NDLI: Some new neural network architectures with improved learning schemes

Content Provider	Springer Nature Link
Author	Sinha, M. Kumar, K. Kalra, P. K.
Copyright Year	2000
Abstract	Here, we present two new neuron model architectures and one modified form of existing standard feedforward architecture (MSTD). Both the new models use self-scaling scaled conjugate gradient algorithm (SSCGA) and lambda–gamma (L–G) algorithm and entail the properties of basic as well as higher order neurons (i.e., multiplication and the aggregation functions). Of these two, compensatory neural network architecture (CNNA) requires relatively smaller number of inter-neuronal connections, cuts down on the computational budget by almost 50% and speeds up convergence, besides, gives better training and prediction accuracy. The second model sigma–pi–sigma (SPS) ensures faster convergence, better training and prediction accuracy. The third model (MSTD) performs much better than the standard feedforward architecture (STD). The effect of normalizing the outputs for training also studied here shows virtually no improvement, at low iteration level, say ∼500, with increasing range of scaling. Increasing the number of neurons beyond a point also shows to have little effect in the case of higher order neuron.The numerous simulation runs for the problem of satellite orbit determination and the complex XOR problems establishes the robustness of the proposed neuron models architectures.
Starting Page	214
Ending Page	223
Page Count	10
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	4
Issue Number	4
Language	English
Publisher	Springer-Verlag
Publisher Date	2000-12-14
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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