This paper proposes a co-evolutionary recurrent neural network （CERNN） for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets： the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series.
应用原子力显微镜（atomic force microscopy,AFM）探测了静息、脂多糖（LPS）或伴刀豆蛋白（ConA）活化的人外周血淋巴细胞的形态结构、超微结构及粘滞力特性。从AFM图像可知,经LPS或ConA刺激活化后的淋巴细胞比静息状态的淋巴细胞有所增大,并且表面分布着大小不一的颗粒状聚合物。利用AFM高空间分辨的力位移曲线测量系统,发现经LPS或ConA刺激活化后淋巴细胞的粘滞力是静息状态淋巴细胞的2~3倍。通过AFM探测淋巴细胞活化状态的可视化数据,可以更好地理解淋巴细胞的行为。
