Xue Fangzheng, Li Qian, Li Xiumin
Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China.
College of Automation, Chongqing University, Chongqing 400044, China.
PLoS One. 2017 Jul 31;12(7):e0181816. doi: 10.1371/journal.pone.0181816. eCollection 2017.
Recently, echo state network (ESN) has attracted a great deal of attention due to its high accuracy and efficient learning performance. Compared with the traditional random structure and classical sigmoid units, simple circle topology and leaky integrator neurons have more advantages on reservoir computing of ESN. In this paper, we propose a new model of ESN with both circle reservoir structure and leaky integrator units. By comparing the prediction capability on Mackey-Glass chaotic time series of four ESN models: classical ESN, circle ESN, traditional leaky integrator ESN, circle leaky integrator ESN, we find that our circle leaky integrator ESN shows significantly better performance than other ESNs with roughly 2 orders of magnitude reduction of the predictive error. Moreover, this model has stronger ability to approximate nonlinear dynamics and resist noise than conventional ESN and ESN with only simple circle structure or leaky integrator neurons. Our results show that the combination of circle topology and leaky integrator neurons can remarkably increase dynamical diversity and meanwhile decrease the correlation of reservoir states, which contribute to the significant improvement of computational performance of Echo state network on time series prediction.
近年来，回声状态网络（ESN）因其高精度和高效的学习性能而备受关注。与传统的随机结构和经典的 sigmoid 单元相比，简单的循环拓扑结构和泄漏积分器神经元在 ESN 的储层计算方面具有更多优势。在本文中，我们提出了一种兼具循环储层结构和泄漏积分器单元的新型 ESN 模型。通过比较四种 ESN 模型（经典 ESN、循环 ESN、传统泄漏积分器 ESN、循环泄漏积分器 ESN）对 Mackey-Glass 混沌时间序列的预测能力，我们发现我们的循环泄漏积分器 ESN 表现出明显优于其他 ESN 的性能，预测误差大致降低了 2 个数量级。此外，该模型比传统 ESN 以及仅具有简单循环结构或泄漏积分器神经元的 ESN 具有更强的逼近非线性动力学和抗噪声能力。我们的结果表明，循环拓扑结构和泄漏积分器神经元的结合可以显著增加动力学多样性，同时降低储层状态的相关性，这有助于显著提高回声状态网络在时间序列预测方面的计算性能。
