Concerning the problem of random perturbation of controller in synchronous control of neural network with mixed delays， a non-fragile controller based on event-triggered mechanism was proposed. Firstly， a random variable obeying Bernoulli distribution was used to describe the randomness of the existence of controller gain disturbance. Secondly， the event-triggered mechanism was introduced in the synchronous control process of the neural network. Next， a novel bilateral Lyapunov function was constructed to fully consider the system status information， while the functional derivatives were scaled by an improved integral inequality to obtain sufficient conditions for the exponential stability of the synchronization error system. Finally， a non-fragile controller was designed based on the decoupling technique. The effectiveness of the proposed controller was verified by simulation examples. Experimental results show that compared with the existing exponential attenuation coefficient under the same sampling period in the four-tank system， the exponential attenuation coefficient obtained by the proposed controller is improved by 0.16.

Since the small initial variation amplitude of soft fault leads to the low detection efficiency of fault detection algorithm based on traditional neural network observer, a soft fault detection algorithm for Flapping Wing Micro Aerial Vehicle (FWMAV) based on multistep neural network observer and adaptive threshold was proposed. Firstly, a multistep prediction observer model was constructed, and the time-delay ability of it can prevent the observer from being polluted by faulty data. Secondly, the window width of the multistep observer was tested and analyzed according to the actual flight data of FWMAV. Thirdly, an adaptive threshold strategy was proposed to perform the fault detection of the observer residuals with the assistance of residual chi-square detection algorithm. Finally, the proposed algorithm was verified and analyzed with the use of actual flight data of FWMAV. Experimental results show that compared with the fault detection algorithm based on traditional neural network observer, the proposed algorithm has the soft fault detection speed increased by 737.5%, and the soft fault detection accuracy increased by 96.1%. It can be seen that the proposed algorithm can effectively improve the soft fault detection speed and accuracy of FWMAV.

A cloud model is not suitable for non-normal distribution. In order to solve the problem, a new one-dimensional backward cloud algorithm based on uniform distribution was proposed and applied to the credibility evaluation system of simulation system. Firstly, the importance of simulation credibility was expounded, and the credibility evaluation index of the evaluation results for a type of equipment concerning anti-jamming capability was established based on the actual project background. Secondly, the system was evaluated by using the evaluation method for simulation credibility based on cloud model, and the evaluation method was improved. Finally, in order to improve the evaluation method, a one-dimensional backward cloud algorithm based on uniform distribution was derived, and the experiment was designed for verifying the validity of the algorithm. The simulation experimental results show that, the average absolute error of the proposed backward cloud algorithm is less than 5% for large data, which has high applicability and provides a way of thinking for the perfection of cloud model theory. In addition, the simulation credibility evaluation results show that, the proposed method has high accuracy and contains the data information of dispersion and agglomeration, which can provides more comprehensive evaluation and the prediction of error data.