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

TL;DR

This paper introduces a brain-inspired winner-take-all neural network architecture with universal approximation capabilities, demonstrating its effectiveness across various complex tasks with high accuracy and low complexity.

Contribution

The paper proposes a novel single-layer neural network architecture inspired by brain mechanisms, proving its universal approximation property and validating its performance on diverse benchmark problems.

Findings

Achieves universal approximation with a single layer.

Outperforms existing methods in accuracy and complexity.

Effective on real-world and synthetic datasets.

Abstract

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.