基于GRNN的机器鱼直游稳态速度建模
内容摘要：
为解决机器鱼动力学建模中瞬变的强非线性流动控制等难点问题，建立基于广义回归神经网络（General Regression Neural Network, GRNN）的机器鱼直游稳态速度模型。以三关节仿生机器鱼为研究对象，利用神经网络的非线性逼近能力，使用GRNN辨识机器鱼游速与其运动参数之间的强非线性关系，建立了电机控制参数与仿生机器鱼直游稳态速度之间关系的模型，并通过实验进行了预测值与实际值之间的误差分析。实验结果证明，采用GRNN神经网络辨识技术建立仿鲹科机器鱼直游速度模型是完全可行的。
关键词：
仿生机器鱼；运动参数；GRNN神经网络；直游速度模型
中图分类号：TP249; TP183 文献标识码：A
Abstract: To resolve the difficult problems in robot fish dynamics modeling such as transient strongly nonlinear flow control, establish the robot fish straight swim steady-state velocity model based on general regression neural network (GRNN). Taking the three joints biomimetic robot fish as the researching object, use the nonlinear approximation capability of the neural networks, make use of GRNN to recognize the strongly nonlinear relationship between robot fish swim velocity and its motional parameters, set up the relation model of the motor controlled parameters and the biomimetic robotic fish straight swim steady-state velocity, carry out experiments to do the error analysis between the predicted and actual values. The experimental results prove that the biomimetic carangiform robot fish straight swim velocity model using the GRNN neural network identification technique is feasible.
Keywords: biomimetic robot fish; motion parameters; GRNN neural network; straight swim velocity model.
