Wireless sensor network (WSN) is an important part of the Internet of Things (IoT) and has emerged in various new forms, such as smart home, smart city, and intelligent manufacturing system. Due to its high reliability, distributed estimation over nonlinear WSNs is one of the most active fields in recent years. In this paper, a novel distributed functional link least mean square (DFLMS) algorithm based on rblackthe diffusion strategy is proposed, in which the diffusion functional link network (DFLN) is used to model the nonlinear dynamic behavior of the distributed system. In particular, by using different orthogonal polynomials, we develop four types of DFLNs, i.e., trigonometric DFLN (TDFLN), Legendre DFLN (LDFLN), Chebyshev DFLN (CDFLN), and Hermite DFLN (HDFLN). However, the censored measurement caused by the range of sensors brings great challenges to the traditional distributed nonlinear estimation. To tackle this problem, a censored regression-distributed functional link adaptive filtering (CR-DFLAF) algorithm is further proposed. Compared with the DFLMS algorithm, the CR-DFLAF algorithm can compensate the estimated bias in the CR scenario at the price of slightly increased computational complexity. Simulations involving two distributed nonlinear networks verify the effectiveness of the proposed algorithms.

无线传感器网络（WSN）是物联网（IoT）的重要组成部分，以智能家居、智慧城市、智能制造系统等多种新形态出现。由于其高可靠性，非线性 WSN 上的分布式估计是近年来最活跃的领域之一。本文提出了一种新的基于rblackthe扩散策略的分布式泛函链接最小均方(DFLMS)算法，该算法利用扩散泛函链接网络(DFLN)对分布式系统的非线性动态行为进行建模。特别是，通过使用不同的正交多项式，我们开发了四种类型的DFLN，即三角DFLN（TDFLN）、Legendre DFLN（LDFLN）、Chebyshev DFLN（CDFLN）和Hermite DFLN（HDFLN）。然而，传感器范围引起的删失测量给传统的分布式非线性估计带来了巨大的挑战。为了解决这个问题，进一步提出了一种删失回归分布式函数链接自适应滤波（CR-DFLAF）算法。与 DFLMS 算法相比，CR-DFLAF 算法可以补偿 CR 场景中的估计偏差，但代价是计算复杂度略有增加。涉及两个分布式非线性网络的仿真验证了所提出算法的有效性。CR-DFLAF 算法可以以稍微增加计算复杂度为代价来补偿 CR 场景中的估计偏差。涉及两个分布式非线性网络的仿真验证了所提出算法的有效性。CR-DFLAF 算法可以以稍微增加计算复杂度为代价来补偿 CR 场景中的估计偏差。涉及两个分布式非线性网络的仿真验证了所提出算法的有效性。