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Tytuł:
Embryonic Architecture with Built-in Self-test and GA Evolved Configuration Data
Autorzy:
Malhotra, Gayatri
Duraiswamy, Punithavathi
Kishore, J.K.
Powiązania:
https://bibliotekanauki.pl/articles/27311869.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
embryonic
BIST
Self-test
Genetic Algorithm
Cartesian Genetic Programming
Opis:
The embryonic architecture, which draws inspiration from the biological process of ontogeny, has built-in mechanisms for self-repair. The entire genome is stored in the embryonic cells, allowing the data to be replicated in healthy cells in the event of a single cell failure in the embryonic fabric. A specially designed genetic algorithm (GA) is used to evolve the configuration information for embryonic cells. Any failed embryonic cell must be indicated via the proposed Built-in Selftest (BIST) the module of the embryonic fabric. This paper recommends an effective centralized BIST design for a novel embryonic fabric. Every embryonic cell is scanned by the proposed BIST in case the self-test mode is activated. The centralized BIST design uses less hardware than if it were integrated into each embryonic cell. To reduce the size of the data, the genome or configuration data of each embryonic cell is decoded using Cartesian Genetic Programming (CGP). The GA is tested for the 1-bit adder and 2-bit comparator circuits that are implemented in the embryonic cell. Fault detection is possible at every function of the cell due to the BIST module’s design. The CGP format can also offer gate-level fault detection. Customized GA and BIST are combined with the novel embryonic architecture. In the embryonic cell, self-repair is accomplished via data scrubbing for transient errors.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 2; 211--217
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The use of genetic expression programming to optimize the parameters of the Muskingum method comparison with numerical methods, Euphrates river a case study
Autorzy:
Al-Bedyry, Najah
Mergan, Maher
Rasheed, Maha
Al-Khafaji, Zainab
Al-Husseinawi, Fatimah Nadeem
Powiązania:
https://bibliotekanauki.pl/articles/27312169.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
trasowanie rzeczne
programowanie ekspresji genetycznej
regresja liniowa wykładnicza
metoda Runge – Kutta czwartego rzędu
river routing
genetic expression programming
exponential linear regression
forth-order Runge–Kutta method
Opis:
The Muskingham method uses two formulas to describe the translation of flow surges in a river bed. The continuity formula is the first formula, while the relationship between the reach’s storage, inflow, and outflow is the second formula (the discharge storage formula); these formulas are applied to a portion of the river between two river cross sections. Several methods can be utilized to estimate the model’s parameters. This section contrasts the conventional graphic approach with three numerical methods: Genetic algorithm, Exponential regression, and Classical fourth-order Runge-Kutta. This application’s most noticeable plus point was the need to employ a few hydrological variables, such as intake, output, and duration. The location of the Euphrates entrance to the Iraqi territory in Husaybah city was chosen with its hydrological data during the period (1993-2017) to conduct this study. The goal function is established by accuracy criterion approaches (Sum of squares error and sum of squared deviations). Depending on the simulation findings, the suggested predictive flood routing ideawas highly acceptable with the prospect of adopting the Genetic Expression Programming model as a suitable and more accurate replacement to existing methods such as the Muskingum model and other numerical models, where this method gave results (R2 = 0.9984, SSQ = 1.06, SSSD = 80.75), These results achieved a hydrograph that is largely identical to what was given by the hydrological method called Muskingham.
Źródło:
Archives of Civil Engineering; 2023, 69, 3; 507--519
1230-2945
Pojawia się w:
Archives of Civil Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Control of perishable inventory system with uncertain perishability process using neural networks and robust multicriteria optimization
Autorzy:
Chołodowicz, Ewelina
Orłowski, Przemysław
Powiązania:
https://bibliotekanauki.pl/articles/2173677.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
multiple objective programming
optimal control
genetic algorithm
perishable inventories
inventory control
programowanie wielokryterialne
optymalna kontrola
algorytm genetyczny
produkt szybko psujący się
kontrola zapasów
Opis:
The inventory systems are highly variable and uncertain due to market demand instability, increased environmental impact, and perishability processes. The reduction of waste and minimization of holding and shortage costs are the main topics studied within the inventory management area. The main difficulty is the variability of perishability and other processes that occurred in inventory systems and the solution for a trade-off between sufficient inventory level and waste of products. In this paper, the approach for resolving this trade-off is proposed. The presented approach assumes the application of a state-feedback neural network controller to generate the optimal quantity of orders considering an uncertain deterioration process and the FIFO issuing policy. The development of the control system is based on state-space close loop control along with neural networks. For modelling the perishability process Weibull distribution and FIFO policy are applied. For the optimization of the designed control system, the evolutionary NSGA-II algorithm is used. The robustness of the proposed approach is provided using the minimax decision rule. The worst-case scenario of an uncertain perishability process is considered. For assessing the proposed approach, simulation research is conducted for different variants of controller structure and model parameters. We perform extensive numerical simulations in which the assessment process of obtained solutions is conducted using hyper volume indicator and average absolute deviation between results obtained for the learning and testing set. The results indicate that the proposed approach can significantly improve the performance of the perishable inventory system and provides robustness for the uncertain changes in the perishability process.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141182
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary data driven modelling and many objective optimization of non linear noisy data in the blast furnace iron making process
Autorzy:
Mahanta, Bashista Kumar
Chakraborti, Nirupam
Powiązania:
https://bibliotekanauki.pl/articles/29520226.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
deep learning
reference vector
neural net
genetic programming
blast furnace
Opis:
Optimization of process parameters in modern blast furnace operation, where both control and accessing large data set with multiple variables and objectives is a challenging task. To handle such non-linear and noisy data set deep learning techniques have been used in recent time. In this study an evolutionary deep neural network algorithm (EvoDN2) has been applied to derive a data driven model for blast furnace. The optimal front generated from deep neural network is compared against the optimal models developed from bi-objective genetic programming algorithm (BioGP) and evolutionary neural network (EvoNN). The optimization process is applied to all the training models by using constraint based reference vector evolutionary algorithm (cRVEA).
Źródło:
Computer Methods in Materials Science; 2021, 21, 3; 163-175
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Some efficient algorithms to deal with redundancy allocation problems
Autorzy:
Es-Sadqi, Mustapha
Idrissi, Abdellah
Benhassine, Ahlem
Powiązania:
https://bibliotekanauki.pl/articles/2141899.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
redundancy allocation problem
constraint programming
forward checking
optimization
genetic algorithm
top_k
Opis:
In this paper, we will discuss some algorithms in order to better optimize the problems of redundancy allocation in multi-state systems. The goal is to find the optimal configuration of the system that maximizes the availability and minimizes the investment cost. The availability will be evaluated using the universal generating function. In first step, our contribution consists in improving the genetic algorithm. In a second step, in the framework of the Constraint Programming, we propose a new method of optimization based on the Forward Checking as solver. Finally, we used the top-k method in our choice that helps us to get the best k elements from all possible values with highest availability. In comparison with the chosen study, our methods yield better results that satisfy the constraints of the problem in a shorter time.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 48-57
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computational intelligence for predicting biological effects of drug absorption in lungs
Autorzy:
Pacławski, Adam
Szlęk, Jakub
Mendyk, Aleksander
Powiązania:
https://bibliotekanauki.pl/articles/305803.pdf
Data publikacji:
2019
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
empirical model
absorption enhancers
pulmonary drugs
genetic programming
symbolic regression
computational intelligence
Opis:
Recently, the lungs have been extensively examined as a route for delivering drugs (active pharmaceutical ingredients, APIs) into the bloodstream; this is mainly due to the possibility of the noninvasive administration of macromolecules such as proteins and peptides. The absorption mechanisms of chemical compounds in the lungs are still not fully understood, which makes pulmonary formulation composition development challenging. This manuscript presents the development of an empirical model capable of predicting the excipients’ influence on the absorption of drugs in the lungs. Due to the complexity of the problem and the not-fully-understood mechanisms of absorption, computational intelligence tools were applied. As a result, a mathematical formula was established and analyzed. The normalized root-mean-squared error (NRMSE) and R2 of the model were 4.57%, and 0.83, respectively. The presented approach is beneficial both practically by developing an in silico predictive model and theoretically by gaining knowledge of the influence of APIs and excipient structure on absorption in the lungs.
Źródło:
Computer Science; 2019, 20 (1); 99-121
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Enhancing island model genetic programming by controlling frequent trees
Autorzy:
Ono, Keiko
Hanada, Yoshiko
Kumano, Masahito
Kimura, Masahiro
Powiązania:
https://bibliotekanauki.pl/articles/91860.pdf
Data publikacji:
2019
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
genetic programming
island model
frequent tree-based migration strategy
Opis:
In evolutionary computation approaches such as genetic programming (GP), preventing premature convergence to local minima is known to improve performance. As with other evolutionary computation methods, it can be difficult to construct an effective search bias in GP that avoids local minima. In particular, it is difficult to determine which features are the most suitable for the search bias, because GP solutions are expressed in terms of trees and have multiple features. A common approach intended to local minima is known as the Island Model. This model generates multiple populations to encourage a global search and enhance genetic diversity. To improve the Island Model in the framework of GP, we propose a novel technique using a migration strategy based on textit frequent trees and a local search, where the frequent trees refer to subtrees that appear multiple times among the individuals in the island. The proposed method evaluates each island by measuring its activation level in terms of the fitness value and how many types of frequent trees have been created. Several individuals are then migrated from an island with a high activation level to an island with a low activation level, and vice versa. The proposed method also combines strong partial solutions given by a local search. Using six kinds of benchmark problems widely adopted in the literature, we demonstrate that the incorporation of frequent tree information into a migration strategy and local search effectively improves performance. The proposed method is shown to significantly outperform both a typical Island Model GP and the aged layered population structure method.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 51-65
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelling of ship’s trajectory planning in collision situations by hybrid genetic algorithm
Autorzy:
Ni, S.
Liu, Z.
Cai, Y.
Wang, X.
Powiązania:
https://bibliotekanauki.pl/articles/259640.pdf
Data publikacji:
2018
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
trajectory planning
Multiple Genetic Algorithm
ship collision avoidance
nonlinear programming
COLREGs
Opis:
Ship collision-avoidance trajectory planning aims at searching for a theoretical safe-critical trajectory in accordance with COLREGs and good seamanship. In this paper, a novel optimal trajectory planning based on hybrid genetic algorithm is presented for ship collision avoidance in the open sea. The proposed formulation is established based on the theory of the Multiple Genetic Algorithm (MPGA) and Nonlinear Programming, which not only overcomes the inherent deficiency of the Genetic Algorithm (GA) for premature convergence, but also guarantees the practicality and consistency of the optimal trajectory. Meanwhile, the encounter type as well as the obligation of collision avoidance is determined according to COLREGs, which is then considered as the restricted condition for the operation of population initialization. Finally, this trajectory planning model is evaluated with a set of test cases simulating various traffic scenarios to demonstrate the feasibility and superiority of the optimal trajectory.
Źródło:
Polish Maritime Research; 2018, 3; 14-25
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Równoległa realizacja przykładowego algorytmu genetycznego z wykorzystaniem akceleratorów GPU
Autorzy:
Ratuszniak, P.
Stasiak, A.
Łańcucki, R.
Powiązania:
https://bibliotekanauki.pl/articles/118416.pdf
Data publikacji:
2018
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
algorytm genetyczny
programowanie równoległe
akceleracja obliczeń
akceleratory GPU
CUDA
problem komiwojażera
genetic algorithm
parallel programming
computing acceleration
GPU
travelling salesman problem
Opis:
W artykule zaprezentowano praktyczną implementację aplikacji rozwiązującej przykładowy algorytm genetyczny z wykorzystaniem akceleratorów GPU. W tym przypadku zdecydowano się na rozwiązanie za pomocą algorytmu genetycznego typowego problemu optymalizacyjnego, jakim jest problem komiwojażera. Dodatkowo w celu wykorzystania mocy karty graficznej w tworzonej aplikacji wykorzystano technologię programowania na karcie graficznej – technologię Nvidia CUDA.
The paper presents a practical implementation of a local desktop application that solves exemplary genetic algorithm with the use of GPU accelerators. In this case decided with the use of genetic algorithm to solve typical optimization problem which is travelling salesman problem. Additionally used Nvidia CUDA programming technology in order to use power of GPU in created application.
Źródło:
Zeszyty Naukowe Wydziału Elektroniki i Informatyki Politechniki Koszalińskiej; 2018, 13; 63-78
1897-7421
Pojawia się w:
Zeszyty Naukowe Wydziału Elektroniki i Informatyki Politechniki Koszalińskiej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie metod programowania genetycznego w procesie maksymalizacji wydobycia węglowodorów przy zastosowaniu symulatora złożowego
Application of Genetic Programming Methods for the Optimization of Hydrocarbon Production by using a Reservoir Simulator
Autorzy:
Łętkowski, P.
Powiązania:
https://bibliotekanauki.pl/articles/1835296.pdf
Data publikacji:
2017
Wydawca:
Instytut Nafty i Gazu - Państwowy Instytut Badawczy
Tematy:
algorytmy genetyczne
programowanie genetyczne
optymalizacja wydobycia
symulacje złożowe
genetic algorithm
geneting programming
production optimization
filed simulations
Opis:
Artykuł poświęcono zastosowaniu metody programowania genetycznego dla celów optymalizacji wydobycia ropy naftowej na przykładzie testowego złoża węglowodorowego. Prezentowane zagadnienie optymalizacyjne jest prostym przykładem problemu optymalnej kontroli i polega na doborze wydajności wydobycia ropy naftowej w przyjętych przedziałach czasowych w taki sposób, aby w zadanym całkowitym czasie eksploatacji uzyskać maksymalne wydobycie sumaryczne przy minimalnym wydobyciu wody. Problem rozwiązano przy zastosowaniu algorytmu genetycznego, kodującego dozwolone wartości wydajności wydobycia z listy wartości dozwolonych. Z jednej strony działanie takie jest charakterystyczne dla metod programowania genetycznego, zaś z drugiej redukuje istotnie przestrzeń rozwiązań. W artykule zastosowano algorytm genetyczny Hollanda, dla którego zaimplementowano krzyżowanie wielopunktowe oraz adaptację prawdopodobieństw krzyżowania i mutacji na podstawie tzw. współczynnika zróżnicowania populacji. Działanie tak zdefiniowanego mechanizmu adaptacji jest następujące: jeżeli zróżnicowanie populacji rośnie, liniowo zwiększane jest prawdopodobieństwo krzyżowania, a zmniejszane prawdopodobieństwo mutacji; w przeciwnym wypadku (zróżnicowanie populacji maleje) działa mechanizm odwrotny, tzn. zmniejsza się prawdopodobieństwo krzyżowania, a zwiększa prawdopodobieństwo mutacji. Taka metoda z jednej strony gwarantuje różnorodność populacji, z drugiej zaś zapewnia dobrą eksploatację przestrzeni rozwiązań. Przeprowadzono szereg testów mających na celu zweryfikowanie efektywności algorytmu w zależności od liczby punktów krzyżowania (krzyżowanie 1-, 2-, 3-punktowe) oraz długości chromosomu. Wykonane testy wskazują na zadowalającą zbieżność algorytmu, niezależnie od wartości badanych parametrów. Przyjęcie funkcji w określonej postaci spowodowało premiowanie przez algorytm niższych wartości wydobycia, co wynika z nieliniowego przyrostu wydobycia wody dla wyższych wartości wydobycia ropy naftowej.
The paper addresses the problem of oil production optimization by genetic programming methods. The specific example of the problem presented in the paper belongs to the class of, so called, optimal control problems. It consists in finding the time variable rates of oil production that result in the maximum of the total oil production while keeping the total water production at a minimum available level. The problem is solved by a genetic algorithm, that assumes the production rates from the list of the allowable values. This approach typical for genetic programming methods significantly reduces the space of possible solutions. The article uses the Holland genetic algorithm for which multi-point crossing has been implemented and the adaptation of crossing and mutation probabilities based on so the called coefficient of population variability. The adaptive mechanism makes the crossing probability increase and mutation probability decrease for population variability increasing with time, while the crossing probability decrease and mutation probability increase for the variability decreasing with time. This mechanism guarantees the population variability to be at on appropriate level and at the same time, the extrapolation process for the solution space to be effective. Several tests were performed to verify the actual effectiveness of the algorithm for various number of crossing points (1, 2, 3 – crossing points) and chromosome length. Their results show a satisfactory convergence of the method to the final solution independent of the varying parameters values. Adopting a function in a specific form resulted in an algorithm for lower mining values, resulting from a nonlinear increase in water extraction for higher oil production values.
Źródło:
Nafta-Gaz; 2017, 73, 10; 760-767
0867-8871
Pojawia się w:
Nafta-Gaz
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An analysis of the performance of genetic programming for realised volatility forecasting
Autorzy:
Yin, Z.
O’Sullivan, C.
Brabazon, A.
Powiązania:
https://bibliotekanauki.pl/articles/91765.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
realised volatility
genetic programming
high frequency data
Opis:
Traditionally, the volatility of daily returns in financial markets is modeled autoregressively using a time-series of lagged information. These autoregressive models exploit stylised empirical properties of volatility such as strong persistence, mean reversion and asymmetric dependence on lagged returns. While these methods can produce good forecasts, the approach is in essence atheoretical as it provides no insight into the nature of the causal factors and how they affect volatility. Many plausible explanatory variables relating market conditions and volatility have been identified in various studies but despite the volume of research, we lack a clear theoretical framework that links these factors together. This setting of a theory-weak environment suggests a useful role for powerful model induction methodologies such as Genetic Programming (GP). This study forecasts one-day ahead realised volatility (RV) using a GP methodology that incorporates information on market conditions including trading volume, number of transactions, bid-ask spread, average trading duration (waiting time between trades) and implied volatility. The forecasting performance from the evolved GP models is found to be significantly better than those numbers of benchmark forecasting models drawn from the finance literature, namely, the heterogeneous autoregressive (HAR) model, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and a stepwise linear regression model (SR). Given the practical importance of improved forecasting performance for realised volatility this result is of significance for practitioners in financial markets.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 3; 155-172
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A synthesis of adaptive, low-power real-time embedded systems for ARM big.LITTLE technology
Autorzy:
Ciopiński, L.
Deniziak, S.
Powiązania:
https://bibliotekanauki.pl/articles/114109.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
self-adaptive system
real-time embedded system
adaptive scheduler
developmental genetic programming
ARM big.LITTLE
Opis:
In this paper, we present a method of a synthesis of adaptive schedulers for real-time embedded systems. We assume that the system is implemented using a multi-core embedded processor with low-power processing capabilities. First, the developmental genetic programming is used to generate the scheduler and the initial schedule. Then during the system execution, the scheduler modifies the schedule whenever the execution time of the recently finished task has been shorter or longer than expected. The goal of rescheduling is to minimize the power consumption while all time constraints will be satisfied. We present a real-life example as well as some experimental results showing the advantages of the method.
Źródło:
Measurement Automation Monitoring; 2015, 61, 7; 340-342
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Estimation of composite load model parameters as a constrained nonlinear problem
Autorzy:
Regulski, P.
Powiązania:
https://bibliotekanauki.pl/articles/410597.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
composite load model
nonlinear optimization
nonlinear least squares
genetic algorithms
sequential quadratic programming
Opis:
This paper presents the results of application of sequential quadratic programming to the estimation of the unknown composite load model parameters. Traditionally applied estimation methods, such as nonlinear least squares or genetic algorithms, suffer from a number of issues. Genetic algorithms exhibit premature convergence and require high computational resources and nonlinear least squares method is very sensitive to the initial guess and can diverge easily. This paper provides a comparison of all three methods based on computer-generated signals serving as field measurements. Accuracy and precision are assessed as well as computational requirements.
Źródło:
Present Problems of Power System Control; 2015, 6; 33-42
2084-2201
Pojawia się w:
Present Problems of Power System Control
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
NEUROPLASTYCZNOŚĆ MÓZGU WSPARCIEM ROZWOJOWYM DZIECKA WE WCZESNYM DZIECIŃSTWIE
BRAIN NEUROPLASTICITY AS A SUPPORT FOR THE DEVELOPMENT OF CHILDREN IN EARLY CHILDHOOD
Autorzy:
Skibska, Joanna
Powiązania:
https://bibliotekanauki.pl/articles/479939.pdf
Data publikacji:
2015
Wydawca:
Wyższa Szkoła Humanitas
Tematy:
budowa neuronu,
neuroplastyczność mózgu,
rodzaje plastyczności,
mielinizacja,
połączenia nerwowe,
aktywność funkcjonalna mózgu,
genetyczne programowanie
połączeń nerwowych,
wsparcie rozwojowe dziecka
neuron construction,
brain neuroplasticity,
types of plasticity,
myelination,
nerve connections,
functional activity of the brain,
genetic programming of neural connections,
developmental support of a child
Opis:
Tekst stanowi przegląd badań nad plastycznością mózgu, prowadzonych w ubiegłym wieku oraz współcześnie, które dla tej problematyki okazały się przełomowe i pozwoliły na tworzenie teorii, które w obecnym kształcie umożliwiły kontynuację empirycznych poszukiwań odpowiedzi na pytania: jak działa nasz mózg oraz jakie są jego możliwości. Tekst omawia mikroanatomię mózgu, charakteryzuje proces mielinizacji oraz tworzenia nowych połączeń nerwowych istotnych dla naszego rozwoju poznawczego. Określa rolę i znaczenie doświadczenia życiowego oraz uczenia się dla tworzenia nowych połączeń nerwowych, a także zmian i przeobrażeń zachodzących w naszym mózgu. Przedstawia rodzaje plastyczności oraz jej podział z uwzględnieniem okresów rozwojowych. Omawia znaczenie plastyczności mózgu w rozwoju dziecka jako szczególnej formy wsparcia, nie tylko w sytuacjach uszkodzenia mózgu.
This paper is a review of research on brain plasticity introduced in the last century and conducted until today, which turned out to be a breakthrough for the discussed issue and led to the creation of the theory, which in its present form enabled the continuation of empirical research on answers to the following questions: how our brain works and what its capabilities are. The text discusses micro-anatomy of a human brain and characterizes the process of myelination and creating new neural connections essential for our cognitive development. Furthermore, the study defines the role and significance of life experience and learning in creating new neural connections and the changes and transformations taking place in our brain. It also shows the types of plasticity and its division, taking into account periods of development and discusses the importance of brain plasticity in the development of a child as a form of support, not only in situations of brain damage.
Źródło:
Zeszyty Naukowe Wyższej Szkoły Humanitas. Pedagogika; 2015, 10; 79-92
1896-4591
Pojawia się w:
Zeszyty Naukowe Wyższej Szkoły Humanitas. Pedagogika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The On-line Evolutionary Method for Soft Fault Diagnosis in Diode-transistor Circuits
Autorzy:
Korzybski, M.
Ossowski, M.
Powiązania:
https://bibliotekanauki.pl/articles/226980.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electric circuit diagnosis
soft faults
multiple faults
evolutionary computation
gene expression programming
genetic algorithm
differential evolution
Opis:
The paper is devoted to diagnostic method enabling us to perform all the three levels of fault investigations - detection, localization and identification. It is designed for analog diode-transistor circuits, in which the circuit’s state is defined by the DC sources’ values causing elements operating points and the harmonic components with small amplitudes being calculated in accordance with small-signal circuit analysis rules. Geneexpression programming (GEP), differential evolution (DE) and genetic algorithms (GA) are a mathematical background of the proposed algorithms. Time consumed by diagnostic process rises rapidly with the increasing number of possible faulty circuit elements in case of using any of mentioned algorithms. The conncept of using two different circuit models with partly different elements allows us to decrease a number of possibly faulty elements in each circuit because some of possibly faulty elements are absent in one of two investigated circuits.
Źródło:
International Journal of Electronics and Telecommunications; 2015, 61, 1; 109-115
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 38

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