将蚁群神经网络应用于生理信息的融合,为提高仿生机器马虚拟速度的预报精度,根据仿生机器马运动器特点,提出了一种蚁群神经网络模型,即蚁群算法和神经网络相结合的方法,通过人体生理信息变化,预测机器马运动的虚拟速度值,实验表明,经过蚁群算法优化后的神经网络比单纯BP神经网络预测精度和收敛速度都有较大提高,而且可以有效避免单纯BP算法容易陷入局部最优的不足,可以在实际中应用。 The ant colony neural network is applied to the fusion of physiological information. In order to improve the prediction precision of the virtual speed of the bionic robot, an ant colony neural network model is proposed according to the characteristics of the bionic robot equine movement. That is, the ant colony algorithm is combined with the neural network The experiments show that the neural network optimized by the ant colony optimization algorithm can improve the prediction accuracy and convergence speed better than the BP neural network alone and can effectively avoid the change of the physiological speed of the robot. Pure BP algorithm is easy to fall into the local optimum, which can be applied in practice.