Neural Networks
Computational neuroscience
`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Modular Learning in Neural Networks: A Modularized Approach to Neural Network Classification
Modular Learning in Neural Networks: A Modularized Approach to Neural Network Classification
Two Strategies of Adaptive Cluster Covering with Descent and Their Comparison to Other Algorithms
Journal of Global Optimization
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Generalized pattern searches with derivative information
Mathematical Programming: Series A and B
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Hybrid Systems for River Flood Forecasting Using MLP, SOM and Fuzzy Systems
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
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Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses - base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.