公开(公告)号：US6047221A

公开(公告)日：2000-04-04

申请号：US943489

申请日：1997-10-03

申请人： Stephen Piche ,  James David Keeler ,  Eric Hartman ,  William D. Johnson ,  Mark Gerules ,  Kadir Liano

发明人： Stephen Piche ,  James David Keeler ,  Eric Hartman ,  William D. Johnson ,  Mark Gerules ,  Kadir Liano

IPC分类号： G05B23/02 ,  G05B13/02

CPC分类号： G05B17/02 ,  G05B13/048

摘要： A method for modeling a steady-state network in the absence of steady-state historical data. A steady-state neural network can be tied by impressing the dynamics of the system onto the input data during the training operation by first determining the dynamics in a local region of the input space, this providing a set of dynamic training data. This dynamic training data is then utilized to train a dynamic model, gain thereof then set equal to unity such that the dynamic model is now valid over the entire input space. This is a linear model, and the historical data over the entire input space is then processed through this model prior to input to the neural network during training thereof to remove the dynamic component from the data, leaving the steady-state component for the purpose of training. This provides a valid model in the presence of historical data that has a large content of dynamic behavior. A single dynamic model is required for each output variable in a multi-input multi-output steady-state model such that for each output there is a separate dynamic model required for pre-filtering. They are combined in a single network made up of multiple individual steady-state models for each output. The dynamic model can be identified utilizing a weighting factor for the gain to force the dynamic gain of the dynamic model to the steady-state gain by weighting the difference thereof during optimization of the dynamic model. The steady-state model is optimized utilizing gain constraints during the optimization procedure such that the gain of the network is prevented from exceeding the gain constraints.

摘要翻译： 在没有稳态历史数据的情况下建模稳态网络的方法。 稳态神经网络可以通过在训练操作期间通过首先确定输入空间的局部区域中的动力学来将系统的动力学压印到输入数据上，从而提供一组动态训练数据。 然后利用该动态训练数据来训练动态模型，然后将其增益设置为等于1，使动态模型现在在整个输入空间上有效。 这是一个线性模型，然后在整个输入空间中的历史数据在通过该模型输入到神经网络之前通过该模型进行处理，以在训练期间从数据中移除动态分量，将稳态分量留在目的 训练。 这在存在具有大量动态行为的历史数据的情况下提供了有效的模型。 在多输入多输出稳态模型中，每个输出变量都需要单个动态模型，因此对于每个输出，都需要一个单独的动态模型来进行预滤波。 它们组合在由每个输出的多个单独稳态模型组成的单个网络中。 可以利用增益的加权因子来识别动态模型，以通过在动态模型的优化期间加权其差异来将动态模型的动态增益强制为稳态增益。 在优化过程中利用增益约束优化稳态模型，使得网络的增益被阻止超过增益约束。

公开(公告)号：US5933345A

公开(公告)日：1999-08-03

申请号：US643464

申请日：1996-05-06

申请人： Gregory D. Martin ,  Eugene Boe ,  Stephen Piche ,  James David Keeler ,  Douglas Timmer ,  Mark Gerules ,  John P. Havener

发明人： Gregory D. Martin ,  Eugene Boe ,  Stephen Piche ,  James David Keeler ,  Douglas Timmer ,  Mark Gerules ,  John P. Havener

IPC分类号： G05B13/02 ,  G05B13/04 ,  G05B17/02 ,  G05B21/02

CPC分类号： G05B17/02 ,  G05B13/048

摘要： A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model and an independent dynamic model. The static model is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model is trained over a narrow range of data. The gain K of the static model is utilized to scale the gain k of the dynamic model. The forced dynamic portion of the model referred to as the b.sub.i variables are scaled by the ratio of the gains K and k. The b.sub.i have a direct effect on the gain of a dynamic model. This is facilitated by a coefficient modification block. Thereafter, the difference between the new value input to the static model and the prior steady-state value is utilized as an input to the dynamic model. The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.

摘要翻译： 在预测，控制和优化环境中提供独立静态和动态模型的方法利用独立的静态模型和独立的动态模型。 静态模型是一种严格的预测模型，可以在广泛的数据范围内进行训练，而动态模型则是通过窄范围的数据进行训练。 利用静态模型的增益K来缩放动态模型的增益k。 被称为bi变量的模型的强制动态部分通过增益K和k的比率来缩放。 bi对动态模型的增益有直接的影响。 这通过系数修改块来促进。 此后，将输入到静态模型的新值与先前的稳态值之间的差用作动态模型的输入。 然后将预测的动态输出与先前的稳态值相加以提供预测值Y.此外，在稳态值变化之间经过的路径。