神经网络自适应动态面控制:非线性时滞系统的迟滞输入与动态不确定性

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"Adaptive Neural Network Dynamic Surface Control技术在具有滞后输入和动态不确定性的时滞非线性系统中的应用" 这篇研究论文“Adaptive Neural Network Dynamic Surface Control for a Class of Time-Delay Nonlinear Systems with Hysteresis Inputs and Dynamic Uncertainties”聚焦于一种新型的自适应神经网络动态表面控制策略,该策略应用于一类具有时间延迟、非线性特性、滞后输入以及动态不确定性的复杂系统。论文的主要贡献和知识点包括: 1. **神经网络(NN)建模非线性和未知动态**:论文提出利用神经网络来近似描述系统的非线性行为和未知动态,这使得控制器能够处理未知的非线性不确定性,并且追求跟踪误差的L∞性能。神经网络作为一种强大的非线性函数逼近工具,能够有效地处理复杂的系统模型。 2. **有限覆盖引理与Krasovskii函数**:通过结合有限覆盖引理和神经网络逼近器,论文摒弃了传统的Krasovskii函数,这为实现跟踪误差的L∞性能提供了新的路径。这种方法简化了分析过程,同时也增强了控制策略的适用性和稳定性。 3. **初始化技术与L∞性能**:通过引入初始化技术,论文能够确保跟踪误差的L∞性能得以实现。这意味着即使在系统启动时,控制策略也能迅速收敛并保证系统的性能指标。 4. **广义Prandtl-Ishlinskii(PI)模型**:论文采用了广义的Prandtl-Ishlinskii模型来描述系统的滞后特性。这种模型可以更精确地捕捉滞后输入对系统性能的影响,从而提高控制效果。 5. **动态表面控制(DSC)**:动态表面控制是一种高级的滑模控制方法,它通过在传统滑模控制的基础上引入一个辅助系统,减少了控制信号的抖动,提高了系统的控制精度和鲁棒性。 6. **L∞性能分析**:L∞性能指标是衡量系统在所有可能扰动下的最大响应幅度,确保系统在各种扰动下具有良好的稳定性和鲁棒性。 7. **理论证明与仿真验证**:论文中,作者们对所提出的控制策略进行了理论分析,证明了其稳定性和性能保证,并通过仿真结果验证了该方法的有效性和优越性。 这篇论文为处理具有复杂特性的时滞非线性系统提供了一种创新的控制策略,结合神经网络和动态表面控制的优势,能有效应对滞后输入和动态不确定性,同时保证系统的L∞性能,这对于实际工程应用具有重要的理论和实践价值。

这一段讲的是什么:Abstract—A recent trojan attack on deep neural network (DNN) models is one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker’s chosen trojan trigger. Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation. This work builds STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system and focuses on vision system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of predicted classes for perturbed inputs from a given deployed model—malicious or benign. A low entropy in predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input—a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on three popular and contrasting datasets: MNIST, CIFAR10 and GTSRB. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for different types of triggers. Using CIFAR10 and GTSRB, we have empirically achieved result of 0% for both FRR and FAR. We have also evaluated STRIP robustness against a number of trojan attack variants and adaptive attacks. Index Terms—Trojan attack, Backdoor attack

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