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Estimation of Inverse Model by PSO and Simultaneous Perturbation Method

机译：PSO同时扰动法估计逆模型

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This paper describes estimation of the inverse model by multi-layered neural network. The back-propagation rule requires a sensitivity function of a system. If the system has uncertainly, then we can not calculate the sensitivity function. Hence, we propose a learning rule based on particle swarm optimization (PSO) combining with simultaneous perturbation. PSO and simultaneous perturbation are suitable for estimation of the inverse model with uncertainly, because they can update by only value of the objective function. PSO has a capability of finding a global minimum and simultaneous perturbation can search local area efficiently. We introduce two adaptation method of the combination ratio. One of them is to adapt it depending on the distance from gbest. The other is to adapt it depending on the value of the objective function. The proposed method are investigated using inverse kinematics problem. The simulation results show that the proposed methods obtain the more accurate inverse model.
机译：本文介绍了多层神经网络逆模型的估计。背传播规则需要系统的灵敏度函数。如果系统不确定，那么我们无法计算灵敏度函数。因此，我们提出了一种基于粒子群优化（PSO）与同时扰动组合的学习规则。 PSO和同时扰动适用于不确定地估计逆模型，因为它们只能通过目标函数的值更新。 PSO具有查找全局最小和同时扰动的能力可以有效地搜索局域。我们介绍了两个组合比的适应方法。其中一个是根据与Gbest的距离来调整它。另一个是根据目标函数的值来调整它。使用逆运动学问题研究了所提出的方法。仿真结果表明，所提出的方法获得更准确的逆模型。

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