Papers by Author: Tian Yan Du

Abstract: Biological fermentation process is a complex nonlinear dynamic coupling process. As it is very difficult to measure the key biological parameters on line, the process control is unavailable to industrial production in time. In this respect, however, soft sensing can solve the above problem. To overcome some drawbacks of PSO and FNN, such as falling into local minimum occasionally and slow convergence speed, the extremum disturbed particle swarm optimization (tPSO) algorithm is proposed and then combined with fuzzy neural network (FNN) to optimize the network parameters. Furthermore, the tPSO-FNN is applied in the soft sensor modeling of lysine biological fermentation. Experiment results show that the model proposed could measure the key parameters. And the soft sensor model based on tPSO-FNN has higher precision and better performance than the model based on FNN.

Abstract: This paper deals with an implementation method of component-based Petri nets system based on a graphical programming language (LabVIEW). LabVIEW is not only a graphical programming language, but also a virtual instrument platform which is widely used in virtual measurement and control system. The Places (token number) of Petri nets are represented by Numeric Controls of LabVIEW. The Transitions of Petri nets are represented by subVIs of LabVIEW. Transition subVI will change the tokens of Places by the Numeric Controls' Reference when it is fired. This method will make it ease to implement a Petri net by simply combining Place and Transition components (subVI). The example implementing a special Petri net shows that the Front Panel of the controller reflects the system operating state directly; the Block Diagram is similar to the topology of original Petri net. The combination of two graphic languages makes the modeling, analysis and formal verification of measurement and control system based on Petri nets easier.