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An upgraded Myxococcus xanthus chassis with enhanced growth characteristics for efficient genetic manipulation
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作者 Weifeng Hu Yan Wang +4 位作者 Xiaoran Yue Weiwei Xue Wei Hu Xinjing Yue Yuezhong Li 《Engineering Microbiology》 2024年第3期62-66,共5页
Myxobacteria are well known for multicellular social behaviors and valued for biosynthesis of natural products.Myxobacteria social behaviors such as clumping growth severely hamper strain cultivation and genetic manip... Myxobacteria are well known for multicellular social behaviors and valued for biosynthesis of natural products.Myxobacteria social behaviors such as clumping growth severely hamper strain cultivation and genetic manip-ulation.Using Myxococcus xanthus DK1622,we engineered Hu04,which is deficient in multicellular behavior and pigmentation.Hu04,while maintaining nutritional growth and a similar metabolic background,exhibits improved dispersed growth,streamlining operational procedures.It achieves high cell densities in culture and is promising for synthetic biology applications. 展开更多
关键词 Myxococcus xanthus Operation deficiency Phenotypic optimization CHASSIS genetic performance
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GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS 被引量:2
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作者 LI Guodong ZHANG Qingchun LIANG Yingchun 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2007年第2期56-59,共4页
In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA(genetic algnrithm)-based PID neural network controller is designed and trained tO emulate the operation of a c... In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA(genetic algnrithm)-based PID neural network controller is designed and trained tO emulate the operation of a complete system (magnetic bearing, controller, and power amplifiers). The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions (chromosomes), increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems. 展开更多
关键词 Magnetic bearing Non-linearity PID neural network genetic algorithm Local minima Robust performance
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