Optimal Hidden Structure for Feedforward Neural Networks
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
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IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Expert Mutation Operators for the Evolution of Radial Basis Function Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Hybrid Framework for Neuro-Dynamic Programming Application to Water Supply Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
On different facets of regularization theory
Neural Computation
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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Orthogonal transformation, which can lead to compaction of information, has been used in two ways to optimize on the size of feedforward networks: 1) through the selection of optimum set of time-domain inputs, and the optimum set of links and nodes within a neural network (NN); and 2) through the orthogonalization of the data to be used in NN's, in case of processes with periodicity. The proposed methods are efficient and are also extremely robust numerically. The singular value decomposition (SVD) and QR with column pivoting factorization (QRcp) are the transformations used. SVD mainly serves as the null space detector; QRcp coupled with SVD is used for subset selection, which is one of the main operations on which the design of the optimal network is based. SVD has also been used to devise a new approach for the assessment of the convergence of the NN's, which is an alternative to the conventional output error analysis