36 research outputs found
Synchronization of Memristive FitzHugh-Nagumo Neural Networks
A new mathematical model of neural networks described by diffusive
FitzHugh-Nagumo equations with memristors and linear synaptic coupling is
proposed and investigated. The existence of absorbing set for the solution
semiflow in the energy space is proved and global dynamics of the memristive
neural networks are dissipative. Through uniform estimates and maneuver of
integral inequalities on the interneuron difference equations, it is shown that
exponential synchronization of the neural network at a uniform convergence rate
occurs if the coupling strength satisfies a threshold condition explicitly
expressed by the system parameters, which is illustrated by an example and
numerical simulation experiments.Comment: arXiv admin note: text overlap with arXiv:2209.0194
Finite-time Stability, Dissipativity and Passivity Analysis of Discrete-time Neural Networks Time-varying Delays
The neural network time-varying delay was described as the dynamic properties of a neural cell, including neural functional and neural delay differential equations. The differential expression explains the derivative term of current and past state. The objective of this paper obtained the neural network time-varying delay. A delay-dependent condition is provided to ensure the considered discrete-time neural networks with time-varying delays to be finite-time stability, dissipativity, and passivity. This paper using a new Lyapunov-Krasovskii functional as well as the free-weighting matrix approach and a linear matrix inequality analysis (LMI) technique constructing to a novel sufficient criterion on finite-time stability, dissipativity, and passivity of the discrete-time neural networks with time-varying delays for improving. We propose sufficient conditions for discrete-time neural networks with time-varying delays. An effective LMI approach derives by base the appropriate type of Lyapunov functional. Finally, we present the effectiveness of novel criteria of finite-time stability, dissipativity, and passivity condition of discrete-time neural networks with time-varying delays in the form of linear matrix inequality (LMI)
Recent Advances and Applications of Fractional-Order Neural Networks
This paper focuses on the growth, development, and future of various forms of fractional-order neural networks. Multiple advances in structure, learning algorithms, and methods have been critically investigated and summarized. This also includes the recent trends in the dynamics of various fractional-order neural networks. The multiple forms of fractional-order neural networks considered in this study are Hopfield, cellular, memristive, complex, and quaternion-valued based networks. Further, the application of fractional-order neural networks in various computational fields such as system identification, control, optimization, and stability have been critically analyzed and discussed
Quantized passive filtering for switched delayed neural networks
The issue of quantized passive filtering for switched delayed neural networks with noise interference is studied in this paper. Both arbitrary and semi-Markov switching rules are taken into account. By choosing Lyapunov functionals and applying several inequality techniques, sufficient conditions are proposed to ensure the filter error system to be not only exponentially stable, but also exponentially passive from the noise interference to the output error. The gain matrix for the proposed quantized passive filter is able to be determined through the feasible solution of linear matrix inequalities, which are computationally tractable with the help of some popular convex optimization tools. Finally, two numerical examples are given to illustrate the usefulness of the quantized passive filter design methods
Finite-time decentralized event-triggered feedback control for generalized neural networks with mixed interval time-varying delays and cyber-attacks
This article investigates the finite-time decentralized event-triggered feedback control problem for generalized neural networks (GNNs) with mixed interval time-varying delays and cyber-attacks. A decentralized event-triggered method reduces the network transmission load and decides whether sensor measurements should be sent out. The cyber-attacks that occur at random are described employing Bernoulli distributed variables. By the Lyapunov-Krasovskii stability theory, we apply an integral inequality with an exponential function to estimate the derivative of the Lyapunov-Krasovskii functionals (LKFs). We present new sufficient conditions in the form of linear matrix inequalities. The main objective of this research is to investigate the stochastic finite-time boundedness of GNNs with mixed interval time-varying delays and cyber-attacks by providing a decentralized event-triggered method and feedback controller. Finally, a numerical example is constructed to demonstrate the effectiveness and advantages of the provided control scheme
Finite-time anti-synchronization of multi-weighted coupled neural networks with and without coupling delays
The multi-weighted coupled neural networks (MWCNNs) models with and without coupling delays are investigated in this paper. Firstly, the finite-time anti-synchronization of MWCNNs with fixed topology and switching topology is analyzed respectively by utilizing Lyapunov functional approach as well as some inequality techniques, and several anti-synchronization criteria are put forward for the considered networks. Furthermore, when the parameter uncertainties appear in MWCNNs, some conditions for ensuring robust finite-time anti-synchronization are obtained. Similarly, we also consider the finite-time anti-synchronization and robust finite-time anti-synchronization for MWCNNs with coupling delays under fixed and switched topologies respectively. Lastly, two numerical examples with simulations are provided to confirm the effectiveness of these derived results
Thermodynamic Computing
The hardware and software foundations laid in the first half of the 20th
Century enabled the computing technologies that have transformed the world, but
these foundations are now under siege. The current computing paradigm, which is
the foundation of much of the current standards of living that we now enjoy,
faces fundamental limitations that are evident from several perspectives. In
terms of hardware, devices have become so small that we are struggling to
eliminate the effects of thermodynamic fluctuations, which are unavoidable at
the nanometer scale. In terms of software, our ability to imagine and program
effective computational abstractions and implementations are clearly challenged
in complex domains. In terms of systems, currently five percent of the power
generated in the US is used to run computing systems - this astonishing figure
is neither ecologically sustainable nor economically scalable. Economically,
the cost of building next-generation semiconductor fabrication plants has
soared past $10 billion. All of these difficulties - device scaling, software
complexity, adaptability, energy consumption, and fabrication economics -
indicate that the current computing paradigm has matured and that continued
improvements along this path will be limited. If technological progress is to
continue and corresponding social and economic benefits are to continue to
accrue, computing must become much more capable, energy efficient, and
affordable. We propose that progress in computing can continue under a united,
physically grounded, computational paradigm centered on thermodynamics. Herein
we propose a research agenda to extend these thermodynamic foundations into
complex, non-equilibrium, self-organizing systems and apply them holistically
to future computing systems that will harness nature's innate computational
capacity. We call this type of computing "Thermodynamic Computing" or TC.Comment: A Computing Community Consortium (CCC) workshop report, 36 page