https://www.selleckchem.com/products/ag-1024-tyrphostin.html This article introduces a distributed estimator design problem for the stochastic Hamiltonian systems under fading wireless channels. The phenomenon that the channel outputs are related to the target state and the estimation of the adjacent state is considered to facilitate the implementation of distributed state estimation. Furthermore, the fixed undirected graph simplifies the analysis of the system. By resorting to fading channels and the graph theory, the main goal of the addressed problem is to design estimators to estimate the target state of the Hamiltonian system and guarantee the exponential stability in the mean-square sense of the estimation system. Based on the stochastic analysis method and the structural properties of the Hamiltonian system, sufficient conditions are obtained for the existence of the designed estimator gain for each sensor. Two examples are given to indicate the effectiveness of the theoretical claim.Information granulation and degranulation play a fundamental role in granular computing (GrC). Given a collection of information granules (referred to as reference information granules), the essence of the granulation process (encoding) is to represent each data (either numeric or granular) in terms of these reference information granules. The degranulation process (decoding) that realizes the reconstruction of original data is associated with a certain level of reconstruction error. An important issue is how to reduce the reconstruction error such that the data could be reconstructed more accurately. In this study, the granulation process is realized by involving fuzzy clustering. A novel neural network is leveraged in the consecutive degranulation process, which could help significantly reduce the reconstruction error. We show that the proposed degranulation architecture exhibits improved capabilities in reconstructing original data in comparison with other methods. A series of