A novel fast Kolmogorov's spline complex network for pattern detection
WSEAS TRANSACTIONS on SYSTEMS
A novel fast Kolmogorov's spline complex network for pattern detection
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Applying falsity input to neural networks to solve single output regression problems
ACACOS'11 Proceedings of the 10th WSEAS international conference on Applied computer and applied computational science
Fast decorrelated neural network ensembles with random weights
Information Sciences: an International Journal
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A method is suggested for learning and generalization with a general one-hidden layer feedforward neural network. This scheme encompasses the use of a linear combination of heterogeneous nodes having randomly prescribed parameter values. The learning of the parameters is realized through adaptive stochastic optimization using a generalization data set. The learning of the linear coefficients in the linear combination of nodes is achieved with a linear regression method using data from the training set. One node is learned at a time. The method allows for choosing the proper number of net nodes, and is computationally efficient. The method was tested on mathematical examples and real problems from materials science and technology