摘要：Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.

摘要：为解决智能座舱语音系统中语音信号的采集、识别准确率及车载语音交互系统精确控制的问题,设计一种基于NI-myDAQ数据采集卡、CAN数据分析仪以及LabVIEW软件系统的语音交互系统.该系统运行主要包括3个步骤:语音信号采集和处理、实时识别语音信号,以及语音指令转换为CAN数据报文并发送至智能座舱的控制器.测试结果表明:通过LabVIEW平台的图形化编程和信号处理功能,不仅可以提高系统的响应时间及开发效率,保证系统的语音识别准确性,而且能够提升用户的体验.