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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Portoroz, Slovenia, 1999

Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Portoroz, Slovenia, 1999

Dobnikar, Andrej
Steele, Nigel C.
Pearson, David W.

Paperback

General ComputersGeneral GardeningGeneral Mathematics

ISBN10: 3211833641
ISBN13: 9783211833643
Publisher: Springer Nature
Published: Jul 15 1999
Pages: 352
Weight: 1.84
Height: 0.77 Width: 8.25 Depth: 11.00
Language: English
From the contents: Neural networks - theory and applications: NNs (= neural networks) classifier on continuous data domains- quantum associative memory - a new class of neuron-like discrete filters to image processing - modular NNs for improving generalisation properties - presynaptic inhibition modelling for image processing application - NN recognition system for a curvature primal sketch - NN based nonlinear temporal-spatial noise rejection system - relaxation rate for improving Hopfield network - Oja's NN and influence of the learning gain on its dynamics Genetic algorithms - theory and applications: transposition: a biological-inspired mechanism to use with GAs (= genetic algorithms) - GA for decision tree induction - optimising decision classifications using GAs - scheduling tasks with intertask communication onto multiprocessors by GAs - design of robust networks with GA - effect of degenerate coding on GAs - multiple traffic signal control using a GA - evolving musical harmonisation - niched-penalty approach for constraint handling in GAs - GA with dynamic population size - GA with dynamic niche clustering for multimodal function optimisation Soft computing and uncertainty: self-adaptation of evolutionary constructed decision trees by information spreading - evolutionary programming of near optimal NNs

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