The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The germ concentration is an important biology parameter, which affects the yield of glutamic acid fermentation process. It is difficult to realize the on-line and real-time detection. By analyzing the producing process of glutamic acid, the process parameters, which influence the germ concentration, have been found, and samples have been selected from field history data, then the soft-sensing model...
With massive data of a fermentation process, a single data-based soft-sensor modeling method suffers from heavy features and bad accuracy. A novel soft sensor using multi-model neural network (MNN) based on modified kernel fuzzy clustering is proposed. Firstly, features of sample data are extracted using principal component analysis (PCA) and the secondary variables are determined by PCA. Secondly,...
The proposed method is adopted by genetic algorithm and BP neural network combine together, so GA-BP network is structured and used to the study of fault model and fault diagnose. It combines the ability of genetic algorithm with global optimizing and neural network with learning and memory, and resolves to problems that are slowly study speed and local minimum solution to produce easily for the BP...
Aiming at the complexity of the glutamic acid fermentation process, a neural network dynamic model of the fermentation process was established. The improved differential evolutionary algorithm (DEA) was used to the multi-variables optimal control of the fermentation process and the optimal control trajectories of operating variables were found out. Some improvements of the primitive DEA were made...
A method of fault diagnosis based on composite model and support vector machines for fermentation process is proposed to overcome its difficulty in direct measurement of state parameters. In order to obtain the process state, composite model is presented by combining mass equations of bioreactors with RBF neural network that serve as estimators of unmeasured process kinetic parameters. Then Support...
Support vector regression (SVR) is a novel type of learning machine, which has shown to provide better generalization performance than traditional techniques. This thesis introduces a new type of support vector machine for regression (v-SVR), which based on SVR. The new algorithm can control the accuracy of fitness and prediction error by adjusting the parameter v. In the experiments v-SVR is used...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.