Search

The suspension culture of animal cells in vitro involves complex biochemical reaction and metabolism of a large number of cells. The on-line detection of some biological parameters (such as glucose concentration, lactic acid concentration and cell density and so on) is very important and difficult to achieve. The off-line analysis also will lead to the delay of the system regulation and control. In order to estimate the key parameters of suspension culture of foot-and-mouth disease (FMD) vaccine by a more precise method, a soft-sensor method based on relevance vector machine (RVM) is adopted in this paper. Firstly, the auxiliary variables of the model are determined by using the uniform incidence degree algorithm, and the dominant variables are determined by characteristics of the FMD vaccine during the preparation process, then the soft-sensor model is established by using the RVM algorithm. Compared with SVM, the simulation results show that the RVM algorithm can solve the problem of real-time detection of biological parameters in the animal cell culture effectively. It can also reduce the complexity of the model and has a certain practical value.

A new possibility degree for comparing two projects is defined. A new method is proposed for interval multi-attribute decision-making, in which the attribute values and weights are in the form of interval numbers. The method can compare and rank interval numbers, and avoid computing the probability distribution of integral evaluation values, if all attribute values’ distributions are known. The proposed method is straightforward and can be performed on computer easily. Finally, an illustrative example, which assumes the weights and attribute values are uniform distributions, is given to demonstrate the feasibility and practicability of the proposed method.