Shukai Duan

Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects

This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.

Memristor-Based Cellular Nonlinear/Neural Network: Design, Analysis, and Applications

Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current-voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.

Analog memristive memory with applications in audio signal processing

Since the development of the HP memristor, much attention has been paid to studies of memristive devices and applications, particularly memristor-based nonvolatile semiconductor memory. Owing to its unique properties, theoretically, one could restart a memristor-based computer immediately without the need for reloading the data. Further, current memories are mainly binary and can store only ones and zeros, whereas memristors have multilevel states, which means a single memristor unit can replace many binary transistors and realize higher-density memory. It is believed that memristors can also implement analog storage besides binary and multilevel information memory. In this paper, an implementation scheme for analog memristive memory is considered. A charge-controlled memristor model is derived and the corresponding SPICE model is constructed. Special write and read operations are demonstrated through numerical analysis and circuit simulations. In addition, an audio analog record/play system using a memristor crossbar array is designed. This system can provide great storage capacity (long recording time) and high audio quality with a simple small circuit structure. A series of computer simulations and analyses verify the effectiveness of the proposed scheme.

MEMRISTOR MODEL AND ITS APPLICATION FOR CHAOS GENERATION

This paper contributes to the understanding of memristor operation and its possible application fields through: (a) derivation of a complete mathematical model for the HP memristor which takes into consideration the inter-dependence between memristance, charge and flux along with the boundary and initial conditions of operation; (b) an introduction of detailed charge- and flux-controlled SPICE memristor models realizing the proposed mathematical memristor model; (c) The incorporation of the memristor model in the SPICE realization of a third-order chaotic system where a single HP memristor acts as the nonlinear part of the system. Simulation results are provided to validate the mathematical model and the synthesis and operation of the third-order chaotic system.

Memristive crossbar array with applications in image processing

A memristor is a kind of nonlinear resistor with memory capacity. Its resistance changes with the amount of charge or flux passing through it. As the fourth fundamental circuit element, it has huge potential applications in many fields, and has been expected to drive a revolution in circuit theory. Through numerical simulations and circuitry modeling, the basic theory and properties of memristors are analyzed, and a memristorbased crossbar array is then proposed. The array can realize storage and output for binary, grayscale and color images. A series of computer simulations demonstrates the effectiveness of the proposed scheme. Owing to the advantage of the memristive crossbar array in parallel information processing, the proposed method is expected to be used in high-speed image processing.

Global exponential stability of a class of memristive neural networks with time-varying delays

This paper studies the uniqueness and global exponential stability of the equilibrium point for memristor-based recurrent neural networks with time-varying delays. By employing Lyapunov functional and theory of differential equations with discontinuous right-hand side, we establish several sufficient conditions for exponential stability of the equilibrium point. In comparison with the existing results, the proposed stability conditions are milder and more general, and can be applied to the memristor-based neural networks model whose connection weight changes continuously. Numerical examples are also presented to show the effectiveness of the theoretical results.

Exponential stability of impulsive discrete systems with time delay and applications in stochastic neural networks: A Razumikhin approach

This paper investigates exponential stability of the equilibrium point of discrete-time delayed dynamic systems with impulsive effects. Firstly, some Razumikhin-type theorems considering stabilizing effects of impulses are introduced. These results show that even the impulse-free component of the original system is unstable; impulses may compensate the deviating trend. Then, we apply the theoretical results to a class of recurrent neural networks under stochastic perturbations and derive several stability preservation criteria; the applicable region of the impulsive strength is also estimated. Some numerical examples are provided to illustrate the efficiency of the results at the end.

A Memristor-Based Scroll Chaotic System — Design, Analysis and Circuit Implementation

A scroll chaotic system containing a HP memristor model and triangular wave sequence is proposed in this article. Because the memristor is both a nonlinear element and a memory element intrinsically, it is considered a potential candidate to reduce system power consumption and circuit size. A reasonable mathematical structure of triangular wave sequence and the selection of appropriate amplitude, balance point and turning point reduce the dynamic range of signal input caused by the integrator. The proposed system produces a wealth of chaos, just by changing one parameter. Circuit simulations are conducted and the chaotic attractors can be observed. Theoretical analysis, computer simulation and calculation of maximum Lyapunov exponent have been used to research the basic dynamics of this system. The consistency of circuit implementation and computer simulations verifies the effectiveness of the system design.

Electronic Nose Feature Extraction Methods: A Review

Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.

Pavlov associative memory in a memristive neural network and its circuit implementation

Associative memory is the process by which an association between two stimuli or a behavior and a stimulus is learned. This paper contributes to propose a memristive neural network and realize the Pavlov associative memory through (a) putting forward a novel average-input-feedback (AIF) learning law; (b) proposing a detailed two-terminal charge-controlled SPICE memristor models; (c) building a memristive neural network (MNN) circuit, for the first time, to realize the Pavlov associative memory. The results prove the effectiveness of AIF on facilitating the memristor for associative learning in memristive neural networks.