Trajectory formation of arm movement by a neural network with forward and inverse dynamics models

The minimum torque-change model predicts and reproduces human multijoint movement data quite well. However, there are three cirticisms of the current neural network models for trajectory formation based on the minimum torque-change criterion: (1) their spatial representation of time; (2) backpropagation is essential; and (3) they require too many iterations. According, a new neural network model for trajectory formation is proposed based on the minimum torque-change criterion. This neural network model basically uses a forward dynamics model, an inverse dynamics model and a trajectory formation mechanism which generates an approximate minimum torque-change trajectory