… 3 with the "training and optimization" block being the atmost essential element of the … is
initialized to collaborate with parameter optimization using the HPSGW. The HPSGW is initialized …

… We used the proposed variants to train artificial neural networks on classification problems.
In … approaches in order to initialize the initial population. We proposed two new initialization …

… Step 2: A MLPNNs is trained using the initialized positions of the … To train a feed-forward
neural network we have to adjust the number of hidden neurons in hidden layers of the network. …

… and PSO algorithms need an initialization of the population for starting … It uses an ANN with
the LMBP algorithm for training the ANN … We assume that the training dataset with all the input …

… its network structure through neural network training … 1) Initialize particle swarm parameters
and BiLSTM network structure. Particle swarm parameters mainly include the particle swarm …

… An appropriate technique initializes the weights such that the training of the network is …
advances in weight initialization for neural networks. The weight initialization techniques in …

… artificial neural network (ANN) technique and particle swarm … out to optimally choose the
training algorithm, number of … of particles, maximum number of iterations, etc.) and initialize …

… (GA), particle swarm optimization (PSO), artificial bee colony (ABC), … Input parameters are
initialized within the specified range, … or training parameters to obtain the best structure network …

… This study focuses on applying an artificial neural network trained by particle … appropriate
particle position between a randomly initialized population. The P b e s t and G b e s t of each …

… neural network (NN) is trained by the particle swarm optimization algorithm (PSO). The NN
is implemented in hardware, and the PSO training … Initialize random values for all particles at …