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Interdependent multiobjective control using biased neural network (Biased NN)

机译:偏置神经网络(偏置NN)的相互依存多目标控制

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A Biased Neural Network (Biased-NN) is proposed to solve an interdependent multiobjective control problem. The main idea of the Biased-NN stems from a decoupled fuzzy sliding mode control scheme that provides the simple way to achieve asymptotic stability for a class of decoupled systems. Each neuron in the Biased-NN is used to approximate a sign function in order to replace the sliding mode control structure with the Biased-NN. Such a feature is useful for handling the interdependent multiobjective control problem based upon the proposed supporting strategy. While the previous works require a priori knowledge for all the objectives, the proposed method uses only expert knowledge of the objective that is considered main concern. Simulations are conducted to show the effectiveness of the Biased-NN.
机译:提出了偏置神经网络(偏置NN)以解决相互依存的多目标控制问题。偏置-NN的主要思想源于去耦模糊滑模控制方案,该控制方案提供了实现一类解耦系统实现渐近稳定性的简单方法。偏置-NN中的每个神经元用于近似符号功能,以便用偏置-NN替换滑动模式控制结构。这种特征对于基于所提出的支持策略来处理相互依赖的多目标控制问题。虽然以前的作品需要对所有目标的先验知识,但所提出的方法仅使用对认为主要关注的目标的专业知识。进行仿真以显示偏置-NN的有效性。

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