A Novel Stable Locally Recurrent Neural Network with Pole Assignment Projection Approach
一种基于极点配置稳定的新型局部递归神经网络

This paper derives a new stable locally recurrent global forward (LRGF) neural network with pole assignment projection approach. The pole in the hidden neurons of the LRGF neural network can be classified into two situations. One case is that the pole is on the real axis, and the other case is that the pole is a conjugate complex. We divide the dynamic hidden neuron into two parts according to the kind of the pole, so that it can avoid the complexity of the projective computation. A weight function is used to fuse the two parts. The learning method is based on the gradient decent approach, which has been modified to be fit for the proposed neural network. At last, the simulation is given to demonstrate the reliability and effectiveness of the new neural network, and the complexity of the projection computation is also be analyzed.