Convolutional neural networks (CNNs) are widely used in medical image classification, yet their performance is often hindered by data imbalance, limited adaptability, and overfitting. This study aims to improve CNN robustness by introducing a reinforcement learning (RL) framework based on Deep Deterministic Policy Gradients (DDPG) to automatically optimize data augmentation parameters. We propose an RL-driven optimization framework in which a DDPG agent autonomously adjusts data augmentation parameters to improve classification performance. The framework is evaluated across several benchmark datasets, including breast cancer, brain tumor, and COVID-19 X-ray images. Seven publicly available CNN architectures and a transformer model are used for comparison. Explainable artificial intelligence (XAI) using Grad-CAM is employed to validate model attention and prediction reliability. The proposed RL-based approach enables CNNs to rapidly identify optimal augmentation parameters, yielding superior performance compared to seven state-of-the-art CNN baselines and existing RL-integrated models. The DDPG agent demonstrates significant capability in automatically adapting augmentation strategies, consistently improving classification accuracy across medical. Grad-CAM visualizations further confirm improved feature focus and enhanced prediction consistency. The RL-driven DDPG framework effectively enhances image classification performance by autonomously optimizing augmentation parameters and improving CNN adaptability. Validation on multiple datasets and architectures—including transformers—shows that RL contributes to varying degrees of performance gains, establishing the proposed method as a promising direction for robust medical image analysis. The code is accessible at https://github.com/AIPMLab/DDPG.
This paper investigates the fuzzy distributed reinforcement learning (RL) optimal resilient control problem for multiple unmanned surface vehicle (multi-USV) systems under denial-of-service (DoS) attacks. Firstly, the considered multi-USV systems are modeled by Takagi-Sugeno (T-S) fuzzy systems. Then, a resilient distributed formation observer is developed to estimate the leader’s states. A fuzzy optimal resilient formation control scheme is proposed by using the proposed resilient observer and game theory. Considering that the analytical solution of the optimized control is transformed into the game algebraic Riccati equations (GAREs), which are difficult to obtain analytically, a value iteration (VI) learning algorithm is designed to learn the approximate solutions of GAREs under a mild initial excitation (IE) condition. It is proved that the designed controller guarantees that multi-USV systems achieve the desired formation shape with DoS attacks. In addition, the proposed learning algorithm is demonstrated to be convergent and the persistence of excitation (PE) condition in the existing RL algorithms is relaxed. Finally, the simulation and comparison results confirm the feasibility of the proposed optimized control approach.
This paper introduces a family of FMAVT-Sort models based on a fuzzy extension of the Multi-Criteria Decision Analysis (MCDA) method MAVT, alongside a framework for Uncertainty and Distinction Quantification (UQ and DQ) of the model outputs. The models differ in approaches to evaluating functions of fuzzy numbers (FNs), employing both precise and approximate techniques, and utilize four fuzzy ranking methods: defuzzification-based approaches (Centroid Index and Integral of Means), and two methods designed for ranking dependent FNs. The models are applied to a case study on university teacher evaluation. Assessment of local (within the case study) UQ and DQ and global (across a space of independent multicriteria problems) DQ is performed using algorithms that process output values from a large number of fuzzy multicriteria sorting problems simulated via Monte Carlo techniques in a fuzzy environment. A set of UQ indices, combined with a pre-uncertainty analysis stage, is implemented in a General Algorithm for Multicriteria Sorting, designed to support the decision-making process. The results demonstrate the effectiveness of UQ indices for decision support in multicriteria sorting and reveal significant distinctions among FMAVT-Sort models, underscoring the impact of model selection on final sorting outcomes. These findings hold scientific, methodological, and practical significance.
High-precision tracking of robotic manipulators is challenged by nonlinear friction, model mismatch, external disturbances, and abrupt actuator faults. Conventional robust controllers often lump these effects into a single uncertainty, resulting in conservative gains and high control effort. This paper develops a novel fixed-time active fault-tolerant control (AFTC) framework that couples sparse physics-informed dynamics learning with fixed-time robust compensation. A physics-informed neural network with sparse regression (PINN–SR) learns the physics gap between the nominal model and the plant and identifies parsimonious, interpretable terms for friction and structured uncertainties. A fixed-time fault observer then reconstructs the remaining lumped perturbations, including actuator faults, disturbances, and residual approximation errors. Using these compensations, a novel nonsingular fixed-time sliding-mode controller with a state-dependent exponent and hyperbolic-secant gain guarantees convergence within a settling-time bound independent of initial conditions; fixed-time stability is proven via Lyapunov analysis. MATLAB/Simulink–Simscape simulations of a 3-DOF SAMSUNG FARA AT2 manipulator against five recent controllers show improved performance. In the fault-free case, convergence time is 0.276–0.322 s, and the integral absolute error is 0.0186–0.0506rad (38–42% and approximately 40% improvements over the best baseline results, respectively). Under actuator faults, the proposed method further reduces the tracking error by 41–51% while preserving smooth control inputs.
In complex industrial scenarios, due to equipment aging and changing operating conditions, rotating machinery often exhibits new fault modes. Existing deep learning-based fault diagnosis models suffer from catastrophic forgetting during incremental learning under distribution drift, leading to historical knowledge loss and overfitting to new tasks. To address this issue, we propose a Synergistic Weight Refocusing Mechanism (SWRM) for few-shot class-incremental fault diagnosis under varying operating conditions. SWRM coordinates feature adaptation, prototype construction, and metric calibration through sequential information transfer, thereby improving the stability-plasticity balance in incremental learning. Specifically, an efficient channel attention-enhanced refocusing convolutional module keeps the pretrained backbone fixed to preserve historical knowledge while introducing learnable adaptation layers to refocus features toward new faults. A gated multi-scale prototype fusion mechanism processes task-adaptive features, suppresses noise through dynamic aggregation, generates robust fault prototypes, and transfers the learned gating weights as scale priors for calibration. Finally, a scale-prior adaptive meta-calibration module utilizes the fused prototypes and inherited scale priors as adaptive constraints to align old-class classifiers and new-class prototypes into a unified metric space for classification. Experiments on the MBHM dataset demonstrate that SWRM outperforms baseline methods in diagnostic accuracy and anti-forgetting performance, and ablation studies further verify its effectiveness.
