Adaptive fuzzy synchronization for a class of fractional-order neural networks<xref rid="cpb_26_3_030504fn1" ref-type="fn">*</xref><fn id="cpb_26_3_030504fn1"><label>*</label><p>Project supported by the National Natural Science Foundation of China (Grant Nos. 11401243 and 61403157), the Foundation for Distinguished Young Talents in Higher Education of Anhui Province, China (Grant No. GXYQZD2016257), the Fundamental Research Funds for the Central Universities of China (Grant No. GK201504002), the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China (Grant Nos. KJ2015A256 and KJ2016A665), the Natural Science Foundation of Anhui Province, China (Grant No. 1508085QA16), and the Innovation Funds of Graduate Programs of Shaanxi Normal University, China (Grant No. 2015CXB008).</p></fn>

Adaptive fuzzy synchronization for a class of fractional-order neural networks**

Project supported by the National Natural Science Foundation of China (Grant Nos. 11401243 and 61403157), the Foundation for Distinguished Young Talents in Higher Education of Anhui Province, China (Grant No. GXYQZD2016257), the Fundamental Research Funds for the Central Universities of China (Grant No. GK201504002), the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China (Grant Nos. KJ2015A256 and KJ2016A665), the Natural Science Foundation of Anhui Province, China (Grant No. 1508085QA16), and the Innovation Funds of Graduate Programs of Shaanxi Normal University, China (Grant No. 2015CXB008).

Liu Heng^{1, 2}, Li Sheng-Gang^{1, †}, Wang Hong-Xing², Li Guan-Jun²

(color online) Simulation results. (a) Synchronization errors and (b) control inputs of our method, (c) synchronization errors and (d) control inputs of Ref. [11].