Computationally efficient sequential learning algorithms for direct link resource-allocating networks

Computionally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). These are achieved by decomposing existing recursive training algorithms on a layer by layer and neuron by neuron basis. This allows network weights to be updated in an efficient parallel manner and facilitates the implementation of minimal update extensions that yield a significant reduction in computation load per iteration compared to existing sequential learning methods employed in resource-allocation network (RAN) and minimal RAN (MRAN) approaches. The new algorithms, which also incorporate a pruning strategy to control network growth, are evaluated on three different system identification benchmark problems and shown to outperform existing methods both in terms of training error convergence and computational efficiency. (c) 2005 Elsevier B.V. All rights reserved.

Effective collision strengths for allowed transitions among the n

Aims.In this paper we report on calculations of collision strengths and effective collision strengths for allowed transitions among the $n \le$ 5 degenerate levels of Al XIII.
Methods.The Dirac atomic R-matrix code (DARC) has been adopted for these calculations.
Results.Collision strengths are reported over a wide energy range below 300 Ryd, and effective collision strengths are provided for electron temperatures of 4.4 $\le$ log $T_{\rm e} \le$ 6.8 K.