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1. 发明授权

US5377307A System and method of global optimization using artificial neural networks 失效
标题翻译：使用人工神经网络进行全局优化的系统和方法
公开(公告)号：US5377307A
公开(公告)日：1994-12-27
申请号：US957565
申请日：1992-10-07
申请人： Josiah C. Hoskins , Glenn A. Kramer
发明人： Josiah C. Hoskins , Glenn A. Kramer
IPC分类号： B25J9/16 , G06F17/50 , G06Q10/04 , G06F15/18
CPC分类号： G06Q10/04 , B25J9/1605 , G06F17/5009 , G05B2219/39404 , G05B2219/39405 , G05B2219/39406 , G05B2219/40281
摘要： A method of global optimization of complex, highly nonlinear, multivariant systems is described. An artificial neural network (ANN) is trained to create an approximate inverse model. The desired behavior for a particular system is then input to the inverse model to derive approximate model parameters for the particular system. Optimization of the approximate model parameters yields optimal model parameters. The method is applied to the synthesis of mechanical linkages where examples of a type of linkage mechanism are used to train an ANN and derive the approximate inverse model. Inverse models for a number of linkage mechanism types are derived and stored. For a linkage mechanism with unknown linkage parameters, a power spectrum representation of the coupler curve is developed and the inverse model for the type of linkage mechanism retrieved. The representation of the desired coupler curve is input and the approximate linkage parameters derived. Optimization further refines the linkage parameters.
摘要翻译：描述了复杂，高度非线性，多变量系统的全局优化方法。训练人造神经网络（ANN）以创建近似逆模型。然后将特定系统的期望行为输入到逆模型以导出特定系统的近似模型参数。近似模型参数的优化产生最优模型参数。该方法应用于机械连杆的合成，其中使用一种连杆机构的实例来训练ANN并导出近似逆模型。导出和存储多个连接机制类型的反模型。对于具有未知链接参数的联动机制，开发耦合器曲线的功率谱表示，并检索连接机构类型的逆模型。输入所需耦合器曲线的表示，并推导出近似的连接参数。优化进一步优化了链接参数。

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