An improved deep echo state network inspired by tissue-like P system forecasting for non-stationary time series

Abstract As a recurrent neural network, ESN has attracted wide attention because of its simple training process and unique reservoir structure, been applied to time series prediction other fields. However, also some shortcomings, such as the optimization collinearity. Many researchers try optimize structure performance deep by constructing ESN. with increase number network layers, problem low computing efficiency follows. In this paper, we combined membrane build an improved echo state inspired tissue-like P system. Through analysis comparison classical models, found that model proposed in paper achieved great success both predicting accuracy operation efficiency.