RBFNN-Enabled Distributed Estimation Over Complex Networks in Intricate Environments

When complex networks are used for distributed estimation, spatial anisotropy, particularly the potential heterogeneity in noise distributions across multiple nodes, poses significant challenges in practice. In such intricate environments, while state-of-the-art algorithms may excel at specialized nodes, their inability to maintain consistent performance over the entire network often results in systemic degradation. To address this fundamental limitation, an innovative framework is proposed in this letter, wherein the radial basis function neural networks (RBFNNs) are uniformly deployed at each node, serving as universal approximators for various noise profiles. Leveraging this framework, a recursive-type diffusion algorithm is introduced to achieve enhanced network-wide performance. Owing to RBFNN’s remarkable modeling capacity, the proposed algorithm can sustain consistent and superior performance in intricate network environments. Simulations undoubtedly validate its superiority over advanced competing alternatives.