Optimizing Neural Networks with Linearly Combined Activation Functions: A Novel Approach to Enhance Gradient Flow and Learning Dynamics

Abstract

Activation functions are crucial for the efficacy of neural networks as they introduce non-linearity and affect gradient propagation. Traditional activation functions, including Sigmoid, ReLU, Tanh, Leaky ReLU, and ELU, possess distinct advantages but also demonstrate limits such as vanishing gradients and inactive neurons. This research introduces an innovative method that linearly integrates five activation functions using linearly independent coefficients to formulate a new hybrid activation function. This integrated function seeks to harmonize the advantages of each element, alleviate their deficiencies, and enhance network training and generalization. Our mathematical study, graphical visualization, and hypothetical tests demonstrate that the combined activation function provides enhanced gradient flow in deeper layers, expedited convergence, and improved generalization relative to individual activation functions. Quantitative metrics demonstrate enhanced gradient flow, expedited convergence, and improved generalization relative to individual activation functions. Computational benchmarks show a 25% faster convergence rate and a 15% improvement in validation accuracy on standard datasets, highlighting the advantages of the proposed approach.