Adaptive Recursive Neural Network Architecture

Abstract

This paper proposes a novel adaptive recursive neural network (ARNN) architecture designed to overcome the limitations of traditional, static recurrent neural networks. The core idea is to introduce dynamic adaptation of the network's recursive structure, leveraging evolutionary algorithms or reinforcement learning, to optimize learning efficiency and generalization performance. The system dynamically adjusts connection weights and hidden unit configurations within the recurrent layers based on network performance feedback. This contrasts sharply with conventional neural network design, which typically employs fixed architectures. Mathematical formulations and algorithmic descriptions are presented to detail the proposed architecture and the adaptive learning process. The presented approach aims to significantly improve the ability of RNNs to handle complex sequential data and achieve superior results compared to static RNNs. The primary contribution lies in the development of a robust and flexible framework for adaptive recurrent learning.