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Facial expression recognition basically requires fast processing speed as well as quality classification results. In this paper, an approach is presented for such a facial expression recognition using Locally Constrained Support Vector Clustering (LCSVC) and neural network (NN). During feature extracting, the independent component analysis can not only(More)
As a novel evolutionary computation, cuckoo search (CS) algorithm has attracted much attention and wide applications, owing to its easy implementation. CS as most population-based algorithm is good at identifying promising area of the search space, but less good at fine-tuning the approximation to the minimization. To the best of our knowledge, the(More)
Active set (AS) method suffers deteriorating performance and premature convergence when it is faced with a nonlinear programming problem (NLP) consisting of several inequality constraints. Thus, we propose an SQP/IPM algorithm that uses infeasible interior point method (IIPM) for solving quadratic programming (QP) subproblems. In this approach inequality(More)
The field of constrained nonlinear programming (NLP) has been principally challenging to various gradient based optimization techniques. The Sequential quadratic programming algorithm (SQP) that uses active set strategy in solving quadratic programming (QP) subproblems proves to be efficient in locating the points of local optima. However, its efficient(More)
The field of constrained nonlinear programming (NLP) has been principally challenging to various gradient based optimization techniques. The sequential quadratic programming algorithm (SQP) that uses active set strategy in solving quadratic programming (QP) subproblems proves to be efficient in locating the points of local optima. However, its efficient(More)
Aimed at nonlinear MIMO system, a model predictive control (MPC) strategy based on least squares support vector machine (LSSVM) and mutative scale chaos optimization is proposed. Existent method doesn’t consider the constrain of control law and results in some meaningless solution. So the proposed method utilizes mutative scale chaos optimization(More)
In this paper a hybrid adaptive genetic algorithm is proposed for solving constrained optimization problems. Genetic algorithm proposed here combines adaptive penalty method and smoothing technique in order to make the algorithm not needing parameters tuning and easily escaping from the local optimal solutions. Meanwhile, local line search technique is(More)