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Twin support vector machine (TSVM) is a rapid algorithm for resolving discriminating problems using a pair of quadratic programming problems (QPPs). Based on the TSVM and SVM, this paper proposes regularization twin support vector machine with L2 loss function (L2-RTSVM) for Classification, the coordinate descent algorithm with shrinking technique is used to solve the L2-RTSVM. L2-RTSVM has higher...
In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic programming problems. The state variables of the neural network are proven to be globally stable and the output variables are proven to be convergent to optimal solutions as long as the...
In this paper we introduce the use of recurrent least square support vector machine algorithm for the adaptive control of a class of nonlinear discrete-time systems. The curse of dimensionality is avoided by using the finite time window. Advantage of the newly designed algorithm is that the computation of inverse matrix is avoided. Simulation results also verify the effectiveness of the algorithm.
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