Related Experiment Videos
Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under
Jie Mi1, Rizhao Gong1, Quanxin Zhu2
1School of Mathematics and Statistics, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China.
Summary
This study achieves finite-time secure synchronization for neural networks with delays and stochastic disturbances under intermittent denial-of-service attacks using a novel stability lemma and auxiliary system.
Area of Science:
- Control Theory
- Neural Networks
- Cybersecurity
Background:
- Neutral-type stochastic delayed neural networks (NSDNNs) face challenges with denial-of-service (DoS) attacks.
- Traditional stability analysis methods are insufficient for NSDNNs under intermittent DoS attacks due to controller failure intervals.
Purpose of the Study:
- To investigate finite-time secure synchronization (FNTSS) of NSDNNs under aperiodic intermittent DoS attacks.
- To develop novel methods overcoming limitations of existing stability analysis for such systems.
Main Methods:
- A generalized Halanay inequality incorporating multiple delays and attack intervals was established.
- A specialized auxiliary system (AS) was constructed to manage the neutral term.
- An aperiodically intermittent controller (AIC) and equivalence technique (EVT) were utilized for stability analysis.
Main Results:
- Finite-time stability criteria for the AS under DoS attacks were derived.
- The FNTSS in the mean square of NSDNNs was achieved using the EVT, contingent on AS stability and bounded delays.
Conclusions:
- The proposed control scheme effectively achieves FNTSS for NSDNNs under intermittent DoS attacks.
- The developed generalized Halanay inequality and auxiliary system approach offer a robust solution for secure synchronization problems.
Related Concept Videos
01:23Linear time-invariant Systems
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
01:07Time-Domain Interpretation of PD Control
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
Consider the example of control of motor torque. Initially, a positive...
01:24BIBO stability of continuous and discrete -time systems
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
01:25Multimachine Stability
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
01:35Long-term Potentiation
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
01:31Classification of Systems-II
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,