A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property

11/08/2015
by   E. Lotfi, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/21/2017

On the approximation by single hidden layer feedforward neural networks with fixed weights

Feedforward neural networks have wide applicability in various disciplin...
research
04/10/2018

Cortex Neural Network: learning with Neural Network groups

Neural Network has been successfully applied to many real-world problems...
research
07/28/2020

Brain Emotional Learning-based Prediction Model For the Prediction of Geomagnetic Storms

This study suggests a new data-driven model for the prediction of geomag...
research
08/04/2021

Growing an architecture for a neural network

We propose a new kind of automatic architecture search algorithm. The al...
research
06/04/2021

Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation

Each year a growing number of wind farms are being added to power grids ...
research
03/08/2023

On the Benefits of Biophysical Synapses

The approximation capability of ANNs and their RNN instantiations, is st...

Please sign up or login with your details

Forgot password? Click here to reset