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Wyświetlanie 1-4 z 4
Tytuł:
Methodological aspects of qualitative-quantitative analysis of decision-making processes
Autorzy:
Gawlik, R.
Powiązania:
https://bibliotekanauki.pl/articles/406734.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
qualitative-quantitative analysis
hierarchical decision-making
neural-network models
management
manufacturing processes
Opis:
The paper aims at recognizing the possibilities and perspectives of application of qualitativequantitative research methodology in the field of economics, with a special focus on production engineering management processes. The main goal of the research is to define the methods that would extend the research apparatus of economists and managers by tools that allow the inclusion of qualitative determinants into quantitative analysis. Such approach is justified by qualitative character of many determinants of economic occurrences. At the same time quantitative approach seems to be predominant in production engineering management, although methods of transposition of qualitative decision criteria can be found in literature. Nevertheless, international economics and management could profit from a mixed methodology, incorporating both types of determinants into joint decision-making models. The research methodology consists of literature review and own analysis of applicability of mixed qualitative-quantitative methods for managerial decision-making. The expected outcome of the research is to find which methods should be applied to include qualitativequantitative analysis into multicriteria decision-making models in the fields of economics, with a special regard to production engineering management.
Źródło:
Management and Production Engineering Review; 2016, 7, 2; 3-11
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference
Autorzy:
Bagheri, A.
Zargarian, N.
Mondani, F.
Nosratti, I.
Powiązania:
https://bibliotekanauki.pl/articles/2082743.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural network
prediction models
pulses
weed interference
yield estimation
Opis:
This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) af- fected by weeds using artificial neural network and multiple regression models. Systematic sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil maturity. The weed density and height as well as canopy cover of the weeds and lentil were measured in the first sampling stage. In addition, weed species richness, diversity and even- ness were calculated. The measured variables in the first sampling stage were considered as predictive variables. In the second sampling stage, lentil yield and biomass dry weight were recorded at the same sampling points as the first sampling stage. The lentil yield and biomass were considered as dependent variables. The model input data included the total raw and standardized variables of the first sampling stage, as well as the raw and stan- dardized variables with a significant relationship to the lentil yield and biomass extracted from stepwise regression and correlation methods. The results showed that neural network prediction accuracy was significantly more than multiple regression. The best network in predicting yield of lentil was the principal component analysis network (PCA), made from total standardized data, with a correlation coefficient of 80% and normalized root mean square error of 5.85%. These values in the best network (a PCA neural network made from standardized data with significant relationship to lentil biomass) were 79% and 11.36% for lentil biomass prediction, respectively. Our results generally showed that the neural net- work approach could be used effectively in lentil yield prediction under weed interference conditions.
Źródło:
Journal of Plant Protection Research; 2020, 60, 3; 284-295
1427-4345
Pojawia się w:
Journal of Plant Protection Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Direct interaction between micronutrients and bell pepper (Capsicum annum L.), to affect fitness of Myzus persicae (Sulzer)
Autorzy:
Alizamani, T.
Shakarami, J.
Mardani-Talaee, M.
Zibaee, A.
Serrao, J.E.
Powiązania:
https://bibliotekanauki.pl/articles/2082745.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural network
prediction models
pulses
weed interference
yield estimation
Opis:
The green peach aphid, Myzus persicae (Sulzer), is a polyphagous and holocyclic aphid which significantly damages agricultural crops. In the current study, the effects of micro- nutrients on some secondary metabolites of bell pepper (Capsicum annum L.) leaves and their subsequent influence on the life table parameters of M. persicae were investigated under greenhouse conditions. The flavonoid content in bell pepper leaves significantly changed following micronutrient treatments in the wavelength of 270 nm while there were no significant differences in the wavelengths 300 and 330 nm. The highest anthocyanin content was recorded after Fe treatment (3.811 mg ⋅ ml–1) while the total phenolic content in the bell pepper leaves increased after Mn (541.2 mg ⋅ ml –1 ) treatment compared to Fe (254.5 mg ⋅ ml –1 ) and control (216.33 mg ⋅ ml –1 ), respectively. The highest values of intrinsic (r) and finite rates of population increase (λ) of M. persicae were gained with Zn (0.320 and 1.377 day–1 , respectively) treatment although the highest and the lowest values of the mean generation time (T) were found with Fe and Zn (14.07 and 12.63 days, respectively) treat- ments, respectively. Our findings suggest that Mn, more than Zn micronutrients, decreased ecological fitness of green peach aphid and may help enhance the efficiency of pest control techniques.
Źródło:
Journal of Plant Protection Research; 2020, 60, 3; 253-262
1427-4345
Pojawia się w:
Journal of Plant Protection Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Elman neural network for modeling and predictive control of delayed dynamic systems
Autorzy:
Wysocki, A.
Ławryńczuk, M.
Powiązania:
https://bibliotekanauki.pl/articles/229646.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
dynamic models
process control
model predictive control
neural networks
Elman neural network
delayed systems
Opis:
The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
Źródło:
Archives of Control Sciences; 2016, 26, 1; 117-142
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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