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Tytuł:
A strategy in sports betting with the nearest neighbours search and genetic algorithms
Autorzy:
Borycki, D.
Powiązania:
https://bibliotekanauki.pl/articles/106184.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
sports betting
nearest neighbour search
genetic algorithm
English Premier League
Opis:
The point of sports betting is not merely to correctly predict the outcome of a game, but to actually win on a bet. We propose a model of sports betting that uses the nearest neighbours search and genetic algorithms to do the job. It uses data on the teams playing, their respective formations, individual players, results of previous games, as well as odds offered by bookmakers. The model has been trained using the data from the seasons 2002/03 until 2008/09 of the English Premier League and tested against the already played games of the seasons 2009/10 and 2010/11.
Źródło:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2011, 11, 1; 7-13
1732-1360
2083-3628
Pojawia się w:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Voice command recognition using hybrid genetic algorithm
Autorzy:
Wroniszewska, M.
Dziedzic, J.
Powiązania:
https://bibliotekanauki.pl/articles/1955309.pdf
Data publikacji:
2010
Wydawca:
Politechnika Gdańska
Tematy:
voice command recognition
genetic algorithms
K-nearest neighbour
hybrid approach
Opis:
Speech recognition is a process of converting the acoustic signal into a set of words, whereas voice command recognition consists in the correct identification of voice commands, usually single words. Voice command recognition systems are widely used in the military, control systems, electronic devices, such as cellular phones, or by people with disabilities (e.g., for controlling a wheelchair or operating a computer system). This paper describes the construction of a model for a voice command recognition system based on the combination of genetic algorithms (GAs) and K-nearest neighbour classifier (KNN). The model consists of two parts. The first one concerns the creation of feature patterns from spoken words. This is done by means of the discrete Fourier transform and frequency analysis. The second part constitutes the essence of the model, namely the design of the supervised learning and classification system. The technique used for the classification task is based on the simplest classifier – K-nearest neighbour algorithm. GAs, which have been demonstrated as a good optimization and machine learning technique, are applied to the feature extraction process for the pattern vectors. The purpose and main interest of this work is to adapt such a hybrid approach to the task of voice command recognition, develop an implementation and to assess its performance. The complete model of the system was implemented in the C++ language, the implementation was subsequently used to determine the relevant parameters of the method and to improve the approach in order to obtain the desired accuracy. Different variants of GAs were surveyed in this project and the influence of particular operators was verified in terms of the classification success rate. The main finding from the performed numerical experiments indicates the necessity of using genetic algorithms for the learning process. In consequence, a highly accurate recognition system was obtained, providing 94.2% correctly classified patterns. The hybrid GA/KNN approach constituted a significant improvement over the simple KNN classifier. Moreover, the training time required for the GA to learn the given set of words was found to be on a level that is acceptable for the efficient functioning of the voice command recognition system.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2010, 14, 4; 377-396
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Przygotowanie danych do wyznaczania wskaźnika reprezentatywności Ru na przykładzie Jednolitej Części Wód Podziemnych nr 37
Preparation of source data in GIS programs to calculate the representativity index Ru on the example of Groundwater Body no. 37
Autorzy:
Kachnic, M.
Powiązania:
https://bibliotekanauki.pl/articles/2062622.pdf
Data publikacji:
2012
Wydawca:
Państwowy Instytut Geologiczny – Państwowy Instytut Badawczy
Tematy:
JCWPd
wskaźnik reprezentatywności
GIS
GWstat
GWB
representativity index
nearest neighbour index
Opis:
Zgodnie z Dyrektywami Unii Europejskiej do oceny stanu ilościowego i jakościowego wód podziemnych wykorzystuje się sieć punktów obserwacyjnych. Do oceny reprezentatywności sieci pomiarowej zaleca się w tych dokumentach obliczanie wskaźnika reprezentatywności Ru. Wskaźnik Ru jest istotnym uzupełnieniem charakterystyki jednolitej części wód podziemnych (JCWPd). Do przygotowania danych do obliczeń Ru w programie GWstat wykorzystano programy ArcGis i IDRISI. Wyznaczanie wartości Ru wykonano na podstawie lokalizacji otworów studziennych zarejestrowanych w bazie danych Banku HYDRO w obszarze JCWPd 37. Wyliczony z wykorzystaniem programu GWstat wskaźnik Ru wyniósł 57%.
The Water Framework Directive requires Member States to regularly monitor and protect the quality and quantity of groundwater. Assessment of spatial representativity within each groundwater body (GWB) is also recommended and the representativity index Ru has been proposed. For Ru calculation, GWstat software is suggested (WFD-GW, 2001). Due to difficulties in preparing raw data in the GWstat software, the author used ArcGis and IDRISI programs. The ArcGis was used to digitize the border of GWB 37 and location of boreholes in GWB 37 from the Polish Hydrogeological Survey GIS server (e-PSH). The IDRISI program was used to prepare the matrix of GWB. Initial estimation of Ru calculated in the GWstat is 57%.
Źródło:
Biuletyn Państwowego Instytutu Geologicznego; 2012, 451 Hydrogeologia z. 13; 123--128
0867-6143
Pojawia się w:
Biuletyn Państwowego Instytutu Geologicznego
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Some Nonparametric Estimators of Regression Function
Wybrane nieparametryczne estymatory funkcji regresji
Autorzy:
Baszczyńska, Aleksandra
Powiązania:
https://bibliotekanauki.pl/articles/906895.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
regression function
kernel function
smoothing parameter
k-nearest neighbour method
kernel method
Opis:
In the paper some nonparametric estimators of regression function are studied: Nadaraya-Watson estimator and k-nearest neighbour one. Properties of these estimators and possibilities of using them in practice are taken into consideration. A comparative study of the two estimators is presented. Different techniques of choosing method’s parameters (kernel function, smoothing parameter h and parameter k) are used in this study to choose the optimal ones. Some practical rules are proposed and they are used in this study.
W pracy przedstawiono wybrane dwa nieparametryczne estymatory funkcji regresji: estymator jądrowy Nadaraya-Watsona oraz estymator k-najbliższego sąsiada. Podano ich własności, możliwości wykorzystania oraz dokonano porównania tych estymatorów. Przedstawiono również przykład zastosowania estymatora jądrowego regresji z uwzględnieniem właściwego doboru parametrów metody (funkcji jądra i parametru wygładzania h) oraz estymatora k-najbliższego sąsiada z uwzględnieniem właściwego doboru parametru k. Zaproponowano również praktyczne zasady wyboru parametrów estymacji funkcji regresji i wykorzystano je w przykładzie.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2007, 206
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An approach to unsupervised classification
Autorzy:
Przybyła, T.
Pander, T.
Horoba, K.
Kupka, T.
Matonia, A.
Powiązania:
https://bibliotekanauki.pl/articles/333363.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
klasyfikacja
grupowanie rozmyte
klasyfikacja nienadzorowana
klasyfikator najbliższych sąsiadów
classification
fuzzy clustering
unsupervised classification
nearest neighbour classifier
Opis:
Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the unsupervised classification with the popular classifiers. The fuzzy clustering method is used to create a learning set. The learning set includes only these patterns that are the best representative of each class in the input dataset. The numerical experiment uses an artificial dataset as well as the medical datasets (PIMA, Wisconsin Breast Cancer) and illustrates the usefulness of the proposed method.
Źródło:
Journal of Medical Informatics & Technologies; 2011, 17; 105-111
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Reduction of reference set with the method of cutting hyperplanes
Autorzy:
Sierszeń, A.
Powiązania:
https://bibliotekanauki.pl/articles/333155.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
reguła NN
pattern recognition
nearest neighbour rule
reference set condensation
reference set reduction
method of cutting hyperplanes
Opis:
Reduction of this type may help to solve one of the greatest problems in pattern recognition, i.e. the compromise between the time of making a decision and its correctness. In the analysis of biomedical data, classification time is less important than certainty that classification is correct, i.e. that reliability of classification is accepted by the algorithm’s operator. It is usually possible to reduce the number of wrong decisions, using a more complex recognition algorithm and, as a consequence, increasing classification time. However, with a large quantity of data, this time may be considerably reduced by condensation of a set. Condensation of a set presented in this article is incremental, i.e. formation of the condensed reference set begins from a set containing one element. In each step, the size of the set is increased with one object. This algorithm consists in dividing the feature space with hyperplanes determined with pairs of the mutually furthest points. The hyperplanes are orthogonal to segments linking pairs of the mutually furthest points and they go through their centre.
Źródło:
Journal of Medical Informatics & Technologies; 2009, 13; 215-220
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A differential evolution approach to dimensionality reduction for classification needs
Autorzy:
Martinović, G.
Bajer, D.
Zorić, B.
Powiązania:
https://bibliotekanauki.pl/articles/331498.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
classification
differential evolution
feature subset selection
k-nearest neighbour algorithm
wrapper method
ewolucja różnicowa
selekcja cech
algorytm najbliższego sąsiada
Opis:
The feature selection problem often occurs in pattern recognition and, more specifically, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features, which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold cross-validation on the archive solutions and selecting the best one. Experimental analysis is conducted on several standard test sets. The classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis shows that the proposed approach successfully determines good feature subsets which may increase the classification accuracy.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 111-122
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of mortality rates in heart failure patients with data mining methods
Autorzy:
Bohacik, J.
Kambhampati, C.
Davis, D. N.
Cleland, J. G. F.
Powiązania:
https://bibliotekanauki.pl/articles/908867.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
heart failure
data mining
prediction of mortality rates
home telemonitoring
Bayesian network method
decision tree method
neural network method
nearest neighbour method
Opis:
Heart failure is one of the severe diseases which menace the human health and affect millions of people. Half of all patients diagnosed with heart failure die within four years. For the purpose of avoiding life-threatening situations and minimizing the costs, it is important to predict mortality rates of heart failure patients. As part of a HEIF-5 project, a data mining study was conducted aiming specifically at extracting new knowledge from a group of patients suffering from heart failure and using it for prediction of mortality rates. The methodology of knowledge discovery in databases is analyzed within the framework of home telemonitoring. Several data mining methods such as a Bayesian network method, a decision tree method, a neural network method and a nearest neighbour method are employed. The accuracy for the data mining methods from the point of view of avoiding life-threatening situations and minimizing the costs is discussed. It seems that the decision tree method achieves the best accuracy results and is also interpretable for the clinicians.
Źródło:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2013, 13, 1; 7-16
1732-1360
2083-3628
Pojawia się w:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity metrics
Autorzy:
Wang, Yang
Xiao, Yu
Lai, Jianhui
Chen, Yanyan
Powiązania:
https://bibliotekanauki.pl/articles/949848.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
missing traffic data
similarity metrics
K-nearest neighbour method
stochastic characteristics
metoda porównywania danych
metryki podobieństwa
metoda najbliższego sąsiada
cechy stochastyczne
Opis:
Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.
Źródło:
Archives of Transport; 2020, 54, 2; 59-73
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast reduction of large dataset for nearest neighbor classifier
Autorzy:
Raniszewski, M.
Powiązania:
https://bibliotekanauki.pl/articles/333106.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
metody podziału
metody redukcji
przetwarzanie obrazów
reguła najbliższego sąsiada
pomiar reprezentatywny
division methods
reduction methods
images processing
nearest neighbour rule
representative measure
Opis:
Accurate and fast classification of large data obtained from medical images is very important. Proper images (data) processing results to construct a classifier, which supports the work of doctors and can solve many medical problems. Unfortunately, Nearest Neighbor classifiers become inefficient and slow for large datasets. A dataset reduction is one of the most popular solution to this problem, but the large size of a dataset causes long time of a reduction phase for reduction algorithms. A simple method to overcome the large dataset reduction problem is a dataset division into smaller subsets. In this paper five different methods of large dataset division are considered. The received subsets are reduced by using an algorithm based on representative measure. The reduced subsets are combined to form the reduced dataset. The experiments were performed on a large (almost 82 000 samples) two–class dataset dating from ultrasound images of certain 3D objects found in a human body.
Źródło:
Journal of Medical Informatics & Technologies; 2010, 16; 111-116
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Monte Carlo investigation of two distance measures between statistical populations and their application to cluster analysis
Miary odległości pomiędzy populacjami statystycznymi i ich zastosowanie w analizie skupień - Badanie Monte Carlo
Autorzy:
Rossa, Agnieszka
Powiązania:
https://bibliotekanauki.pl/articles/904614.pdf
Data publikacji:
1997
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
hierarchical cluster analysis methods
robustness of the nearest neighbour method
the Mahalanobis distance
the Kullback-Leibler divergence
the Marczewski-Steinhaus distance measure
Opis:
The paper deals with a simulation study of one of the well-known hierarchical cluster analysis methods applied to classifying the statistical populations. In particular, the problem of clustering the univariate normal populations is studied. Two measures of the distance between statistical populations are considered: the Mahalanobis distance measure which is defined for normally distributed populations under assumption that the covariance matrices are equal and the Kullback-Leibler divergence (the so called Generalized Mahalanobis Distance) the use of which is extended on populations of any distribution. The simulation study is concerned with the set of 15 univariate normal populations, variances of which are chanched during successive steps. The aim is to study robustness of the nearest neighbour method to departure from the variance equality assumption when the Mahalanobis distance formula is applied. The differences between two cluster families, obtained for the same set of populations but with the different distance matrices applied, are studied. The distance between both final cluster sets is measured by means of the Marczewski-Steinhaus distance.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 1997, 141
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatyczna detekcja płaszczyzn w chmurze punktów w oparciu o algorytm RANSAC i elementy teorii grafów
RANSAC algorithm and elements of graph thory for automatic plane detection in 3D point cloud
Autorzy:
Poręba, M.
Goulette, F.
Powiązania:
https://bibliotekanauki.pl/articles/129757.pdf
Data publikacji:
2012
Wydawca:
Stowarzyszenie Geodetów Polskich
Tematy:
chmura punktów
segmentacja
RANSAC
graf
algorytm najbliższego sąsiada
etykietowanie
spójny komponent
point cloud
segmentation
graph
k-nearest neighbour algorithm
labelling
connected component
Opis:
Artykuł przedstawia metodę automatycznego wyodrębniania punktów modelujących płaszczyzny w chmurach punktów pochodzących z mobilnego bądź statycznego skaningu laserowego. Zaproponowany algorytm bazuje na odpornym estymatorze RANSAC umożliwiającym iteracyjną detekcję płaszczyzn w zbiorze cechującym się znacznym poziomem szumu pomiarowego i ilością punktów odstających. Aby zoptymalizować jego działanie, dla każdej wykrytej płaszczyzny uwzględniono relacje sąsiedztwa pomiędzy punktami przynależnymi. W tym celu zastosowano podejście oparte na teorii grafów, gdzie chmura punktów traktowana jest jako graf nieskierowany, dla którego poszukiwane są spójne składowe. Wprowadzona modyfikacja obejmuje dwa dodatkowe etapy: ustalenie najbliższych sąsiadów dla każdego punktu wykrytej płaszczyzny wraz z konstrukcją listy sąsiedztwa oraz etykietowanie spójnych komponentów. Rezultaty uzyskane pokazują iż algorytm poprawnie wykrywa płaszczyzny modelujące, przy czym niezbędny jest odpowiedni dobór parametrów początkowych. Czas przetwarzania uzależniony jest przede wszystkim od liczby punktów w chmurze. Nadal jednak aktualny pozostaje problem wrażliwości algorytmu RANSAC na niską gęstość chmury oraz nierównomierne rozmieszczenie punktów.
Laser scanning techniques play very important role in acquiring of spatial data. Once the point cloud is available, the data processing must be performed to achieve the final products. The segmentation is an inseparable step in point cloud analysis in order to separate the fragments of the same semantic meaning. Existing methods of 3D segmentation are divided into two categories. The first family contains algorithms functioning on principle of fusion, such as surface growing approach or split-merge algorithm. The second group consists of techniques making possible the extraction of features defined by geometric primitives i.e.: sphere, cone or cylinder. Hough transform and RANSAC algorithm (RANdom SAmple Consensus) are classified to the last of aforementioned groups. This paper studies techniques of point cloud segmentation such as fully automatic plane detection. Proposed method is based on RANSAC algorithm providing an iterative plane modelling in point cloud affected by considerable noise. The algorithm is implemented sequentially, therefore each successive plane represented by the largest number of points is separated. Despite all advantages of RANSAC, it sometimes gives erroneous results. The algorithm looks for the best plane without taking into account the particularity of the object. Consequently, RANSAC may combine points belonging to different objects into one single plane. Hence, RANSAC algorithm is optimized by analysing the adjacency relationships of neighbouring points for each plane. The approach based on graph theory is thus proposed, where the point cloud is treated as undirected graph for which connected components are extracted. Introduced method consists of three main steps: identification of k-nearest neighbours for each point of detected plane, construction of adjacency list and finally connected component labelling. Described algorithm was tested with raw point clouds, unprocessed in sense of filtration. All the numerical tests have been performed on real data, characterized by different resolutions and derived from both mobile and static laser scanning techniques. Obtained results show that proposed algorithm properly separates points for particular planes, whereas processing time is strictly dependent on number of points within the point cloud. Nevertheless, susceptibility of RANSAC algorithm to low point cloud density as well as irregular points distribution is still animportant problem. This paper contains literature review in subject of existing methods for plane detection in data set. Moreover, the description for proposed algorithm based on RANSAC, its principle, as well as the results is also presented.
Źródło:
Archiwum Fotogrametrii, Kartografii i Teledetekcji; 2012, 24; 301-310
2083-2214
2391-9477
Pojawia się w:
Archiwum Fotogrametrii, Kartografii i Teledetekcji
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pattern layer reduction for a generalized regression neural network by using a self-organizing map
Autorzy:
Kartal, S.
Oral, M.
Ozyildirim, B. M.
Powiązania:
https://bibliotekanauki.pl/articles/329728.pdf
Data publikacji:
2018
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
generalized regression neural network
artificial neural network
self organizing map
nearest neighbour
reduced dataset
sztuczna sieć neuronowa
mapa samoorganizująca
metoda najbliższych sąsiadów
redukcja zbioru danych
Opis:
In a general regression neural network (GRNN), the number of neurons in the pattern layer is proportional to the number of training samples in the dataset. The use of a GRNN in applications that have relatively large datasets becomes troublesome due to the architecture and speed required. The great number of neurons in the pattern layer requires a substantial increase in memory usage and causes a substantial decrease in calculation speed. Therefore, there is a strong need for pattern layer size reduction. In this study, a self-organizing map (SOM) structure is introduced as a pre-processor for the GRNN. First, an SOM is generated for the training dataset. Second, each training record is labelled with the most similar map unit. Lastly, when a new test record is applied to the network, the most similar map units are detected, and the training data that have the same labels as the detected units are fed into the network instead of the entire training dataset. This scheme enables a considerable reduction in the pattern layer size. The proposed hybrid model was evaluated by using fifteen benchmark test functions and eight different UCI datasets. According to the simulation results, the proposed model significantly simplifies the GRNN’s structure without any performance loss.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2018, 28, 2; 411-424
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie wybranych modeli nieliniowych do prognozy ilości osadu nadmiernego
Application of Selected Nonlinear Methods to Forecast the Amount of Excess Sludge
Autorzy:
Gawdzik, J.
Szeląg, B.
Bezak-Mazur, E.
Stoińska, R.
Powiązania:
https://bibliotekanauki.pl/articles/1818016.pdf
Data publikacji:
2016
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
osady nadmierne
oczyszczanie ścieków
metoda wektorów nośnych
k–najbliższego sąsiada
drzewa wzmacniane
excess sludge
wastewater treatment
support vector machine (SVM)
k–nearest neighbour
boosted trees
Opis:
Operation of a sewage treatment plant is a complex task because it requires maintaining the parameters of its activities at the appropriate level in order to achieve the desired effect of reducing pollution and reduce the flow of sediment discharged from the biological reactor. The basis for predicting the amount of excess sludge and operational parameters WWTP can provide physical models describing the biochemical changes occurring in the reactor, in which the input parameters, ie. Indicators of effluent quality and quantity of wastewater are modeled in advance. However, due to numerous interactions and uncertainty of the data in the physical models and forecast errors parameters of the inlet to the treatment plant Simulation results may be affected by significant errors. Therefore, to minimize the prediction error parameters of operation of the technological objects deliberate use of a black box model. In these models at the stage of learning is generated model structure underlying the projections analyzed the operating parameters of the plant. This publication presents the possibility of the use of methods: support vector, k – nearest neighbour and trees reinforced to predict the amount of the resulting excess sludge during wastewater treatment in the WWTP located in Sitkówka – News with a capacity of 72,000 3/d with a load of 275,000 PE . Due to the fact that did not have the quality parameters of wastewater at the inlet to the activated sludge chambers it was not possible to verify the empirical relationships commonly used in engineering practice to determine the size of the daily flow of excess sludge. Due to the significant differences in the amount of excess sludge generated in the period (t = 1-7 days) the simulation of the amount of sludge into the time were performed. To assessment the compatibility of measurement results and simulations quantities of sludge the mean absolute error and relative error of prediction for the considered parameter of technology was used. The analyzes carried out revealed that the amount of generated excess sludge can be predicted on the basis of parameters describing the quantity and quality of influent waste water (slurry concentration of total nitrogen and total phosphorus, BOD5) and the operating parameters of the biological reactor (recirculation rate, concentration and temperature of the sludge, the dosed amount of methanol and PIX). On the basis of computations, it can be concluded that the most accurate forecasting results amounts of sediment were obtained by using a reinforced trees (t = 2 to 5 days) and Support Vector Machines methods (t = 1, 6, 7 days). While the highest values of forecast errors sediments was obtained using a k – nearest neighbor (t = 2 to 5 days) and reinforced trees (t = 1, 6, 7 days).
Źródło:
Rocznik Ochrona Środowiska; 2016, Tom 18, cz. 2; 695-708
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bubble algorithm for the reduction of reference
Autorzy:
Sierszeń, A.
Powiązania:
https://bibliotekanauki.pl/articles/333099.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
rozpoznawanie wzorców
reguła najbliższego sąsiada
kondensacja zbioru odniesienia
redukcja zbioru odniesienia
algorytmy bańki
pattern recognition
nearest neighbour rule
reference set condensation
reference set reduction
bubble algorithms
Źródło:
Journal of Medical Informatics & Technologies; 2010, 16; 117-123
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 19

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