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Nonconvex Activated Fuzzy Zeroing Neural Network-based (NAFZNN) and Nonconvex Activated Fuzzy Noise-Tolerant Zeroing Neural Network-based (NAFNTZNN) models are devised and analyzed, drawing inspiration from the classical ZNN/NTZNN-based model for online addressing Time-Varying Quadratic Programming Problems (TVQPPs) with Equality and Inequality Constraints (EICs) in noisy circumstances, respectively. Furthermore, the proposed NAFZNN model and NAFNTZNN model are considered as general proportion-differentiation controller, along with general proportion-integration-differentiation controller. Besides, theoretical results demonstrate the global convergence of both the NAFZNN and NAFNTZNN models for TVQPPs with EIC under noisy conditions. Moreover, numerical results illustrate the efficiency, robustness, and ascendancy of the NAFZNN and NAFZNN models in addressing TVQPPs online, exhibiting inherent noise tolerance. Ultimately, an application example to plant leaf disease identification is conducted to support the feasibility and efficacy of the designed NAFNTZNN model, which shows its potential practical value in the field of image recognition.
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