Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Tabu Search
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Approximating Unknown Mappings: An Experimental Evaluation
Journal of Heuristics
A SOM-based data mining strategy for adaptive modelling of an offset lithographic printing process
Engineering Applications of Artificial Intelligence
| Hi-index | 0.01 |
Artificial neural networks (ANN) are inspired by the structure of biological neural networks and their ability to integrate knowledge and learning. In ANN training, the objective is to minimize the error over the training set. The most popular method for training these networks is back propagation, a gradient descent technique. Other non-linear optimization methods such as conjugate directions set or conjugate gradient have also been used for this purpose. Recently, metaheuristics such as simulated annealing, genetic algorithms or tabu search have been also adapted to this context.There are situations in which the necessary training data are being generated in real time and, an extensive training is not possible. This "on-line" training arises in the context of optimizing a simulation. This paper presents extensive computational experiments to compare 12 "on-line" training methods over a collection of 45 functions from the literature within a short-term horizon. We propose a new method based, on the tabu search methodology, which can compete in quality with the best previous approaches.