Temat: neural network model - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Black box dynamic modelling of proton exchange membrane fuel cells with artificial neural networks
Autorzy:: Kapica, J.
Powiązania:: https://bibliotekanauki.pl/articles/411175.pdf
Data publikacji:: 2016
Wydawca:: Polska Akademia Nauk. Oddział w Lublinie PAN
Tematy:: PEM fuel cells
neural network model
dynamic behaviour
black box
Opis:: The fuel cells are energy sources which can play an important role in transition of the energy sector into broader use of renewable energy. Numerical modelling provides an easy way to investigate properties of the objects modelled. There are various ways to model dynamic behaviour of the PEM fuel cells including methods using artificial neural networks. There are no clear rules of how a neural network should be configured: how many neurons in the hidden layer and which training algorithm should be used. In a time series modelling task additional parameters including sampling frequency, learning data set duration and number of past data points used for training need to be determined. The paper presents results of research on the influence of various model parameters on the PEM fuel cell modelling accuracy.
Źródło:: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2016, 5, 4; 85-89
2084-5715
Pojawia się w:: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Acoustical Assessment of Automotive Mufflers Using FEM, Neural Networks, and a Genetic Algorithm
Autorzy:: Chang, Y.-C.
Chiu, M.-C.
Wu, M.-R.
Powiązania:: https://bibliotekanauki.pl/articles/177901.pdf
Data publikacji:: 2018
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: acoustics
finite element method
genetic algorithm
muffler optimization
polynomial neural network model
Opis:: In order to enhance the acoustical performance of a traditional straight-path automobile muffler, a multi-chamber muffler having reverse paths is presented. Here, the muffler is composed of two internally parallel/extended tubes and one internally extended outlet. In addition, to prevent noise transmission from the muffler’s casing, the muffler’s shell is also lined with sound absorbing material. Because the geometry of an automotive muffler is complicated, using an analytic method to predict a muffler’s acoustical performance is difficult; therefore, COMSOL, a finite element analysis software, is adopted to estimate the automotive muffler’s sound transmission loss. However, optimizing the shape of a complicated muffler using an optimizer linked to the Finite Element Method (FEM) is time-consuming. Therefore, in order to facilitate the muffler’s optimization, a simplified mathematical model used as an objective function (or fitness function) during the optimization process is presented. Here, the objective function can be established by using Artificial Neural Networks (ANNs) in conjunction with the muffler’s design parameters and related TLs (simulated by FEM). With this, the muffler’s optimization can proceed by linking the objective function to an optimizer, a Genetic Algorithm (GA). Consequently, the discharged muffler which is optimally shaped will improve the automotive exhaust noise.
Źródło:: Archives of Acoustics; 2018, 43, 3; 517-529
0137-5075
Pojawia się w:: Archives of Acoustics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Study on maritime logistics warehousing center model and precision marketing strategy optimization based on fuzzy method and neural network model
Autorzy:: Xiao, K.
Hu, X.
Powiązania:: https://bibliotekanauki.pl/articles/260268.pdf
Data publikacji:: 2017
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:: maritime logistics warehousing center mode
precision marketing strategy optimization
fuzzy method
neural network model
polarity reversal
Opis:: The bulk commodity, different with the retail goods, has a uniqueness in the location selection, the chosen of transportation program and the decision objectives. How to make optimal decisions in the facility location, requirement distribution, shipping methods and the route selection and establish an effective distribution system to reduce the cost has become a burning issue for the e-commerce logistics, which is worthy to be deeply and systematically solved. In this paper, Logistics warehousing center model and precision marketing strategy optimization based on fuzzy method and neural network model is proposed to solve this problem. In addition, we have designed principles of the fuzzy method and neural network model to solve the proposed model because of its complexity. Finally, we have solved numerous examples to compare the results of lingo and Matlab, we use Matlab and lingo just to check the result and to illustrate the numerical example, we can find from the result, the multi-objective model increases logistics costs and improves the efficiency of distribution time.
Źródło:: Polish Maritime Research; 2017, S 2; 30-38
1233-2585
Pojawia się w:: Polish Maritime Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Noise Elimination of Reciprocating Compressors Using FEM, Neural Networks Method, and the GA Method
Autorzy:: Chang, Y.-C.
Chiu, M.-C.
Xie, J.-L.
Powiązania:: https://bibliotekanauki.pl/articles/178126.pdf
Data publikacji:: 2017
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: finite element method
polynomial neural network model
genetic algorithm
group method of data handling
reciprocating compressor
optimization
Opis:: Industry often utilizes acoustical hoods to block noise emitted from reciprocating compressors. However, the hoods are large and bulky. Therefore, to diminish the size of the compressor, a compact discharge muffler linked to the compressor outlet is considered. Because the geometry of a reciprocating compressor is irregular, COMSOL, a finite element analysis software, is adopted. In order to explore the acoustical performance, a mathematical model is established using a finite element method via the COMSOL commercialized package. Additionally, to facilitate the shape optimization of the muffler, a polynomial neural network model is adopted to serve as an objective function; also, a Genetic Algorithm (GA) is linked to the OBJ function. During the optimization, various noise abatement strategies such as a reverse expansion chamber at the outlet of the discharge muffler and an inner extended tube inside the discharge muffler, will be assessed by using the artificial neural network in conjunction with the GA optimizer. Consequently, the discharge muffler that is optimally shaped will decrease the noise of the reciprocating compressor.
Źródło:: Archives of Acoustics; 2017, 42, 2; 189-197
0137-5075
Pojawia się w:: Archives of Acoustics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Shape Optimisation of Multi-Chamber Acoustical Plenums Using BEM, Neural Networks, and GA Method
Autorzy:: Chang, Y.-C.
Cheng, H.-C.
Chiu, M.-C.
Chien, Y.-H.
Powiązania:: https://bibliotekanauki.pl/articles/177780.pdf
Data publikacji:: 2016
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: boundary element method
plenum
centre-opening baffle
polynomial neural network model
group method of data handling
optimisation
genetic algorithm
Opis:: Research on plenums partitioned with multiple baffles in the industrial field has been exhaustive. Most researchers have explored noise reduction effects based on the transfer matrix method and the boundary element method. However, maximum noise reduction of a plenum within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of multi-chamber plenums becomes essential. In this paper, two kinds of multi-chamber plenums (Case I: a two-chamber plenum that is partitioned with a centre-opening baffle; Case II: a three-chamber plenum that is partitioned with two centre-opening baffles) within a fixed space are assessed. In order to speed up the assessment of optimal plenums hybridized with multiple partitioned baffles, a simplified objective function (OBJ) is established by linking the boundary element model (BEM, developed using SYSNOISE) with a polynomial neural network fit with a series of real data – input design data (baffle dimensions) and output data approximated by BEM data in advance. To assess optimal plenums, a genetic algorithm (GA) is applied. The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.
Źródło:: Archives of Acoustics; 2016, 41, 1; 43-53
0137-5075
Pojawia się w:: Archives of Acoustics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Effect of ship neural domain shape on safe and optimal trajectory
Autorzy:: Lisowski, J.
Powiązania:: https://bibliotekanauki.pl/articles/24201475.pdf
Data publikacji:: 2023
Wydawca:: Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:: artificial neural network model
method for optimization
dynamic programming method
ship safety domain
safe ship control
path planning
multi-object decision model
computer simulation
Opis:: This article presents the task of safely guiding a ship, taking into account the movement of many other marine units. An optimally neural modified algorithm for determining a safe trajectory is presented. The possible shapes of the domains assigned to other ships as traffic restrictions for the particular ship were subjected to a detailed analysis. The codes for the computer program Neuro-Constraints for generating these domains are presented. The results of the simulation tests of the algorithm for a navigational situation are presented. The safe trajectories of the ship were compared at different distances, changing the sailing conditions and ship sizes.
Źródło:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2023, 17, 1; 185--191
2083-6473
2083-6481
Pojawia się w:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: A Small Wind Turbine Output Model for Spatially Constrained Remote Island Micro-Grids
Autorzy:: Žigman, D.
Meštrović, K.
Tomiša, T.
Powiązania:: https://bibliotekanauki.pl/articles/2172468.pdf
Data publikacji:: 2022
Wydawca:: Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:: wind turbine
small wind turbine
decision tree model
artificial neural network model
random forest model
micro-grids
spatially constrained remote Island micro-grids
remote Island micro-grid
Opis:: Modelling operation of the power supply system for remote island communities is essential for its operation, as well as a survival of a modern society settled in challenging conditions. Micro-grid emerges as a proper solution for a sustainable development of a spatially constrained remote island community, while at the same time reflecting the power requirements of similar maritime subjects, such as large vessels and fleets. Here we present research results in predictive modelling the output of a small wind turbine, as a component of a remote island micro-grid. Based on a month-long experimental data and the machine learning-based predictive model development approach, three candidate models of a small wind turbine output were developed, and assessed on their performance based on an independent set of experimental data. The Random Forest Model out performed competitors (Decision Tree Model and Artificial Neural Network Model), emerging as a candidate methodology for the all-year predictive model development, as a later component of the over-all remote island micro-grid model.
Źródło:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2022, 16, 1; 143--146
2083-6473
2083-6481
Pojawia się w:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Kinetics of the continuous reaction crystallization of barium sulphate in BaCl2 - (NH4)2SO4 - NaCl - H2O system - neural network model
Autorzy:: Piotrowski, K.
Koralewska, J.
Wierzbowska, B.
Matynia, A.
Powiązania:: https://bibliotekanauki.pl/articles/778848.pdf
Data publikacji:: 2009
Wydawca:: Zachodniopomorski Uniwersytet Technologiczny w Szczecinie. Wydawnictwo Uczelniane ZUT w Szczecinie
Tematy:: siarczan baru
jony sodu
sole pohartownicze
hartowanie stali
chlorek baru
kinetyka krystalizacji
barium sulphate
sodium ions
used quenching salts
steel hardening
barium chloride
reaction crystallization kinetics
population density distribution
chemical neutralization
solid waste utilization
neural network model
Opis:: One of the main toxic components of post quenching salts formed in large quantities during steel hardening processes is BaCl2. This dangerous ingredient can be chemically neutralized after dissolution in water by means of reaction crystallization with solid ammonium sulphate (NH4)2SO4. The resulting size distribution of the ecologically harmless crystalline product - BaSO4 - is an important criteria deciding about its further applicability. Presence of a second component of binary quenching salt mixture (BaCl2-NaCl) in water solution, NaCl, influences the reaction-crystallization process kinetics affecting the resulting product properties. The experimental 39 input-output data vectors containing the information about the continuous reaction crystallization in BaCl2 - (NH4)2SO4 - NaCl - H2O system ([BaCl2]RM = 10-24 mass %, [NaCl]RM = 0-12 mass %, T = 305-348 K and τ = 900-9000 s) created the database for the neural network training and validation. The applicability of diversified network configurations, neuron types and training strategies were verified. An optimal network structure was used for the process modeling.
Źródło:: Polish Journal of Chemical Technology; 2009, 11, 4; 13-19
1509-8117
1899-4741
Pojawia się w:: Polish Journal of Chemical Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Membrain neural network for visual pattern recognition
Autorzy:: Popko, A.
Jakubowski, M.
Wawer, R.
Powiązania:: https://bibliotekanauki.pl/articles/103198.pdf
Data publikacji:: 2013
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:: neural network
pattern recognition
neuron model
Opis:: Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.
Źródło:: Advances in Science and Technology. Research Journal; 2013, 7, 18; 54-59
2299-8624
Pojawia się w:: Advances in Science and Technology. Research Journal
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Neural networks as performance improvement models in intelligent CAPP systems
Autorzy:: Rojek, I.
Powiązania:: https://bibliotekanauki.pl/articles/971020.pdf
Data publikacji:: 2010
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:: classification model
neural network
tool
manufacturing operation
Opis:: The paper presents neural networks as performance improvement models in intelligent computer aided process planning systems (CAPP systems). For construction of these models three types of neural networks were used: linear network, multi-layer network with error backpropagation, and the Radial Basis Function network (RBF). The models were compared. Due to the comparison, we can say which type of neural network is the best for selection of tools for manufacturing operations. Tool selection for manufacturing operation is a classification problem. Hence, neural networks were built as classification models, meant to improve tool selection for manufacturing. The study was done for selected manufacturing operations: turning, milling and grinding. Models for the milling operation were presented in detail.
Źródło:: Control and Cybernetics; 2010, 39, 1; 54-68
0324-8569
Pojawia się w:: Control and Cybernetics
Dostawca treści:: Biblioteka Nauki

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