Particle swarm optimization for neural network learning enhancement

The AFSA is intelligence and random search algorithm generated by studying the behavior of fish swarm in nature, solving optimization problem using swarm

The results show that for cancer dataset, Quantum Particle Swarm Optimization in Elman Recurrent Neural Network (QPSOERN) with bounded Vmax of hyperbolic tangent

The particle swarm optimization (PSO) algorithm is firstly proposed to search for the weights of the Chebyshev neural networks (CNNs), and then an adaptive controller for the

Key words: Radial basis function, adaptive chaotic particle swarm optimization, standard particle swarm optimization, genetic algorithm, neuro-fuzzy systems, bioreactor cell control

Pada penelitian ini dilakukan pengujian model dengan menggunakan Probabilistic Neural Network (PNN) dan Probabilistic Neural Network & Particle Swarm Optimization

In this work, a model is developed to train a Deep Neural Network (DNN) with Particle Swarm Optimization (PSO) Algorithm for predicting Protein Secondary

In the experiments, we use two different PSC approaches, namely Multi-elitist Particle Swarm Optimization (MEPSO) [1] and Multi-dimensional Particle Swarm Optimization (MD-PSO)

Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established