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Roeva,Olympia; Pencheva,Tania; Tzonkov,Stoyan; Arndt,Michael; Hitzmann,Bernd; Kleist,Sofia; Miksch,Gerchard; Friehs,Karl; Flaschel,Erwin. |
The paper presents the implementation of multiple model approach to modelling of Escherichia coli BL21(DE3)pPhyt109 fed-batch cultivation processes for an extracellular production of bacterial phytase. Due to the complex metabolic pathways of microorganisms, the accurate modelling of bioprocesses is rather difficult. Multiple model approach is an alternative concept which helps in modelling and control of complex processes. The main idea is the development of a model based on simple submodels for the purposes of further high quality process control. The presented simulations of E. coli fed-batch cultivation show how the process could be divided into different functional states and how the model parameters could be obtained easily using genetic algorithms.... |
Tipo: Journal article |
Palavras-chave: Genetic algorithms; Modelling; Multiple model approach. |
Ano: 2007 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582007000400012 |
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Smith, Peter. |
Some aspects of economic systems (eg, nonlinearity, qualitative variables) are intractable when incorporated into models. The widespread practice of excluding them (or greatly limiting their role) produces deviations of unknown size and form between the resulting models and the reality they purport to represent. To explore this issue, and the extent to which a change in methodology can improve tractability, a combination of two techniques, fuzzy logic and genetic algorithms, was applied to the problem of how the sellers in a freely competitive market, if initially trading at different prices, can find their way to supply/demand equilibrium. A multi-agent model was used to simulate the evolution of autonomously- learnt rule-governed behaviour, (i), under... |
Tipo: Working or Discussion Paper |
Palavras-chave: Competition; Markets; Walrasian Crier; Equilibrium; Fuzzy logic; Genetic algorithms; Evolutionary algorithms; Industrial Organization. |
Ano: 2004 |
URL: http://purl.umn.edu/30569 |
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Kellermann, Konrad; Balmann, Alfons. |
Land markets play a crucial role in agricultural structural change. Because the dynamics of structural change and land markets, respectively, mainly depend on the interactions between individual farms, agent-based modeling (ABM) has been established as a tool for understanding and explaining structural change and land market dynamics. This is particularly so because of ABM's ability to capture heterogeneity, non-convexity and dynamics. Unfortunately, the behavioral foundation of economic actors in ABM, i.e., of the farms, is often specified as ad hoc or simply based on "expert knowledge". In this contribution, the highly-detailed ABM AgriPoliS - which uses a myopic normative behavioral foundation - is coupled with a genetic algorithm (GA) to detect market... |
Tipo: Conference Paper or Presentation |
Palavras-chave: Agent-based modeling; Genetic algorithms; Land markets; Behavioral economics; Land Economics/Use; Q12; C6. |
Ano: 2006 |
URL: http://purl.umn.edu/25446 |
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Dornelles,Eldair F.; Kraisig,Adriana R.; Silva,José A. G. da; Sawicki,Sandro; Roos-Frantz,Fabricia; Carbonera,Roberto. |
ABSTRACT Artificial intelligence may represent an efficient strategy for simulation and optimization of important processes in agriculture. The main goal of the study is to propose the use of artificial intelligence, namely artificial neural networks and genetic algorithms, respectively, in the simulation of oat grain yield and optimization of seeding density, considering the main succession systems of southern Brazil. The study was conducted in a randomized complete block design with four replicates, following a 4 x 2 factorial scheme, for seeding densities (100, 300, 600 and 900 seeds m-2) and oat cultivars (Brisasul and URS Taura), in succession systems of corn/oats and soybean/oats. A multi-layered artificial neural network and a genetic algorithm were... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Avena sativa; Artificial neural networks; Genetic algorithms; Innovation. |
Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000300183 |
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