提出了被控对象模型——扑翼模型，它是对可进化的实际生物模型的简化，具有非线性、多输入多输出等特点。用在复杂的学习系统时，可对其效率及性能进行检验。基于该模型，构造了一个神经网络控制方案。它是一个逐级发展的学习控制系统，可用于分析和研究生物的学习过程。在该系统上，进行了对目标捕捉的计算机模拟，当目标静止、随机运动或以一定规律运动时，均获得了满意的结果。随着学习的深入，该系统还可获得预测能力。由于本系统具有逐层建构的特点，故可完成复杂的控制任务。 Proposed a controlled object model - flapping wing model, it is the evolution of the actual biological model of the simplified, with nonlinear, multiple input and output characteristics. When used in a complex learning system, its efficiency and performance can be tested. Based on the model, a neural network control scheme is constructed. It is a progressive learning control system that can be used to analyze and study the biological learning process. In this system, a computer simulation of the target capture was performed, and satisfactory results were obtained when the target was stationary, stochastic or some regular motion. As learning progresses, the system also has predictive power. As the system has the characteristics of layer by layer construction, it can complete the complex control tasks.