  Here, we propose a brain-inspired winner-take-all emotional neural network
(WTAENN) and prove the universal approximation property for the novel
architecture. WTAENN is a single layered feedforward neural network that
benefits from the excitatory, inhibitory, and expandatory neural connections as
well as the winner-take-all (WTA) competitions in the human brain s nervous
system. The WTA competition increases the information capacity of the model
without adding hidden neurons. The universal approximation capability of the
proposed architecture is illustrated on two example functions, trained by a
genetic algorithm, and then applied to several competing recent and benchmark
problems such as in curve fitting, pattern recognition, classification and
prediction. In particular, it is tested on twelve UCI classification datasets,
a facial recognition problem, three real world prediction problems (2 chaotic
time series of geomagnetic activity indices and wind farm power generation
data), two synthetic case studies with constant and nonconstant noise variance
as well as k-selector and linear programming problems. Results indicate the
general applicability and often superiority of the approach in terms of higher
accuracy and lower model complexity, especially where low computational
complexity is imperative.
