一类实时求解支持向量拟合的连续反馈神经网络模型
Continuous-time Recurrent Neural Network for Real-time Support Vector Regression
本文提出了一类通过微分方程描述的连续时间反馈神经网络模型用以实时求解支持向量拟合问题。首先,支持向量拟合被描述成一个约束二次凸规划问题,进而提出了一类用于实时求解此类问题的具有单层网络结构的连续反馈神经网络模型,此类模型可以用于支持向量机的训练。仿真结果显示了所提出的神经网络模型的良好性能和计算能力。
his paper presents a continuous-time recurrent neural network described by differential equations for real-time support vector regression (SVR). The SVR is first formulated as a convex quadratic programming problem, and then a continuous-time recurrent neural network with one-layer structure is designed for training the support vector machine. Furthermore, simulation results on an illustrative example are given to demonstrate the effectiveness and performance of the proposed neural network.
刘庆山
计算技术、计算机技术
智能系统反馈神经网络支持向量拟合二次规划收敛
Intelligent system recurrent neural networks support vector regression quadratic programming convergence.
刘庆山.一类实时求解支持向量拟合的连续反馈神经网络模型[EB/OL].(2012-09-29)[2025-08-21].http://www.paper.edu.cn/releasepaper/content/201209-341.点此复制
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