Knowledge-Based Neural Networks for Modelling Time Series

Various methods exist for extracting rules from data for classi cation purposes. We propose a new method for initializing a neural network used for time series modelling and prediction. We extract binary rules from a real valued time series and encode them into a neural network using an adaptation of KBANN. We test the method on the Lorenz system as well as on real world data in the form of a seismic time series

Maturity, distance and density (MD 2) metrics for optimizing trust prediction for business intelligence

Trust, Trust prediction, Optimization, Prediction, Business intelligence, Optimized business intelligence,

Study of the Ground-State Geometry of Silicon Clusters Using Artificial Neural Networks

Theoretical determination of the ground-state geometry of Si clusters is a difficult task. As the number of local minima grows exponentially with the number of atoms, to find the global minimum is a real challenge. One may start the search procedure from a random distribution of atoms but it is probably wiser to make use of any available information to restrict the search space. Here, we introduce a new approach, the Assisted Genetic Optimization (AGO) that couples an Artificial Neural Network (ANN) to a Genetic Algorithm (GA). Using available information on small Silicon clusters, we trained an ANN to predict good starting points (initial population) for the GA. AGO is applied to Si10 and Si20 and compared to pure GA. Our results indicate: i) AGO is, at least, 5 times faster than pure GA in our test case; ii) ANN training can be made very fast and successfully plays the role of an experienced investigator; iii) AGO can easily be adapted to other optimization problems