Interview

Abstract

Bocheng Bao Dr Bocheng Bao from the Changzhou University in China, talks to us about the work behind the paper ‘Chaotic bursting in memristive diode bridge coupled Sallen-Key low-pass filter’, page 1104. The appearance of real memristors is gradually generating far-reaching influences in the field of circuit fundamental theory, but they are not expected to be commercially available in the near future. To solve this problem, numerous emulators behaving like memristors have been presented for the development of memristor-based application circuits. Nevertheless, most of the emulators, using masses of ready-made discrete components, imitate the theoretical characteristics. Only the circuits implemented with operational amplifiers and analogue multipliers, as well as those composed of diode-bridge with RC or LC filters, have been utilised for hardware experiments of memristor-based chaotic circuits. Since a simple low-order memristive chaotic circuit using minimal discrete components can serve as a teaching paradigm in mathematical and experimental demonstrations of dynamical phenomena, it is a research topic of significant value to seek such memristive chaotic circuits and thereby reveal these phenomena. I have more than 20 years' experience of working in industry and have served as a senior engineer and general manager in several enterprises. In 2008, I returned to college and took the nascent concept of memristor and memristive chaotic circuits as the main content of my PhD thesis. I joined the School of Information Science and Engineering, Changzhou University, China, in 2011, and currently I am working on theoretical analyses, numerical simulations and hardware experiments of memristor-based application circuits and switching DC-DC converters. Due to the similar characteristics between memristor and neuron synapse, memristors can be applied to the artificial neural network (ANNs). A special bursting phenomenon has been discovered in some memristor-based chaotic circuits recently, which indicates that the communication activity in biological neurons and endocrine cells can be modeled using a memristor-based chaotic circuit. It is quite important to seek new memristor-based chaotic circuits with special dynamics, which is effective in helping us understand the dynamical properties of ANNs. By parallel coupling a memristive diode bridge with an inductor into a second-order Sallen-Key low-pass filter (LPF), we have created a novel third-order memristive chaotic oscillator with simple circuit realisation. From this, a point-cycle chaotic bursting phenomenon is numerically simulated and experimentally captured. It is highly interesting that the striking chaotic bursting phenomenon can also be discovered in a third-order simple memristive chaotic oscillator, which will provide great convenience for future research on the mechanism of chaotic bursting. The Sallen-Key LPF-based chaotic circuit, to the knowledge of the authors, has not previously been reported in any literature. The previously reported memristor-based chaotic circuits are at least third-order non-autonomous or fourth-order autonomous, leading to greater complexity of electrical connection topologies or algebraic system structures. However, the proposed novel memristive chaotic oscillator is only third-order and its circuit realisation is extremely simple. Due to its simple circuit realisation, this oscillator could be used for future construction and investigation of memristor-based ANNs. Our research group is now working on theoretical analysis, numerical simulations and hardware experiments of memristor-based application circuits including chaotic oscillating circuits and artificial neural networks. In the past few years, we have constructed some novel memristor-based application circuits and revealed several specific dynamic phenomena of coexisting self-excited/hidden multiple attractors, coexisting self-excited/hidden infinitely many attractors, and periodic/chaotic bursting therein. Memristor-based chaotic circuits, particularly memristor-based ANNs, belong to a completely new research field within circuit fundamental theory and engineering applications. We think this field will be an extremely important topic for researchers over the next ten years and many new achievements will be reported. We sincerely look forward to seeing how our lifestyles will be completely changed owing to the widespread applications of memristor-based ANNs in the near future!