Exponential Adaptive Lag Synchronization of Memristive Neural Networks via Fuzzy Method and Applications in Pseudorandom Number Generators

abstract

• 2014 IEEE. This paper investigates the problem of exponential lag synchronization control of memristive neural networks (MNNs) via the fuzzy method and applications in pseudorandom number generators. Based on the knowledge of memristor and recurrent neural networks, the model of MNNs is established. Then, considering the state-dependent properties of memristor, a fuzzy model of MNNs is employed to provide a new way of analyzing the complicated MNNs with only two subsystems, and update laws for the connection weights of slave systems and controller gain are designed to make the slave systems exponentially lag synchronized with the master systems. Two examples about synchronization problems are presented to show the effectiveness of the obtained results, and an application of the obtained theory is also given in the pseudorandom number generator.

authors

• Huang, Tingwen

published proceedings

• IEEE TRANSACTIONS ON FUZZY SYSTEMS

author list (cited authors)

• Wen, S., Zeng, Z., Huang, T., & Zhang, Y.

citation count

• 244

complete list of authors

• Wen, Shiping||Zeng, Zhigang||Huang, Tingwen||Zhang, Yide

publication date

• December 2014

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE transactions on fuzzy systems  Journal

keywords

• Adaptive Lag Synchronization
• Fuzzy Model
• Memristor
• Neural Networks
• Pseudorandom Number Generator (prng)

Digital Object Identifier (DOI)

• 10.1109/TFUZZ.2013.2294855

start page

• 1704

end page

• 1713

volume

• 22

issue

• 6

URL

• http://dx.doi.org/10.1109/tfuzz.2013.2294855