文献类型：会议论文

英文题名：Improved Cycle Reservoir with Regular Jump Networks with Simple Disjunction Algorithm

作者：Wang, Heshan[1];Huang, Jian[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

会议论文集：Chinese Automation Congress (CAC)

会议日期：NOV 27-29, 2015

会议地点：Wuhan, PEOPLES R CHINA

语种：英文

外文关键词：echo state network; simple disjunction algorithm; time-series prediction; cycle reservoir with regular jump networks

摘要：Echo state networks (ESNs) have become one of the most effective recurrent neural networks because of its good prediction performance in real-valued time-series modeling tasks and simple training processes. The original ESN concept uses fixed randomly created reservoirs, and this concept is considered of its main advantages. However, ESN has been criticized for its randomly created connectivity and weight structure. Finding an optimal reservoir for a given task is an important problem. To address this problem, a cycle reservoir with regular jump network (CRJN) model was proposed. To improve the performance of CRJN and keep the output weights small, we present a simple disjunction algorithm (SDA). First, an appropriately sized reservoir is employed. The weight rates of each internal neuron are then calculated. Finally, internal neurons with weight rates exceeding a threshold are spread into two neurons. A system identification and two time-series benchmark tasks are applied to demonstrate the feasibility and superiority of SDA. Results show that the SDA method outperforms several other improved approaches. Furthermore, this method is able to keep the output weights small, thus increasing the stability of CRJN.