Biological fermentation process is of high variability and nonlinear, therefore, it is difficult to establish the precise mathematical model of the system. Using particle swarm optimization algorithm and fuzzy neural network with their respective advantages, we put forward a particle swarm algorithm of fuzzy neural network, and applied biology fermentation control system. By using fuzzy neural network to build biological fermentation control system, the particle swarm optimization algorithm for fuzzy neural network parameters was optimized. Finally, the simulation experiment was carried out to test the control system performances. The simulation results show that, the fuzzy neural network based on particle swarm biology fermentation control system has higher control precision, stronger robustness, and has good application prospect.%研究发酵控制优化问题,发酵控制系统是一个复杂的大时延系统,发酵过程具有动态性、时变性,传统难以获得较高的控制精度.为了提高生物发酵控制精度,提出一种人工智能的生物发酵控制算法.采用神经网络的智能性捕捉发酵过程的时变性、动态性变化特点,并采用智能算法中的粒子群算法对控制模型参数进行快速优化,最后采用仿真对控制系统进行测试.仿真结果表明,提出算法提高了生物发酵控制系统的控制精度高,系统鲁棒性强,为生物发酵控制优化设计提供了参考.
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