Synchronization for a class of complex-valued memristor-based competitive neural networks(CMCNNs) with different time scales

Abstract

In this paper, the synchronization problem of complex-valued memristive competitive neural networks(CMCNNs) with different time scales is investigated. Based on differential inclusions and inequality techniques, some novel sufficient conditions are derived to ensure synchronization of the driveresponse systems by designing a proper controller. Finally, a numerical example is provided to illustrate the usefulness and feasibility of our results.

Multiple firing patterns, energy conversion and hardware implementation within Hindmarsh-Rose-improved neuron model

“…Where the capacitor capacity is selected as 10 nF and the value of V1 is adjustable. By comparing equation (26) and equation (27), we can acquire the parameter values of the designed circuit in table 5. The variation of the external current can be realized by adjusting the value of voltage V1.…”

“…In recent years, as a new type of electronic component, the memristor has important application in the fields of neural networks [18][19][20][21][22], image encryption [22][23][24][25][26], synchronization [27][28][29][30][31] and nonlinear chaotic circuit systems [32][33][34]. The memristor is the fourth fundamental circuit component on the basis of the existing three types of basic electronic components (resistor, capacitor and inductor) [35].…”

Four-dimensional Hindmarsh–Rose neuron model with hidden firing multistability based on two memristors

“…As memristor is discovered and implemented as the fourth basic electronic element, the study and application of memristor chaotic circuits have become a hotspot at present. In recent years, the memristor has been widely applied in the neural network [2][3][4][5][6][7][8], image encryption [9][10][11][12][13], synchronization [14][15][16][17][18][19], nonlinear chaotic circuit system [20][21][22][23][24], and many other fields.…”