288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

November 2009, Vol. 21, No. 11, Pages 3214-3227
(doi: 10.1162/neco.2009.06-07-542)
© 2009 Massachusetts Institute of Technology
Adaptive Capability of Recurrent Neural Networks with Fixed Weights for Series-Parallel System Identification
Article PDF (113.78 KB)

By a fundamental neural filtering theorem, a recurrent neural network with fixed weights is known to be capable of adapting to an uncertain environment. This letter reports some mathematical results on the performance of such adaptation for series-parallel identification of a dynamical system as compared with the performance of the best series-parallel identifier possible under the assumption that the precise value of the uncertain environmental process is given. In short, if an uncertain environmental process is observable (not necessarily constant) from the output of a dynamical system or constant (not necessarily observable), then a recurrent neural network exists as a series-parallel identifier of the dynamical system whose output approaches the output of an optimal series-parallel identifier using the environmental process as an additional input.