In this paper, we consider the predefined-time synchronization problem of stochastic memristor-based bidirectional associative memory neural networks (MBAMNNs). Firstly, considering the influence of stochastic disturbance, we propose two new predefined-time theorems. Secondly, combined with the predefined-time theorem proposed in this paper, we design a feedback controller to realize the predefined-time synchronization of MBAMNNs. Then, the sufficient conditions for the predefined-time synchronization of MBAMNNs are obtained based on Ito’s formula and Lyapunov theorem. Finally, the effectiveness of the theorems are verified by numerical simulations.

Anomaly detection plays a vital role as a crucial security measure for edge devices in Artificial Intelligence and Internet of Things (AIoT). With the rapid development of IoT ( Internet of Things), changes in system configurations and the introduction of new devices can lead to significant alterations in device relationships and data flows within the IoT, thereby triggering concept drift. Previously trained anomaly detection models fail to adapt to the changed distribution of streaming data, resulting in a high number of false positive events. This paper aims to address the issue of concept drift in IoT anomaly detection by proposing a comprehensive Concept Drift Detection, Interpretation, and Adaptation framework (CDDIA). We focus on accurately capturing the concept drift of normal data in unsupervised scenarios. To interpret drift samples, we integrate a search optimization algorithm and the SHAP method, providing a comprehensive interpretation of drift samples at both the sample and feature levels. Simultaneously, by utilizing the sample-level interpretation results for filtering new and old samples, we retrain the anomaly detection model to mitigate the impact of concept drift and reduce the false positive rate. This integrated strategy demonstrates significant advantages in maintaining model stability and reliability. The experimental results indicate that our method outperforms five baseline methods in adaptability across three datasets and provides interpretability for samples experiencing concept drift.

Based on variable substitution, calculating definite integral and solving the minimization problem, in this paper we establish a new fixed-time stability theorem, which can provide a novel upper bound estimate formula for the settling time. By dividing the considered complex-valued neural networks (CVNNs) into double-layer real-valued neural networks, the fixed-time synchronization of CVNNs is analyzed by means of the new fixed-time stability theorem. Both theoretical derivation and numerical simulation show the new upper bound estimate formula for the settling time in this paper is more accurate than that given in the classic fixed-time stability theorem. A numerical example is given to verify the effectiveness of the main results.