength regions of the images are delineated using pre-processing procedures. The delineated images are decomposed into their corresponding intrinsic mode functions using interpolation methods such as Radial basis function multiquadratic and hierarchical b-spline techniques. Results show that bi-dimensional empirical mode decomposition analyses using both interpolations are able to represent architectural variations of femur bone radiographic images. As the strength of the bone depends on architectural variation in addition to bone mass, this study seems to be clinically useful.
Źródło:
Acta of Bioengineering and Biomechanics; 2013, 15, 2; 73-80
1509-409X
2450-6303
Pojawia się w:
Acta of Bioengineering and Biomechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Meshless local radial point interpolation (MLRPI) for generalized telegraph and heat diffusion equation with non-local boundary conditions
Autorzy:
Shivanian, E.
Khodayari, A.
Powiązania:
https://bibliotekanauki.pl/articles/279501.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
non-local boundary condition
meshless local radial point interpolation (MLRPI) method
local weak formulation
radial basis function
telegraph equation
Opis:
In this paper, the meshless local radial point interpolation (MLRPI) method is formulated to the generalized one-dimensional linear telegraph and heat diffusion equation with non-local boundary conditions. The MLRPI method is categorized under meshless methods in which any background integration cells are not required, so that all integrations are carried out locally over small quadrature domains of regular shapes, such as lines in one dimensions, circles or squares in two dimensions and spheres or cubes in three dimensions. A technique based on the radial point interpolation is adopted to construct shape functions, also called basis functions, using the radial basis functions. These shape functions have delta function property in the frame work of interpolation, therefore they convince us to impose boundary conditions directly. The time derivatives are approximated by the finite difference time- -stepping method. We also apply Simpson’s integration rule to treat the non-local boundary conditions. Convergency and stability of the MLRPI method are clarified by surveying some numerical experiments.
Źródło:
Journal of Theoretical and Applied Mechanics; 2017, 55, 2; 571-582
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties
Autorzy:
Peng, Cheng
Zhang, Anguo
Li, Junyu
Powiązania:
https://bibliotekanauki.pl/articles/2055174.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
multiagent system
radial basis function
RBF neural network
sliding mode control
cooperative control
system wieloagentowy
radialna funkcja bazowa
sieć neuronowa RBF
sterowanie ślizgowe
Opis:
The consensus problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty is studied. We design an online-update radial basis function (RBF) neural network based distributed adaptive control protocol, where the sliding model control method is also applied to eliminate the influence of the external disturbance and system uncertainty. System consensus is verified by using the Lyapunov stability theorem, and sufficient conditions for cooperative uniform ultimately boundedness (CUUB) are also derived. Two simulation examples demonstrate the effectiveness of the proposed method for both homogeneous and heterogeneous MASs.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 635--645
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Radial Basis Function Neural Network based on Growing Neural Gas Network applied for evaluation of oil agglomeration process efficiency
Autorzy:
Marcin, Kamiński
Stanisławski, Radosław
Bastrzyk, Anna
Powiązania:
https://bibliotekanauki.pl/articles/1450770.pdf
Data publikacji:
2020
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
oil agglomeration modeling
dolomite
Radial Basis Function Neural Network
Growing Neural Gas Network
Opis:
In this study, the neural model for modeling of oil agglomeration of dolomite in the presence of anionic and cationic surfactants (sodium oleate and dodecylammonium hydrochloride) was implemented. The effect of surfactants concentration, oil dosage, time of mixing, pH, and mixing speed of the impeller in the process recovery were investigated using Radial Basis Function Neural Network (RBFNN). A significant problem in this modeling, was the selection of the structure of the neural network. In algorithms based on the RBFNN, the issue mentioned relates to the number of nodes in the determination of the hidden layer. Also, the distribution of functions in data space is significant. In the proposed solution, at this stage of the neural model design, the Growing Neural Gas Network (GNGN) was implemented. Such a procedure introduced automation of the calculation process. The centers were obtained from the GNGN and the structure (number of radial neurons) can be approximated based on a simple searching algorithm. The idea of the data calculations was implemented as an original algorithm that can be easily transferred to Matlab, Python, or Octave software. The values predicted from the neural networks model were in good agreement with the experimental data. Thus, the RBFNN-GNGN model used in this study, can be employed as a reliable and accurate method to predict, and in the future to optimize the performance of oil agglomeration process.
Źródło:
Physicochemical Problems of Mineral Processing; 2020, 56, 6; 194-205
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie metody funkcji radialnych do analizy akustycznych drgań własnych
Application radial basis function method for solve acoustical eigen value problem
Autorzy:
Majkut, L.
Olszewski, R.
Powiązania:
https://bibliotekanauki.pl/articles/251177.pdf
Data publikacji:
2013
Wydawca:
Instytut Naukowo-Wydawniczy TTS
Tematy:
metoda funkcji radialnych
drgania własne
kabina pojazdu
radial basis function method
eigenvalues
vehicle cabin
Opis:
W artykule opisano możliwości zastosowania Metody Funkcji Radialnych do wyznaczania akustycznych częstotliwości drgań własnych w przestrzeniach ograniczonych. Porównano metodyki popularnych narzędzi obliczeniowych takich jak Metoda Elementów Skończonych i Metoda Elementów Brzegowych wraz ze wskazaniem wad i zalet do Metody Funkcji Radialnych.
In the paper the possibility of Radial Basis Function Method for the calculation of acoustic eigenvalues is described. The proposed method is compared with other numerical methods of wave acoustic. The advantages and disadvantages of Finite Element Method and Boundary Element Method are described and compared to proposed Radial Basis Function Method.
Źródło:
TTS Technika Transportu Szynowego; 2013, 10; 1109-1115, CD
1232-3829
2543-5728
Pojawia się w:
TTS Technika Transportu Szynowego
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie radialnych funkcji bazowych do analizy akustycznych drgań własnych kabiny pojazdu
Application of Radial Basis Function Method to the acoustic eigenvalues problem analysis of vehicle cabin
Autorzy:
Majkut, L.
Olszewski, R.
Powiązania:
https://bibliotekanauki.pl/articles/310747.pdf
Data publikacji:
2017
Wydawca:
Instytut Naukowo-Wydawniczy "SPATIUM"
Tematy:
analiza akustyczna
eksploatacja pojazdów
radialne funkcje bazowe
acoustic analysis
exploitation vehicle
radial basis function
Opis:
W artykule opisano możliwości zastosowania Metody Funkcji Radialnych do wyznaczania akustycznych częstotliwości drgań własnych w przestrzeniach ograniczonych. Porównano metodyki popularnych narzędzi obliczeniowych takich jak Metoda Elementów Skończonych i Metoda Elementów Brzegowych wraz ze wskazaniem wad i zalet do Metody Funkcji Radialnych.
In the paper the possibility of Radial Basis Function Method for the calculation of acoustic eigenvalues is described. The proposed method is compared with other numerical methods of wave acoustic. The advantages and disadvantages of Finite Element Method and Boundary Element Method are described and compared to proposed Radial Basis Function Method.
Źródło:
Autobusy : technika, eksploatacja, systemy transportowe; 2017, 18, 12; 1110-1113, CD
1509-5878
2450-7725
Pojawia się w:
Autobusy : technika, eksploatacja, systemy transportowe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applying LCS to affective image classification in spatial - frequency domain
Autorzy:
Lee, P. -M.
Hsiao, T.-C.
Powiązania:
https://bibliotekanauki.pl/articles/91808.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
image classification
pattern recognition
Hilbert-Huang transform
HHT
empirical mode decomposition
EMD
Hilbert transform
HT
Extended Classifier Systems
XCSs
Area Under Curve
AUC
Radial-Basis Function Network
RBF Network
LCS
Opis:
Recent studies have utilizes color, texture, and composition information of images to achieve affective image classification. However, the features related to spatial-frequency domain that were proven to be useful for traditional pattern recognition have not been tested in this field yet. Furthermore, the experiments conducted by previous studies are not internationally-comparable due to the experimental paradigm adopted. In addition, contributed by recent advances in methodology, that are, Hilbert-Huang Transform (HHT) (i.e. Empirical Mode Decomposition (EMD) and Hilbert Transform (HT)), the resolution of frequency analysis has been improved. Hence, the goal of this research is to achieve the affective image-classification task by adopting a standard experimental paradigm introduces by psychologists in order to produce international-comparable and reproducible results; and also to explore the affective hidden patterns of images in the spatial-frequency domain. To accomplish these goals, multiple human-subject experiments were conducted in laboratory. Extended Classifier Systems (XCSs) was used for model building because the XCS has been applied to a wide range of classification tasks and proved to be competitive in pattern recognition. To exploit the information in the spatial-frequency domain, the traditional EMD has been extended to a two-dimensional version. To summarize, the model built by using the XCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the XCS was compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used for classification tasks. Contributed by proper selection of features for model building, user-independent findings were obtained. For example, it is found that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation. The effect of hue, saturation, and brightness; is also presented.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 99-123
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Free vibration of structures by radial basis function – pseudospectral method
Autorzy:
Krowiak, A.
Powiązania:
https://bibliotekanauki.pl/articles/128260.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Instytut Mechaniki Stosowanej
Tematy:
meshless methods
radial basis function
pseudospectral methods
metody bezsiatkowe
radialna funkcja bazowa
metoda pseudospektralna
Opis:
The paper deals with the use of the radial basis function-pseudospectral method in vibration analysis of twodimensional mechanical structures. The method combines meshless features of radial basis function (RBF) with efficiency and simplicity of the pseudospectral method. In present work the main emphasis is laid on appropriate assumption of the interpolant for the sought function due to the number of the boundary conditions in analysed problem. This interpolation function enables to obtain the weighting coefficients for derivative approximation in a governing equation. The method is applied to free vibration analysis of arbitrarily shaped membrane and plate.
Źródło:
Vibrations in Physical Systems; 2014, 26; 113-120
0860-6897
Pojawia się w:
Vibrations in Physical Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Integrated fault-tolerant control of a quadcopter UAV with incipient actuator faults
Autorzy:
Kantue, Paulin
Pedro, Jimoh O.
Powiązania:
https://bibliotekanauki.pl/articles/2172129.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
fault tolerant control
quadrocopter
incipient actuator fault
radial basis function
neural network
sterowanie tolerujące uszkodzenia
kwadrokopter
radialna funkcja bazowa
sieć neuronowa
Opis:
An integrated approach to the fault-tolerant control (FTC) of a quadcopter unmanned aerial vehicle (UAV) with incipient actuator faults is presented. The framework is comprised of a radial basis function neural network (RBFNN) fault detection and diagnosis (FDD) module and a reconfigurable flight controller (RFC) based on the extremum seeking control approach. The dynamics of a quadcopter subject to incipient actuator faults are estimated using a nonlinear identification method comprising a continuous forward algorithm (CFA) and a modified golden section search (GSS) one. A time-difference-of-arrival (TDOA) method and the post-fault system estimates are used within the FDD module to compute the fault location and fault magnitude. The impact of bi-directional uncertainty and FDD detection time on the overall FTC performance and system recovery is assessed by simulating a quadcopter UAV during a trajectory tracking mission and is found to be robust against incipient actuator faults during straight and level flight and tight turns.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 4; 601--617
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex
Autorzy:
Kalinchyk, Vasyl
Meita, Olexandr
Pobigaylo, Vitalii
Borychenko, Olena
Kalinchyk, Vitalii
Powiązania:
https://bibliotekanauki.pl/articles/2174915.pdf
Data publikacji:
2022
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
classification
modelling
neural network
radial basis function network
RBF
multilayer perceptron
MLP
Opis:
This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognise the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for constructing neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters are made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification based on artificial neural networks is offered.
Źródło:
Rocznik Ochrona Środowiska; 2022, 24; 26--40
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
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
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
Wyświetlanie 1-15 z 24

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