In this paper, a neuro‐fuzzy observer (NFO) is proposed for estimating the unmeasured states of an information‐poor system by relaxing the strictly positive real condition (without using filtered fuzzy basis function (FBF) and filtered output estimation error) and without using high‐gain terms. To recover the performance of the observer in the absence of high‐gain terms, a concept of weighted fuzzy rules (or strengthened FBF) is proposed. The weighted fuzzy rules are then used to propose the concept of weighted function approximation which is then utilized in the design of NFO to estimate the unknown dynamical function. For reducing computational power and avoiding over‐tuning of the weights, a concept of relay‐switching is also introduced in the design of the NFO. The stability analysis of the proposed NFO is also presented using Lyapunov approach and the effectiveness of the proposed scheme is demonstrated through a simulation example.

中文翻译：

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基于规则的强度不足的信息模糊系统神经模糊观察者

本文提出了一种神经模糊观测器（NFO），通过放宽严格的正实条件（不使用滤波的模糊基函数（FBF）和滤波的输出估计误差）来估计信息贫乏系统的未测状态。使用高收益条款。为了在没有高增益项的情况下恢复观察者的性能，提出了加权模糊规则（或增强的FBF）的概念。然后将加权模糊规则用于提出加权函数逼近的概念，然后将其用于NFO的设计中以估计未知动态函数。为了降低计算能力并避免权重过度调整，NFO的设计中还引入了继电器切换的概念。