… After the simulation, the results showed that the training of neural networks with the WE-PSO
algorithm is better in terms of precision and its efficiency is much higher than the traditional …

… Unfortunately, the training of simple recurrent neural networks is problematic because of …
a new training algorithm based on particle swarm optimization. The algorithm does not use …

… a modified particle swarm optimization (PSO) variant with two-level learning phases to train
… can be significantly deteriorated if particles are initialized at solution regions far away from …

… Proper initialization of iteratively calculated optimization algorithms is important and often …
In Particle Swarm Optimization algorithm analyzed in this work, initial values of position …

… on a Particle Swarm Optimization (PSO) method, which is combined with an artificial neural
network (… Input of the ANN is a candidate subset of features, and it is trained to predict crash …

… The initialization stage is to set algorithm limitations, swarm … Using the feedforward neural
network, which biologically … , involves multilayer to train neural networks with the two layers …

… the Particle Swarm Optimization (PSO), a heuristic optimization, for training neural network
… PSO starts by initializing a random set of particles as the solutions. It is including the initial …

… In our experimental studies, we initialize a swarm of 20 particles, a maximum of 10 iterations,
and … Specifically, these models train and test each optimized candidate network using the …

… The performed method has been implemented in the Matlab. The total time that consists of
training, testing stages and the estimation results takes approximately 150 seconds. Stopping …

… are initialized to generate the initial particle positions and velocities. Then the PSO optimizes
the training of the neural network … Each model is evaluated in the training, testing, and …