title: Noise cancellation of memristive neural networks.
abstract: This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with asymmetric voltage thresholds. Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device. Also certain templates of memristive neural networks are established to implement the noise cancellation.
authors.affiliation: Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China. Electronic address: wenshiping226@126.com.
authors.forename: Shiping
authors.initials: S
authors.lastname: Wen
authors.affiliation: Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China. Electronic address: zgzeng527@126.com.
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Zeng
authors.affiliation: Texas A & M University at Qatar, Doha 5825, Qatar. Electronic address: tingwen.huang@qatar.tamu.edu.
authors.forename: Tingwen
authors.initials: T
authors.lastname: Huang
authors.affiliation: Platform Technologies Research Institute, RMIT University, VIC 3001, Australia. Electronic address: x.yu@rmit.edu.au.
authors.forename: Xinghuo
authors.initials: X
authors.lastname: Yu
mesh_terms: D016571:Neural Networks, Computer; D059629:Signal-To-Noise Ratio
keywords: Memristor; Neural network; Stability