Pages that link to "Item:Q387497"

The following pages link to Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm (Q387497):

Displaying 18 items.

• GSA for machine learning problems: a comprehensive overview (Q823283) (← links)
• A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training (Q872357) (← links)
• Autonomous particles groups for particle swarm optimization (Q1639896) (← links)
• A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design (Q1642969) (← links)
• Stability analysis of particle dynamics in gravitational search optimization algorithm (Q1671295) (← links)
• Training artificial neural networks by a hybrid PSO-CS algorithm (Q1736659) (← links)
• A new swarm intelligence approach for clustering based on krill herd with elitism strategy (Q1736727) (← links)
• Two kinds of classifications based on improved gravitational search algorithm and particle swarm optimization algorithm (Q1798354) (← links)
• A review on computational intelligence for identification of nonlinear dynamical systems (Q2023111) (← links)
• Quantum particle swarm optimization algorithm with the truncated mean stabilization strategy (Q2102250) (← links)
• Hybrid particle swarm optimization and pattern search algorithm (Q2129208) (← links)
• A novel Hausdorff fractional NGMC\((p,\mathrm{n})\) grey prediction model with grey wolf optimizer and its applications in forecasting energy production and conversion of China (Q2247278) (← links)
• Solving Dirichlet boundary problems for ODEs via swarm intelligence (Q2690417) (← links)
• A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy (Q2798463) (← links)
• Gravity training method (Q2993035) (← links)
• (Q3047728) (← links)
• GSA improvement via the von Neumann stability analysis (Q6095505) (← links)
• A new decision making method for selection of optimal data using the von Neumann-Morgenstern theorem (Q6549298) (← links)