The book "Neural Network-Based State Estimation of Nonlinear Systems" offers a comprehensive analysis and innovative approaches to state estimation, system identification, as well as fault detection and isolation in nonlinear systems. It presents efficient and easily implementable neural network methods that are supported by mathematical stability proofs. The authors provide experimental evaluations that demonstrate the robustness of the methods against unmodeled dynamics, external disturbances, and measurement noise. This specialized book is aimed at professionals and students engaged in modern techniques of system analysis and monitoring. It serves as a valuable reference for anyone working in the fields of control engineering, automation, and system theory.