## 1 Introduction
In this paper a genetic algorithm(GA) was applied to estimate the state vector of a possibly nonlinear system.They show how to construct the gain matrix of such an observer using GA real-time evolution observer to minimize the output error. Apart from the relatively simple design procedure, the authors did not provide the convergence conditions of the observer and did not consider robustness issues with respect to model uncertainty. The convergence condition of the observer was proposed by [PERSON] et al.[16]. In particular, the authors showed a technique for increasing its convergence rate with genetic programming. In Reference [17], the authors presented a robust observer design method, by which the optimal selection of observer operating points can be realized in the presence of measurement noise and inaccurate knowledge of mathematical model parameters. In Reference [18], a multiple model adaptive nonlinear observer was proposed and all observer parameters are optimized using particle swarm optimization. The research shows that the intelligent optimization algorithm has excellent performance for solving multivariable systems. But the above researches mainly focus on the optimization of the gain matrix of the traditional adaptive observer. In Reference [19], the authors utilized the GA searching ability to determine unobtainable state variables in the control system and constructed an adaptive GA observer to estimate a parameter. However, this study does not involve robust estimation of multi-dimensional time-varying parameters. In Reference [20], a neural network-based adaptive observer is designed to estimate the states of de-icing robot, but the initial state deviation is not considered  
When the input and output of the system are known, the parameters and states to be identified can be considered as solving the inverse optimization problem. In general, the optimization task is complicated by the existence of non-linear objective functions with multiple local minima [21]. For a dynamic nonlinear system, accurate estimation of multidimensional states and parameters is a complex optimization problem. The DE (Differential evolutionary) algorithm emerged as a very competitive form of evolutionary computing more than a decade ago [21]. It is a calculation strategy that use mechanisms inspired by biological evolution, such as reproduction, mutation and recombination. Therefore, DE has some intelligent characteristics, including self-organizing, self-adaptive and self-learning features. It is often used to tackle complex optimization problems. In this paper, a class of nonlinear systems with uncertain time-varying parameters and uncertain initial state are studied. Differential evolutionary algorithms (DE) is introduced for dynamic state and parameter estimation. The results obtained are applied to a three tanks systems, and simulation results are presented to demonstrate the effectiveness and feasibility of the developed results. The main contribution includes--the unmeasurable parameters and states within the nonlinear system can be estimated by an novel adaptive observer by creating an augmented states vector \([x\ p]^{\mathsf{T}}\). A novel adaptive observer based on intelligent optimization algorithm is built. Parameter estimation and state tracking are considered as an optimal problem and the fitness function is constructed by maximizing the correlation coefficient or minimizing the Euclide distance between the real output and the estimated output. Through iterative optimization of DE algorithm, the initial value of state and parameters of the dynamic system can be estimated correctly, so as to reconstruct the output trajectory of the system and the unknown states. In order to avoid the accumulation of estimation errors, an initial condition correction is carried out for each time window, so the observer has a certain robustness. This paper is organized as follows--the considered problem is formulated in Section 2.