基于动态自适应的自适应神经网络

Abstract

This paper presents a novel self-adaptive neural network architecture, termed Dynamic Adaptive Neural Network (DANN), designed to enhance model generalization. Traditional neural networks often suffer from suboptimal performance due to fixed parameters and lack of adaptability. DANN leverages dynamic adjustments to both the weights and connections within the network, achieved through a reinforcement learning-based optimization process. This approach allows the network to continuously adapt to the data, mitigating the limitations of static model parameters. The paper details the core mechanism, including the learning algorithm employed for weight and connection adjustment, and discusses the experimental results demonstrating the superior performance of DANN compared to state-of-the-art models. The core claim is that the dynamic self-adaptive network architecture significantly improves generalization capabilities through continuous optimization of the network's parameters.