This paper investigates the fixed/prescribed-time synchronization problem for fuzzy inertial memristive neural networks (FIMNNs) with hybrid delays. Within a unified framework, a comparative study was conducted on the interval matrix method and the maximum absolute value method with respect to the state-dependent switching parameters induced by memristive characteristics. Correspondingly, two different controllers are designed, with theorem constraints expressed algebraically and as LMIs. Numerical experiments demonstrate that, under identical initial system parameters, the interval matrix method constructs a Lyapunov–Krasovskii functional (LKF) incorporating an integral term, enabling finer handling of time-delay effects. As a result, it provides a more accurate estimate of the upper bound of the settling time (ST) compared to the maximum absolute value method, thereby achieving faster and more efficient synchronization control. Ultimately, the proposed results are applied to image encryption, thereby demonstrating its theoretical significance and practical utility.

The accurate prediction of wind power is critical for achieving dynamic equilibrium in economic energy scheduling, storage allocation, and generation planning within power systems . To address the challenges of excessive modal decomposition components and low prediction efficiency resulting from the chaotic, intermittent, and non-stationary nature of wind power signals, a sophisticated prediction method integrating aggregate modal decomposition with a hybrid network model is proposed. Preliminarily, the wind power sequence is decomposed into several primary components using CEEMDAN, and these components are paired to form primary aggregation components, excluding the main trend component. Subsequently, the components of the primary aggregation that exceed the critical threshold of relative sample entropy are re-aggregated and re-decomposed by VMD. Finally, the primary trend component is combined with the prediction of LSTM, the primary aggregation components are estimated through the integration of BiLSTM, and the secondary decomposition components are measured by Attention-BiLSTM. These predictive values are then reconstructed to obtain wind power forecasts. Experimental analysis on a wind power dataset has shown that the proposed approach outperforms other models, significantly enhancing prediction efficiency and accuracy.