反应-扩散分数顺序记忆神经网络的稳定
概括
这项研究引入了一种新方法,以反应-扩散项稳定分数顺序的记忆神经网络. 这种方法确保了使用利亚普诺夫稳定性理论和Hardy-Poincaré不等式应用的全局非对称和Mittag-Leffler稳定性.
科学领域:
- 控制理论 控制理论
- 神经网络的神经网络的神经网络
- 非线性动力学是一种非线性动力学.
背景情况:
- 分数顺序系统提供了增强的建模功能.
- 记忆神经网络表现出复杂的动态.
- 反应-扩散术语引入空间依赖.
研究的目的:
- 开发一个稳定控制策略,以分数顺序的记忆神经网络与反应-扩散条款.
- 解决稳定性分析中扩散术语所带来的挑战.
- 为了确保全球的异位和米塔格-莱弗勒稳定性.
主要方法:
- 一种基于Hardy-Poincaré不等式的新处理方法,用于估计扩散项.
- 卡库塔尼的固定点定理,用于确定平衡点的存在.
- 对于控制器设计和稳定性验证的莱普诺夫稳定性理论.
主要成果:
- 一种新的方法,以减少保守主义来估计扩散项.
- 可以测试平衡点存在的代数条件.
- 一个规定的控制器,保证了全球对称/米塔格-莱弗勒稳定性.
结论:
- 拟议的控制方法有效地稳定了分数顺序的记忆神经网络,使用反应-扩散项.
- 这种新方法提供了不那么保守的稳定性条件.
- 通过一个说明性的例子来验证这些发现.
相关概念视频
The Nernst Equation
41.5K
Nonstandard Reaction Conditions
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
41.5K
Second Order systems II
137
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
137
First Order Systems
129
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
129
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model
98
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
98
Multi-Step Reactions
7.4K
Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
7.4K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
84
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
84


