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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Improvement of generalization ability for identifying dynamical systems by using universal learning networks.
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Improvement of generalization ability for identifying dynamical systems by using universal learning networks.

机译：通过使用通用学习网络来提高识别动力学系统的泛化能力。

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摘要

This paper studies how the generalization ability of models of dynamical systems can be improved by taking advantage of the second order derivatives of the outputs with respect to the external inputs. The proposed method can be regarded as a direct implementation of the well-known regularization technique using the higher order derivatives of the Universal Learning Networks (ULNs). ULNs consist of a number of interconnected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. A generalized learning algorithm has been derived for the ULNs, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. First, the method for computing the second order derivatives of ULNs is discussed. Then, a new method for implementing the regularization term is presented. Finally, simulation studies on identification of a nonlinear dynamical system with noises are carried out to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed method can improve the generalization ability of neural networks significantly, especially in terms that (1) the robust network can be obtained even when the branches of trained ULNs are destructed, and (2) the obtained performance does not depend on the initial parameter values.
机译：本文研究如何通过利用输出相对于外部输入的二阶导数来提高动力系统模型的泛化能力。所提出的方法可以看作是使用通用学习网络（ULN）的高阶导数的众所周知的正则化技术的直接实现。 ULN由许多相互连接的节点组成，其中节点中可以具有任何连续可微的非线性函数，每对节点可以通过任意时间延迟的多个分支连接。已经为ULN导出了一种通用的学习算法，其中结合了一阶导数（梯度）和高阶导数。首先，讨论了计算ULN的二阶导数的方法。然后，提出了一种实现正则化项的新方法。最后，通过仿真研究对带有噪声的非线性动力系统进行辨识，以证明该方法的有效性。仿真结果表明，该方法可以显着提高神经网络的泛化能力，特别是在（1）即使训练有素的ULN的分支被破坏时也可以获得鲁棒的网络，以及（2）获得的性能不依赖于神经网络。初始参数值。

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