An efficient multi-objective parrot optimizer for global and engineering optimization problems. [PDF]
Saad MR, Emam MM, Houssein EH.
europepmc +1 more source
Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah +4 more
wiley +1 more source
Multiple strategies improved spider wasp optimization for engineering optimization problem solving. [PDF]
Sui J, Tian Z, Wang Z.
europepmc +1 more source
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
A Multi-Strategy Improvement Secretary Bird Optimization Algorithm for Engineering Optimization Problems. [PDF]
Qin S, Liu J, Bai X, Hu G.
europepmc +1 more source
Observer‐Based Adaptive Event‐Triggered Tracking Control for Fuzzy TS Systems With Premise Mismatch
This paper presents an adaptive logistic event‐triggered observer‐based tracking controller for Takagi‐Sugeno fuzzy systems under constrained inputs and network delays. Leveraging a hybrid LMI and Secretary Bird Optimization approach, this strategy significantly minimizes communication overhead and computational burden while ensuring optimal reference ...
Oussama Djadane +3 more
wiley +1 more source
Fractional-Order Boosted Hybrid Young's Double-Slit Experimental Optimizer for Truss Topology Engineering Optimization. [PDF]
Qin S, Liu J, Bai X, Hu G.
europepmc +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +2 more
wiley +1 more source
Engineering Optimization of Producing High-Purity Dichlorosilane in a Fixed-Bed Reactor by Trichlorosilane Decomposition. [PDF]
Liu JH +5 more
europepmc +1 more source

