Control of Nonstationary Systems Using Information Preserving Neural Networks

基于模式识别的控制方法可以产生稳定的 在一系列应用中表现出色， 方法可以被设计成使得相对较少的努力或 专业知识需要在一个工业的一部分， 实践者为落实。 人工神经网络 人工神经网络（ANN）非常适合模式识别应用。 人工神经网络的一个优点是它们的插值能力， 概括模式分类，因此，他们持有 Promise作为基于模式的控制器的改进框架 设计 例如，人工神经网络可以被训练来学习一个过程 使用历史工厂数据的输入-输出关系，以及 然后可以在线安装内部模型的角色 自适应控制器。 另一个例子是训练ANN 直接在过程的误差反馈关系中， 然后使用ANN作为控制器本身。 保持 准确的描述，基于人工神经网络的控制器架构必须 提供足够的自由度以确保可塑性， 从而允许以前从未经历过的系统行为 被控制器正确地处理。 研究 该项目涉及使用模式识别来识别 并代表当前工艺特征，同时满足 本款规定而 这项工作的重点是研究控制系统， (1)在设计决策上是自主的， 实现和适应，（2）提供稳定和健壮 性能在广泛的应用，（3）成功 在处理非线性、非平稳系统方面。 私家侦探 一种方法是研究一种由以下部分组成的体系结构 几个ANN，每个执行特定的功能， 将它们整合到统一的设计中。 这些功能 其中包括： 操纵输入和控制的最近历史 输出以形成当前过程的图示模型 性格，将这种绘画模式转化为 适当的算法控制器参数，用于任何 在许多流行的在线控制器算法中， 基于模式的控制器性能评价 在控制器错误的最近历史中显示，并且 在无监督的情况下使用这种性能评估 上述图像到算法翻译的训练 以提高性能并保持稳定， 非线性和非平稳应用。 控制器将 在一系列单输入单输出过程上进行测试 模拟 这些进程将显示一系列 特性，包括工艺顺序的变化， 过程增益的线性度、 主导时间常数，信噪比的大小 比例，以及缓慢和突然变化 模拟过程特性的非平稳变化。

Pattern recognition based control methods can produce stable and robust performance in a range of applications and these methods can be designed such that relatively little effort or expertise is required on the part of an industrial practitioner for implementation. Artificial neural nets (ANNs) are well suited for pattern recognition applications. A strength of ANNs are their ability to interpolate and generalize pattern classifications, consequently they hold promise as an improved framework for pattern based controller design. For example, ANNs can be trained to learn a process input - output relationship using historical plant data, and can then be installed online in the role of the internal model of an adaptive controller. Another example is training ANNs directly in the error feedback relationship of a process and then using the ANN as the controller itself. To maintain an accurate description, ANN based controller architectures must provide sufficient degrees of freedom to ensure plasticity, thus permitting system behaviors never previously experienced by the controller to be properly addressed. The research project involves the use of pattern recognition to identify and represent current process characteristics while satisfying these requirements. The focus of this work is the study of control systems that (1) are autonomous in their decision making for design, implementation and adaptation, (2) provide stable and robust performance over a wide range of applications, and (3) succeed in dealing with nonlinear, nonstationary systems. The PIs' method of approach is to study an architecture comprised of several ANNs that each perform specific functions and integrate them into a unified design. These functions include: classification of the patterns exhibited in the recent histories of the manipulated input and controlled output to form a pictorial model of the current process character, translation of this pictorial model into appropriate algorithmic controller parameters for use in any of a number of popular on-line controller algorithms, controller performance evaluation based on the patterns exhibited in the recent history of the controller error, and use of this performance evaluation in the unsupervised training of the pictorial to algorithmic translation mentioned above to improve performance and maintain stability in nonlinear and nonstationary applications. The controller will be tested on a range of single-input single-output process simulations. The processes will display a range of characteristics, including variations in process order, the degree of linearity of the process gain, the variability of the dominant time constant, the size of the signal to noise ratio, and with both slowly and suddenly changing nonstationary changes in the simulated process character.