To address the problem of diagnostic accuracy and stability degradation caused by random selection initial parameters for wavelet neural network (WNN) fault diagnosis model, this paper proposes a troubleshooting model based on improved gray wolf algorithm (IGWO) network. First, convergence factor policy weight update are redesigned in IGWO algorithm. This study uses nonlinear to balance global ...

Community discovery (CD) under complex networks is a hot discussion issue in network science research. Recently, many evolutionary methods have been introduced to detect communities of networks. However, optimization-based community still suffers from two problems. First, the initialization population quality current algorithm not good, resulting slow convergence speed, and final performance ne...

For Brush-less DC motors to function better under various operating settings, such as constant load situations, variable loading and set speed controller design is essential. Conventional controllers including proportional integral controllers, frequently fall short of efficiency expectations this mostly because the characteristics a motor drive exhibit non linearity. This work proposes hybrid ...

This work proposes a metaheuristic-based approach to hyperparameter selection in multilayer perceptron classify EMG signals. The main goal of the study is improve performance model by optimizing four important hyperparameters: number neurons, learning rate, epochs, and training batches. proposed this shows that optimization using particle swarm gray wolf optimizer significantly improves classif...