Related Experiment Video
Updated: Jan 1, 2026
05:59
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
Published on: October 6, 2023
3.1K
A Unified Framework Design for Finite-Time and Fixed-Time Synchronization of Discontinuous Neural Networks
IEEE Transactions on Cybernetics
|December 28, 2019
Summary
This study introduces a unified framework for finite-time and fixed-time synchronization in discontinuous neural networks. Novel control strategies ensure networks reach and maintain synchronization within a set time.
Area of Science:
- Control Systems Engineering
- Computational Neuroscience
- Applied Mathematics
Background:
- Discontinuous neural networks present challenges in achieving synchronization.
- Existing methods for finite-time/fixed-time synchronization lack a unified approach.
Purpose of the Study:
- To develop a unified framework for finite-time and fixed-time synchronization of discontinuous neural networks.
- To introduce novel control strategies and sliding-mode manifolds for enhanced synchronization.
- To establish criteria for parameter selection and provide setting time estimations.
Main Methods:
- A novel unified integral sliding-mode manifold was designed.
- Unified control strategies were developed to guide network dynamics.
- Criteria for parameter selection were established to ensure manifold convergence.
- Setting time estimations were mathematically derived.
Main Results:
- The proposed framework successfully achieves finite-time and fixed-time synchronization.
- The unified approach allows for diverse control protocols through parameter variation.
- The method demonstrates superiority over existing techniques, as shown by numerical examples.
Conclusions:
- The developed unified framework effectively addresses finite-time/fixed-time synchronization in discontinuous neural networks.
- The novel sliding-mode manifold and control strategies offer flexibility and improved performance.
- This research extends previous synchronization results and validates its efficacy through simulations.
Related Concept Videos
01:25Neural Circuits
2.5K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.5K
01:23Linear time-invariant Systems
813
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...
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...
813
01:24BIBO stability of continuous and discrete -time systems
849
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....
849
01:22Basic Continuous Time Signals
635
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
635
01:11Sampling Continuous Time Signal
617
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...
617
01:28Neuronal Communication
2.8K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
2.8K