提出并建立了面向自繁衍的人工鱼的认知模型，提高人工鱼的认知能力，实现基于“动物逻辑”的人工鱼高级行为控制。
Cognitive models based on self-reproduction are presented and built to improve the cognition of artificial fish, and high-level motion controls based on animal logic are implemented.
研究了基于队列稳定性模型的认知多天线系统功率控制优化问题，并对系统性能进行了分析。
For cognitive MIMO system based on the queuing stability model, the power control optimization problem and the system performance of spectrum access is analyzed.
通过对经典内隐学习任务(如系列反应时任务、加工控制任务及其他高级认知加工任务)的模拟实验，在与人类实验数据的拟合上，CLARION展示出比以往认知模型更好的性能。
By simulating of those classic experiments with human subject in implicit learning, such as SRT, DC, and other high-order cognitive task, CLARION demonstrated its better fit to human data.
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