Name
Title | ||
|---|---|---|
Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems. |
Abstract | ||
|---|---|---|
A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 × 500 grid point discretization of the parameter space. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1162/neco.2007.08-06-316 | Neural Computation |
Keywords | Field | DocType |
pattern search algorithm,equivalent accuracy,optimal artificial neural network,conventional tabulation method,chemical system,chemistry approximation,chemical source term,optimal anns,generalized pattern search method,optimization method,optimal ann,pattern search optimization method,parameter space,objective function,transfer function,pattern search,artificial neural network,source term | Discretization,Mathematical optimization,Nonlinear system,Categorical variable,Computer science,Transfer function,Artificial intelligence,Artificial neural network,Grid,Pattern search,Machine learning,Feed forward | Journal |
Volume | Issue | ISSN |
20 | 2 | 0899-7667 |
Citations | PageRank | References |
5 | 0.51 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Ihme | 1 | 14 | 2.13 |
| Alison Marsden | 2 | 52 | 8.83 |
| Heinz Pitsch | 3 | 110 | 13.25 |