|
|
|
| Registros recuperados: 11 | |
|
| |
|
|
| Lin,Yutian; Liu,Guoli; Lin,Huibin; Gao,Ling; Lin,Jianqiang. |
| Background: Candida utilis is widely used in bioindustry, and its cell mass needs to be produced in a cost effective way. Process optimization based on the experimental results is the major way to reduce the production cost. However, this process is expensive, time consuming and labor intensive. Mathematical modeling is a useful tool for process analysis and optimization. Furthermore, sufficient information can be obtained with fewer experiments by using the mathematical modeling, and some results can be predicted even without doing experiments. Results: In the present study, we performed the mathematical modeling and simulation for the cell mass production of Candida utilis based on limited batch and repeated fedbatch experiments. The model parameters... |
| Tipo: Journal article |
Palavras-chave: Candida utilis; Fermentation; Genetic algorithm; Mathematical model. |
| Ano: 2013 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582013000400002 |
| |
|
| |
|
| |
|
|
| Guo, Qiang; Luo, Chang-shou; Wei, Qing-feng. |
| Considering the complexity of vegetables price forecast, the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm by using the characteristics of genetic algorithm and neural work. Taking mushrooms as an example, the parameters of the model are analyzed through experiment. In the end, the results of genetic algorithm and BP neural network are compared. The results show that the absolute error of prediction data is in the scale of 10%; in the scope that the absolute error in the prediction data is in the scope of 20% and 15%. The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model, especially the absolute error of prediction data is within the scope of 20%.... |
| Tipo: Journal Article |
Palavras-chave: Genetic algorithm; Neural network; Vegetables price; Prediction; China; Agribusiness. |
| Ano: 2011 |
URL: http://purl.umn.edu/117430 |
| |
|
| |
|
| |
|
| |
|
| |
|
|
| ZHAO,Hang; KONG,Fansen. |
| ABSTRACT Shop Scheduling is an important factor affecting the efficiency of production, efficient scheduling method and a research and application for optimization technology play an important role for manufacturing enterprises to improve production efficiency, reduce production costs and many other aspects. Existing studies have shown that improved genetic algorithm has solved the limitations that existed in the genetic algorithm, the objective function is able to meet customers' needs for shop scheduling, and the future research should focus on the combination of genetic algorithm with other optimized algorithms. In this paper, in order to overcome the shortcomings of early convergence of genetic algorithm and resolve local minimization problem in search... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Shop scheduling; Genetic algorithm; Local minimization; Cyclic search. |
| Ano: 2016 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000200601 |
| |
|
| |
| Registros recuperados: 11 | |
|
|
|
