On-Line Learning Fokker-Planck Machine
Neural Processing Letters
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences
Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Learning Probability Densities of Optimization Problems with Constraints and Uncertainty
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
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A new stochastic procedure is applied to optimization problems that arise in the nonlinear modeling of data. The proposed technique is an implementation of a recently introduced algorithm for the construction of probability densities that are consistent with the asymptotic statistical properties of general stochastic search processes. The obtained densities can be used, for instance, to draw suitable starting points in nonlinear optimization algorithms. The proposed setup is tested on a benchmark global optimization example and in the weight optimization of an artificial neural network model. Two additional examples that illustrate aspects that are specific to data modeling are outlined.