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Tytuł:
A Deep Q-Learning Network for ship stowage planning problem
Autorzy:
Shen, Y.
Zhao, N.
Xia, M.
Du, X.
Powiązania:
https://bibliotekanauki.pl/articles/260614.pdf
Data publikacji:
2017
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
Deep Q-Leaning Network (DQN)
container terminal
ship stowage plan
markov decision process
value function approximation
generalization
Opis:
Ship stowage plan is the management connection of quae crane scheduling and yard crane scheduling. The quality of ship stowage plan affects the productivity greatly. Previous studies mainly focuses on solving stowage planning problem with online searching algorithm, efficiency of which is significantly affected by case size. In this study, a Deep Q-Learning Network (DQN) is proposed to solve ship stowage planning problem. With DQN, massive calculation and training is done in pre-training stage, while in application stage stowage plan can be made in seconds. To formulate network input, decision factors are analyzed to compose feature vector of stowage plan. States subject to constraints, available action and reward function of Q-value are designed. With these information and design, an 8-layer DQN is formulated with an evaluation function of mean square error is composed to learn stowage planning. At the end of this study, several production cases are solved with proposed DQN to validate the effectiveness and generalization ability. Result shows a good availability of DQN to solve ship stowage planning problem.
Źródło:
Polish Maritime Research; 2017, S 3; 102-109
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive Rider Feedback Artificial Tree Optimization-Based Deep Neuro-Fuzzy Network for Classification of Sentiment Grade
Autorzy:
Jasti, Sireesha
Kumar, G.V.S. Raj
Powiązania:
https://bibliotekanauki.pl/articles/2200961.pdf
Data publikacji:
2023
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
deep learning network
feedback artificial tree
natural language processing (NLP)
rider optimization algorithm
sentiment grade classification
Opis:
Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.
Źródło:
Journal of Telecommunications and Information Technology; 2023, 1; 37--50
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Financing of distance learning in rural areas by the European Social Fund
Autorzy:
Kowalska, I.
Powiązania:
https://bibliotekanauki.pl/articles/572366.pdf
Data publikacji:
2007
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
financing
distance learning
learning
rural area
European Social Fund
structural fund
fund
European Union
Common Agricultural Policy
education
Internet
computer
learning network
learning scheme
Opis:
The paper is an attempt at evaluating the degree of accuracy of realization of the project: distance learning centres in rural areas financed from the European Social Fund in the frame of the priority II: Development of knowledge-based society, action 2.1.: Broadening the access to education – promotion of lifelong learning; scheme a: decreasing educational inequalities between urban and rural areas. The aims of the project in question include: 1. Creation and equipment with computers and internet connection, of at least 250 distance learning centres in rural areas (in order to enable the final beneficiaries to use the available distance learning programs) 2. Employment in the centres of qualified staff whose task would be to help using the centre’s resources. 3. Creation by the draughtsman, of a countrywide network of distance learning centres using the existing IT infrastructure with units running distance learning. 4. Enabling the final beneficiaries to complement or increase the level of education in the form of distance learning especially at the post-gymnasium level.
Źródło:
Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie. Problemy Rolnictwa Światowego; 2007, 01(16)
2081-6960
Pojawia się w:
Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie. Problemy Rolnictwa Światowego
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:
Falcon optimization algorithm for bayesian network structure learning
Autorzy:
Kareem, Shahab Wahhab
Okur, Mehmet Cudi
Powiązania:
https://bibliotekanauki.pl/articles/2097968.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
Bayesian network
global search
falcon optimization algorithm
structure learning
search and score
Opis:
In machine-learning, some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing, deleting, moving, and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization, greedy search, and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values).
Źródło:
Computer Science; 2021, 22 (4); 553--569
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Stationary supercapacitor energy storage operation algorithm based on neural network learning system
Autorzy:
Jefimowski, W.
Nikitenko, A.
Drążek, Z.
Wieczorek, M.
Powiązania:
https://bibliotekanauki.pl/articles/200935.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
stationary energy storage
operation algorithms
machine learning
supervised learning
prediction
Opis:
The paper proposes to apply an algorithm for predicting the minimum level of the state of charge (SoC) of stationary supercapacitor energy storage system operating in a DC traction substation, and for changing it over time. This is done to insure maximum energy recovery for trains while braking. The model of a supercapacitor energy storage system, its algorithms of operation and prediction of the minimum state of charge are described in detail; the main formulae, graphs and results of simulation are also provided. It is proposed to divide the SoC curve into equal periods of time during which the minimum states of charge remain constant. To predict the SoC level for the subsequent period, the learning algorithm based on the neural network could be used. Then, the minimum SoC could be based on two basic types of data: the first one is the time profile of the energy storage load during the previous period with the constant minimum SoC retained, while the second one relies on the trains’ locations and speed values in the previous period. It is proved that the use of variable minimum SoC ensures an increase of the energy volume recovered by approximately 10%. Optimum architecture and activation function of the neural network are also found.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 4; 733-738
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Exploring convolutional auto-encoders for representation learning on networks
Autorzy:
Nerurkar, Pranav Ajeet
Chandane, Madhav
Bhirud, Sunil
Powiązania:
https://bibliotekanauki.pl/articles/305489.pdf
Data publikacji:
2019
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
network representation learning
deep learning
graph convolutional neural networks
Opis:
A multitude of important real-world or synthetic systems possess network structures. Extending learning techniques such as neural networks to process such non-Euclidean data is therefore an important direction for machine learning re- search. However, this domain has received comparatively low levels of attention until very recently. There is no straight-forward application of machine learning to network data, as machine learning tools are designed for i:i:d data, simple Euclidean data, or grids. To address this challenge, the technical focus of this dissertation is on the use of graph neural networks for network representation learning (NRL); i.e., learning the vector representations of nodes in networks. Learning the vector embeddings of graph-structured data is similar to embedding complex data into low-dimensional geometries. After the embedding process is completed, the drawbacks associated with graph-structured data are overcome. The current inquiry proposes two deep-learning auto-encoder-based approaches for generating node embeddings. The drawbacks in such existing auto-encoder approaches as shallow architectures and excessive parameters are tackled in the proposed architectures by using fully convolutional layers. Extensive experiments are performed on publicly available benchmark network datasets to highlight the validity of this approach.
Źródło:
Computer Science; 2019, 20 (3); 273-288
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection
Autorzy:
Andrysiak, Tomasz
Saganowski, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/1373708.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
Tematy:
dictionary learning
sparse representation
anomaly detection
Opis:
In this article we present the use of sparse representation of a signal and incoherent dictionary learning method for the purpose of network traffic analysis. In learning process we use 1D INK-SVD algorithm to detect proper dictionary structure. Anomaly detection is realized by parameter estimation of the analyzed signal and its comparative analysis to network traffic profiles. Efficiency of our method is examined with the use of extended set of test traces from real network traffic. Received experimental results confirm effectiveness of the presented method.
Źródło:
Schedae Informaticae; 2015, 24; 63-71
0860-0295
2083-8476
Pojawia się w:
Schedae Informaticae
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Learning reduplication with a neural network that lacks explicit variables
Autorzy:
Prickett, Brandon
Traylor, Aaron
Pater, Joe
Powiązania:
https://bibliotekanauki.pl/articles/24201229.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
Tematy:
neural networks
reduplication
symbolic computation
connectionism
generalization
phonology
Opis:
Reduplicative linguistic patterns have been used as evidence for explicit algebraic variables in models of cognition.1 Here, we show that a variable-free neural network can model these patterns in a way that predicts observed human behavior. Specifically, we successfully simulate the three experiments presented by Marcus et al. (1999), as well as Endress et al.’s (2007) partial replication of one of those experiments. We then explore the model’s ability to generalize reduplicative mappings to different kinds of novel inputs. Using Berent’s (2013) scopes of generalization as a metric, we claim that the model matches the scope of generalization that has been observed in humans. We argue that these results challenge past claims about the necessity of symbolic variables in models of cognition.
Źródło:
Journal of Language Modelling; 2022, 10, 1; 1--38
2299-856X
2299-8470
Pojawia się w:
Journal of Language Modelling
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
EEULA: an energy-aware event-driven unicast algorithm for wireless sensor network by learning automata
Autorzy:
Kashani, A. A. S.
Noori, A. M. M.
Powiązania:
https://bibliotekanauki.pl/articles/102180.pdf
Data publikacji:
2016
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
network lifetime
wireless sensor network
learning automata
energy efficiency algorithm
Opis:
Energy consumption is one of the major challenges in wireless sensor networks, thus necessitating an approach for its minimization and for load balancing data. The network lifetime ends with the death of one of its nodes, which, in turn, causes energy depletion in and partition of the network. Furthermore, the total energy consumption of nodes depends on their location; that is, because of the loaded data, energy discharge in the nodes close to the base station occurs faster than other nodes, the model presented here, through using learning automata, selects the path appropriate for data transferring; the selected path is rewarded or penalized taking the reaction of surrounding paths into account. We have used learning automata for energy management in finding the path; the routing protocol was simulated by NS2 simulator; the lifetime, energy consumption and balance in an event-driven network in our proposed method were compared with other algorithms.
Źródło:
Advances in Science and Technology. Research Journal; 2016, 10, 30; 9-18
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks
Autorzy:
Bilski, Jarosław
Smoląg, Jacek
Kowalczyk, Bartosz
Grzanek, Konrad
Izonin, Ivan
Powiązania:
https://bibliotekanauki.pl/articles/2201329.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
feed-forward neural network
neural network learning algorithm
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation
Opis:
This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algorithm. The new approach was tested on several examples involving the problems of classification and function approximation, and next it was compared with a classical computational method. The article presents in detail the idea of parallel neural network computations and shows the obtained acceleration for different problems.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 45--61
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Overview of Existing Computer Network Environments Virtualization for Computer Network Learning
Przegląd istniejących środowisk wirtualizacji sieci komputerowych i ich zastosowanie w nauczaniu
Autorzy:
Wolny, Wiesław
Szołtysik, Mateusz
Powiązania:
https://bibliotekanauki.pl/articles/587014.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Ekonomiczny w Katowicach
Tematy:
Sieci komputerowe
Technologia informacyjna
Wirtualizacja
Computer networks
Information Technology (IT)
Virtualization
Opis:
W artykule przedstawiono koncepcję wykorzystania osiągnięć wirtualizacji do nauczania sieci komputerowych. Pierwsza część skupia się na omówieniu technologii wirtualizacji i emulacji oraz narzędzi, które umożliwiają wykorzystywanie tej technologii. Druga część zawiera opis zmodyfikowanego na własne potrzeby projektu Live Raizo oraz pozostałych narzędzi wchodzących w jego skład.
Źródło:
Studia Ekonomiczne; 2014, 188; 250-264
2083-8611
Pojawia się w:
Studia Ekonomiczne
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Local Levenberg-Marquardt algorithm for learning feedforwad neural networks
Autorzy:
Bilski, Jarosław
Kowalczyk, Bartosz
Marchlewska, Alina
Zurada, Jacek M.
Powiązania:
https://bibliotekanauki.pl/articles/1837415.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
feed-forward neural network
neural network learning algorithm
optimization problem
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation
Opis:
This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introduced modification has been tested on the following benchmarks: the function approximation and classification problems. The obtained results have been compared to the classic LM method performance. The paper shows that the local modification of the LM method significantly improves the algorithm’s performance for bigger networks. Several possible proposals for future works are suggested.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 299-316
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast and Energy Efficient Learning Algorithm for Kohonen Neural Network Realized in Hardware
Autorzy:
Kolasa, M.
Powiązania:
https://bibliotekanauki.pl/articles/386951.pdf
Data publikacji:
2012
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
sieci neuronowe
CMOS
WBAN
zoptymalizowany proces uczenia
niskie zużycie energii
Kohonen neural network
CMOS implementation
optimized learning process
low energy consumption
Opis:
A new fast energy efficient learning algorithm suitable for hardware implemented Kohonen Self-Organizing Map (SOM) is proposed in the paper. The new technique is based on a multistage filtering of the quantization error. The algorithm detects such periods in the learning process, in which the quantization error is decreasing (the ‘activity’ phases), which can be interpreted as a progress in training, as well as the ‘stagnation’ phases, in which the error does not decrease. The neighborhood radius is reduced by 1 always just after the training process enters one of the ‘stagnation’ phases, thus shortening this phase. The comprehensive simulations on the software model (in C++) have been carried out to investigate the influence of the proposed algorithm on the learning process. The learning process has been assessed by the used of five criteria, which allow assessing the learning algorithm in two different ways i.e., by expressing the quality of the vector quantization, as well as the topographic mapping. The new algorithm is able to shorten the overall training process by more than 90% thus reducing the energy consumed by the SOM also by 90%. The proposed training algorithm is to be used in a new high performance Neuroprocessor that will find a broad application in a new generation of Wireless Body Area Networks ( WBAN) used in the monitoring of the biomedical signals like, for example, the Electrocardiogram (ECG) signals.
Źródło:
Acta Mechanica et Automatica; 2012, 6, 3; 52-57
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative study of learning methods for artificial neural network
Badania porównawcze metod uczenia sieci neuronowej
Autorzy:
Tiliouine, H.
Powiązania:
https://bibliotekanauki.pl/articles/153863.pdf
Data publikacji:
2007
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
metody uczenia
sieć neuronowa
neuronowy regulator napięcia
learning methods
artificial neural network (ANN)
neural voltage controller
Opis:
The paper presents a comparative study of various learning methods for artificial neural network. The methods are: the backpropagation BP, the recursive least squares RLS, the Zangwill's method ZGW and the method based on evolutionary algorithm EA. The study consists of evaluating the learning effectiveness of these methods and selecting the most efficient one to be used in the designing of an adaptive neural voltage controller for a synchronous generator.
W artykule przedstawiono wyniki badań porównawczych metod uczenia sieci neuronowych takich jak: metoda propagacji wstecznej błędów, rekurencyjna metoda najmniejszych kwadratów, metoda Zangwill'a, metoda algorytmów ewolucyjnych. Celem tych badań jest dobieranie najefektywniejszej metody uczenia do projektowania adaptacyjnego neuronowego regulatora napięcia generatora synchronicznego.
Źródło:
Pomiary Automatyka Kontrola; 2007, R. 53, nr 4, 4; 117-121
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 200

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