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A design method of an optimal preview controller for a class of linear continuous time-varying systems is presented in this paper. By differentiating both the output error signal and the two sides of the state equation, an augmented error system is constructed, which contains the error signal, the state vector and the derivative of the state vector. Then the performance index function is introduced and the control problem is solved by optimal control theory. Thus the optimal preview control input for the original time-varying system is found. The proposed controller includes the previewable future signal, so the response speed is accelerated. Meanwhile, the integrators in the controller make the output vector track the target signal better. The numerical simulations illustrate the validity of the proposed method.

Face recognition is a promising technology with a great application potential and broad prospects for development. Compared with other identification technologies, face recognition can achieve rapid and easy sampling, without affecting the behavior of the sampled. These advantages have induced a surging demand and interests in this technology, making it a research hotspot in artificial intelligence. This paper extracts the features from the target face image by Principal Component Analysis (PCA), reducing the dimension of the image. Taking the feature coordinates of the face image for classification, it is possible to eliminate the excess computing load induced by high dimensionality. After that, the backpropagation (BP) neural network was improved by the scaled conjugate gradient (SCG) algorithm. The improvement aims to control the model error caused by the defects of the original BP neural network, including inefficient learning, slow convergence and proneness to local minimum. The improved BP neural network was then adopted to classify the feature coordinates of the face image. Finally, the proposed face recognition algorithm was implemented on Matlab and trained with the improved BP neural network. The experimental results show that the proposed algorithm achieved good recognition performance.

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