Contents I. Introduction
ANNs have gained significant attention in recent years due to their remarkable performance in nonlinear function approximation and self-learning. ANNs have been widely applied in various fields such as signal processing [14], [29], [43], crack detection [8], [42], [51], and pattern recognition [16], [17]. In certain applications involving ANNs, there is a necessity to accurately ascertain the state of key neurons. However, acquiring such precise information directly is frequently challenging, where the difficulty stems primarily from the large scale of ANNs and the limited availability of observational resources. As a result, one often has access only to partial state information through sensor observations. Recently, research efforts have been focused on state estimation for ANNs, where the goal is to accurately estimate the unavailable state information by utilizing measurements from deployed sensors [3], [9], [27], [39], [40].
