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Tytuł:
Wpływ opisu danych na efektywność uczenia oraz pracy sztucznej sieci neuronowej na przykładzie identyfikacji białek
Influence of data description on efficiency of learning and job artificial neural network on example of identification of proteins
Autorzy:
BARTMAN, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/457310.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Rzeszowski
Tematy:
sztuczna sieć neuronowa
uczenie
artificial neural network
learning
Opis:
Uczenie jednokierunkowych wielowarstwowych sztucznych sieci neuronowych jest zagadnieniem szeroko omawianym w literaturze. Autorzy większości opracowań skupiają się na metodach uczenia, zdecydowanie mniej prac poświęconych jest wpływowi preprocesingu danych na uczenie i efektywność pracy sieci. Skoro uczenie sztucznych sieci neuronowych jest szukaniem funkcji odwzorowującej zbiór danych wejściowych w zbiór oczekiwanych odpowiedzi, to czego możemy oczekiwać, jeżeli zmienimy opis danych uczących? Zmienia się funkcja odwzorowująca, a więc szukamy innej funkcji, zatem jest możliwe, iż sposób kodowania danych wpływa na efektywność uczenia i pracy sieci. Niniejsza praca dotyka przedstawione zagadnienie badając wpływ sposobu zakodowania opisu białek na efektywność uczenia oraz pracy sieci neuronowej identyfikującej rodzaj białka
Learning feedforward multilayer neural networks is an issue widely discussed in the literature. The authors of the most works focus on methods of learning, much less work is devoted to the influence of data preprocessing on learning and the efficiency of the network. If learning of artificial neural networks is finding the mapping function set of input data into a set of expected responses, what you can expect if you change the description of the data learners? Changes of mapping functions, and so we are looking for another function, so it is possible that the encoding of data affects the efficiency of learning and job of the network. This paper touches the issue presented by examining the impact of coding method information about the proteins on the effectiveness of learning and the work of the neural network identifies the type of protein.
Źródło:
Edukacja-Technika-Informatyka; 2013, 4, 2; 358-365
2080-9069
Pojawia się w:
Edukacja-Technika-Informatyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
PCA-based approximation of a class of distributed parameter systems: classical vs. neural network approach
Autorzy:
Bartecki, K.
Powiązania:
https://bibliotekanauki.pl/articles/201641.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
distributed parameter system
principal component analysis
artificial neural network
supervised learning
unsupervised learning
Opis:
In this article, an approximation of the spatiotemporal response of a distributed parameter system (DPS) with the use of the principal component analysis (PCA) is considered. Based on a data obtained by the numerical solution of a set of partial differential equations, a PCA-based approximation procedure is performed. It consists in the projection of the original data into the subspace spanned by the eigenvectors of the data covariance matrix, corresponding to its highest eigenvalues. The presented approach is carried out using both the classical PCA method as well as two different neural network structures: two-layer feed-forward network with supervised learning (FF-PCA) and single-layer network with unsupervised, generalized Hebbian learning rule (GHA-PCA). In each case considered, the effect of the approximation model structure represented by the number of eigenvectors (or, in the neural case, units in the network projection layer) on the mean square approximation error of the spatiotemporal response and on the data compression ratio is analysed. As shown in the paper, the best approximation quality is obtained for the classical PCA method as well as for the FF-PCA neural approach. On the other hand, an adaptive learning method for the GHA-PCA network allows to use it in e.g. an on-line identification scheme.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 651-660
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning in SMED
Autorzy:
Kutschenreiter-Praszkiewicz, I.
Powiązania:
https://bibliotekanauki.pl/articles/99646.pdf
Data publikacji:
2018
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
time standard
human activity
SMED
artificial neural network
Opis:
The paper discusses Single Minute Exchange of Die (SMED) and machine learning methods, such as neural networks and a decision tree. SMED is one of lean production methods for reducing waste in the manufacturing process, which helps to reorganize a conversion of the manufacturing process from current to the next product. SMED needs set-up activity analyses, which include activity classification, working time measurement and work improvement. The analyses presented in the article are focused on selecting the time measurement method useful from the SMED perspective. Time measurement methods and their comparison are presented in the paper. Machine learning methods are used to suggest the method of time measurement which should be applied in a particular case of workstation reorganization. A training set is developed and an example of classification is presented. Time and motion study is one of important methods of estimating machine changeover time. In the field of time study, researchers present the obtained results by using (linear) multi-linear regression models (MLR), and (non-linear) multi-layer perceptrons (MLP). The presented approach is particularly important for the enterprises which offer make-to-order products. Development of the SMED method can influence manufacturing cost reduction of customized products. In variety oriented manufacturing, SMED supports flexibility and adaptability of the manufacturing system.
Źródło:
Journal of Machine Engineering; 2018, 18, 2; 31-40
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of iterative learning control for ripple torque compensation in PMSM drive
Autorzy:
Wójcik, Adrian
Pajchrowski, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/140797.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
ripple torque
iterative learning control
artificial neural network
permanent magnet synchronous motor
Opis:
The aim of the studywas to find an effective method of ripple torque compensation for a direct drive with a permanent magnet synchronous motor (PMSM) without time- consuming drive identification. The main objective of the research on the development of a methodology for the proper teaching a neural network was achieved by the use of iterative learning control (ILC), correct estimation of torque and spline interpolation. The paper presents the structure of the drive system and the method of its tuning in order to reduce the torque ripple, which has a significant effect on the uneven speed of the servo drive. The proposed structure of the PMSM in the dq axis is equipped with a neural compensator. The introduced iterative learning control was based on the estimation of the ripple torque and spline interpolation. The structurewas analyzed and verified by simulation and experimental tests. The elaborated structure of the drive system and method of its tuning can be easily used by applying a microprocessor system available now on the market. The proposed control solution can be made without time-consuming drive identification, which can have a great practical advantage. The article presents a new approach to proper neural network training in cooperation with iterative learning for repetitive motion systems without time-consuming identification of the motor.
Źródło:
Archives of Electrical Engineering; 2019, 68, 2; 309-324
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Designing Smart Antennas Using Machine Learning Algorithms
Autorzy:
Samantaray, Barsa
Das, Kunal Kumar
Roy, Jibendu Sekhar
Powiązania:
https://bibliotekanauki.pl/articles/27312957.pdf
Data publikacji:
2023
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
artificial neural network
decision tree
ensemble algorithm
machine learning
smart antenna
support vector machine
Opis:
Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.
Źródło:
Journal of Telecommunications and Information Technology; 2023, 4; 46--52
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid, finite element-artificial neural network model for composite materials
Zastosowanie sztucznych sieci neuronowych w modelowaniu numerycznym kompozytów przy pomocy metody elementów skończonych
Autorzy:
Lefik, M.
Powiązania:
https://bibliotekanauki.pl/articles/281993.pdf
Data publikacji:
2004
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
artificial neural network
composite materials
self-learning FE method
Opis:
An application of Artificial Neural Networks for a definition of the effective constitutive law for a composite is described in the paper. First, a classical homogenisation procedure is directly interpreted with a use of this numerical tool. Next, a self-learning Finite Element code (FE with ANN inside) is used in the case when the effective constitutive law is deduced from a numerical experiment (substituting here a purely phenomenological approach). The new contribution to the classical self-learning procedure consists of its adaptation to a case of a non-monotonic loading (non-to-one load-deformation curve). This new ability of the method is principally due to the incremental form of the constitutive equation and the respective scheme of the neural network structure. Also an organisation of a constitutive data-base containing learning patterns is suitably modified. It is shown by examples that the training process is very quick. The error of this method is smaller, comparing to other schemes of data acquisition.
W artykule opisano zastosowanie sztucznych sieci neuronowych do określenia efektywnego związku konstytutywnego dla kompozytów. To narzędzie numeryczne użyte zostało dwojako: do bezpośredniego zapisu wyników otrzymanych w ramach klasycznej metody homogenizacji oraz do wnioskowania o własnościach efektywnych na podstawie eksperymentu numerycznego (zastępującego eksperyment rzeczywisty) wykonanego na małej, lecz reprezentatywnej próbce kompozytu. W tym drugim przypadku zastosowano schemat "samouczącego się" programu metody elementów skończonych, w którym związek konstytutywny opisany jest siecią neuronową. Schemat ten zaadaptowano tak, że może być użyty w przypadku obciążeń niemonotonicznych oraz wtedy, gdy zależność: miara odkształcenia-miara naprężenia nie jest wzajemnie jednoznaczna. Te nowe możliwości uzyskane zostały dzięki przedstawieniu związku konstytutywnego w formie przyrostowej oraz opracowania odpowiedniej do tego budowy sieci neuronowej. Schemat "samouczącego się" programu MES charakteryzuje się tym, że proces formułowania nieznanego związku konstytutywnego jest szybki, a zgodność modelu numerycznego z eksperymentem większa niż dla innych metod.
Źródło:
Journal of Theoretical and Applied Mechanics; 2004, 42, 3; 539-563
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning methods applied to sea level predictions in the upper part of a tidal estuary
Autorzy:
Guillou, N.
Chapalain, G.
Powiązania:
https://bibliotekanauki.pl/articles/2078822.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Instytut Oceanologii PAN
Tematy:
multiple regression model
artificial neural network
multilayer perceptron
regression function
machine learning algorithm
sea level
Opis:
Sea levels variations in the upper part of estuary are traditionally approached by relying on refined numerical simulations with high computational cost. As an alternative efficient and rapid solution, we assessed here the performances of two types of machine learning algorithms: (i) multiple regression methods based on linear and polynomial regression functions, and (ii) an artificial neural network, the multilayer perceptron. These algorithms were applied to three-year observations of sea levels maxima during high tides in the city of Landerneau, in the upper part of the Elorn estuary (western Brittany, France). Four input variables were considered in relation to tidal and coastal surge effects on sea level: the French tidal coefficient, the atmospheric pressure, the wind velocity and the river discharge. Whereas a part of these input variables derived from large-scale models with coarse spatial resolutions, the different algorithms showed good performances in this local environment, thus being able to capture sea level temporal variations at semi-diurnal and spring-neap time scales. Predictions improved furthermore the assessment of inundation events based so far on the exploitation of observations or numerical simulations in the downstream part of the estuary. Results obtained exhibited finally the weak influences of wind and river discharges on inundation events.
Źródło:
Oceanologia; 2021, 63, 4; 531-544
0078-3234
Pojawia się w:
Oceanologia
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Designing a Compact Microstrip Antenna Using the Machine Learning Approach
Autorzy:
Sharma, Kanhaiya
Pandey, Ganga Prasad
Powiązania:
https://bibliotekanauki.pl/articles/1839319.pdf
Data publikacji:
2020
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
artificial neural network
dual band
microstrip antenna
notch
Opis:
This paper presents how machine learning techniques may be applied in the process of designing a compact dual-band H-shaped rectangular microstrip antenna (RMSA) operating in 0.75–2.20 GHz and 3.0–3.44 GHz frequency ranges. In the design process, the same dimensions of upper and lower notches are incorporated, with the centered position right in the middle. Notch length and width are verified for investigating the antenna. An artificial neural network (ANN) model is developed from the simulated dataset, and is used for shape prediction. The same dataset is used to create a mathematical model as well. The predicted outcome is compared and it is determined that the model relying on ANN offers better results.
Źródło:
Journal of Telecommunications and Information Technology; 2020, 4; 44-52
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computerised Recommendations On E-Transaction Finalisation By Means Of Machine Learning
Autorzy:
Budnikas, Germanas
Powiązania:
https://bibliotekanauki.pl/articles/466046.pdf
Data publikacji:
2015
Wydawca:
Główny Urząd Statystyczny
Tematy:
online behaviour
Google Analytics
Naïve Bayes classifier
artificial neural network
Opis:
Nowadays a vast majority of businesses are supported or executed online. Website-to-user interaction is extremely important and user browsing activity on a website is becoming important to analyse. This paper is devoted to the research on user online behaviour and making computerised advices. Several problems and their solutions are discussed: to know user behaviour online pattern with respect to business objectives and estimate a possible highest impact on user online activity. The approach suggested in the paper uses the following techniques: Business Process Modelling for formalisation of user online activity; Google Analytics tracking code function for gathering statistical data about user online activities; Naïve Bayes classifier and a feedforward neural network for a classification of online patterns of user behaviour as well as for an estimation of a website component that has the highest impact on a fulfilment of business objective by a user and which will be advised to be looked at. The technique is illustrated by an example.
Źródło:
Statistics in Transition new series; 2015, 16, 2; 309-322
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Algorytm uczący sztuczną sieć neuronową zbudowany w języku VBA for Excel
Learning algorithm constructed an artificial neural network in VBA for Excel
Autorzy:
Zajkowski, K.
Duer, S.
Powiązania:
https://bibliotekanauki.pl/articles/253605.pdf
Data publikacji:
2012
Wydawca:
Instytut Naukowo-Wydawniczy TTS
Tematy:
algorytm
sztuczna sieć neuronowa
VBA for Excel
algorithm
artificial neural network
Opis:
W artykule przedstawiono realizację sztucznej sieci neuronowej przedstawionej teoretycznie w poprzednim artykule w tej publikacji. Zadaniem sieci jest wyznaczenie rozwiązania równań zawierających współczynniki niezdefiniowane liczbowo, lecz obszarowo. Położenie obszaru zależy od mierzonych wartości elektrycznych oraz od dokładności pomiarowych. Dla pewnych wartości współczynników układu równań nie istnieją funkcje odwrotne równań wejściowych. W tym przypadku niemożliwe jest wyznaczenie rozwiązania układu równań metodami klasycznymi.
This paper presents the implementation of artificial neural network theory, which is presented in previous publication. The network is determine solutions of equations containing coefficients undefined numerically, but sectorally. The location of this area depends on the measured values, and the accuracy of electrical measurements. For certain values of the coefficients of the equations are not inverse functions of input equations. In this case, it is impossible to determine a solution of the equations of classical methods.
Źródło:
TTS Technika Transportu Szynowego; 2012, 9; 531-539, CD
1232-3829
2543-5728
Pojawia się w:
TTS Technika Transportu Szynowego
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses
Autorzy:
Gocławski, J.
Sekulska-Nalewajko, J.
Kuźniak, E.
Powiązania:
https://bibliotekanauki.pl/articles/330961.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
segmentacja obrazu
przestrzeń koloru
przetwarzanie morfologiczne
progowanie obrazu
sztuczna sieć neuronowa
ochrona roślin
image segmentation
colour space
morphological processing
image thresholding
artificial neural network
WTA learning
Widrow-Hoff learning
Cucurbita species
plant stress
ROS detection
Opis:
The increased production of Reactive Oxygen Species (ROS) in plant leaf tissues is a hallmark of a plant's reaction to various environmental stresses. This paper describes an automatic segmentation method for scanned images of cucurbits leaves stained to visualise ROS accumulation sites featured by specific colour hues and intensities. The leaves placed separately in the scanner view field on a colour background are extracted by thresholding in the RGB colour space, then cleaned from petioles to obtain a leaf blade mask. The second stage of the method consists in the classification of within mask pixels in a hue-saturation plane using two classes, determined by leaf regions with and without colour products of the ROS reaction. At this stage a two-layer, hybrid artificial neural network is applied with the first layer as a self-organising Kohonen type network and a linear perceptron output layer (counter propagation network type). The WTA-based, fast competitive learning of the first layer was improved to increase clustering reliability. Widrow-Hoff supervised training used at the output layer utilises manually labelled patterns prepared from training images. The generalisation ability of the network model has been verified by K-fold cross-validation. The method significantly accelerates the measurement of leaf regions containing the ROS reaction colour products and improves measurement accuracy.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 669-684
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural model of human gait and its implementation in MATLAB and Simulink Environment using Deep Learning Toolbox
Autorzy:
Tchórzewski, Jerzy
Wielgo, Arkadiusz
Powiązania:
https://bibliotekanauki.pl/articles/2052427.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
artificial neural network
deep learning toolbox
humanoid robots
MATLAB environment
Simulink environment
modeling of human walking motion
Opis:
The article presents selected results of research on the modeling of humanoid robots, including the results of neural modeling of human gait and its implementation in the environment MATLAB and Simulink with the use of Deep Learning Toolbox. The subject of the research was placed within the scope of the available literature on the subject. Then, appropriate research experiments on human movement along a given trajectory were developed. First, the method of measuring the parameters present in the experiment was established, i.e. input quantities (displacement of the left heel, displacement of the right heel) and output quantities (displacement of the measurement point of the human body in space). Then, research experiments were carried out, as a result of which numerical data were measured in order to use them for teaching and testing the Artificial Neural Network. The Perceptron Artificial Neural Network architecture was used to build a model of a neural human walk along a given trajectory. The obtained results were discussed and interpreted, drawing a number of important conclusions.
Źródło:
Studia Informatica : systems and information technology; 2021, 1-2(25); 39-65
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Decomposition and the principle of interaction prediction in hierarchical structure of learning algorithm of ANN
Autorzy:
Płaczek, S.
Powiązania:
https://bibliotekanauki.pl/articles/376418.pdf
Data publikacji:
2015
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
artificial neural network
hierarchy
decomposition
coordination
coordination principle
Opis:
For the most popular ANN structure with one hidden layer, decomposition is done into two sub-networks. These sub-networks form the first level of the hierarchical structure. On the second level, the coordinator is working with its own target function. In the hierarchical systems theory three coordination strategies are defined. For the ANN learning algorithm the most appropriate is the coordination by the principle of interaction prediction. Implementing an off-line algorithm in all sub-networks makes the process of weight coefficient modification more stable. In the article, the quality and quantity characteristics of a coordination algorithm and the result of the learning algorithm for all sub-networks are shown. Consequently, the primary ANN achieves the global minimum during the learning process.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2015, 84; 113-120
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
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:
Synchrony state generation : an approach using stochastic synapses
Autorzy:
El-Laithy, K.
Bogdan, M.
Powiązania:
https://bibliotekanauki.pl/articles/91844.pdf
Data publikacji:
2011
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
temporal synchrony
artificial neural network
stochastic synapses
synchrony state generation
Hebbian-based learning
Opis:
In this study, the generation of temporal synchrony within an artificial neural network is examined considering a stochastic synaptic model. A network is introduced and driven by Poisson distributed trains of spikes along with white-Gaussian noise that is added to the internal synaptic activity representing the background activity (neuronal noise). A Hebbian-based learning rule for the update of synaptic parameters is introduced. Only arbitrarily selected synapses are allowed to learn, i.e. update parameter values. Results show that a network using such a framework is able to achieve different states of synchrony via learning. Thus, the plausibility of using stochastic-based models in modeling the neural process is supported. It is also consistent with arguments claiming that synchrony is a part of the memory-recall process and copes with the accepted framework in biological neural systems.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 17-25
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 33

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