Design, analysis and verification of noise-tolerant and overshoot-free recurrent neural network

A kind of recurrent neural network (RNN) specialized in solving time-varying problems has wide applications in various fields, where the RNN with integral terms (RNN-IT) as a state-of-art method plays an important role in rejecting noise. However, the RNN-IT always experiences overshoot phenomenon when suppressing noise, which greatly affects the convergence time. In order to overcome the above disadvantage of the RNN-IT, this paper proposes a noise-tolerant and overshoot-free recurrent neural network (NORNN) by designing a time-varying additional term, which can flexibly compensate errors and avoid accumulation, thereby resisting noise and eliminating overshoot. Furthermore, the convergence time of the NORNN is obviously improved, which means that the NORNN can effectively and quickly address time-varying problems even when the noise disturbed. Two theorems and a corollary analyze the convergence, noise-tolerance, and overshoot-free properties of the proposed NORNN. Meanwhile, simulation experiments on solving the time-varying matrix inversion problem and the trajectory tracking of the RPRR manipulator also verify its excellent performance.