Intelligent control of nonlinear systems based upon case-based reasoning

提出了一种基于案例推理的智能控制与信号处理方法。利用小波变换提取非线性对象的I/O信号特征，并利用多分辨模式分析方法区分I/O信号的全局和局部特征。采用分层图像编码技术，对提取的特征进行无损压缩，并设计了有效的存储过程。一个非线性的自适应估计方法的基础上的n-最近邻的方法来估计输出的查询从I/O数据到目前为止。观察到的I/O数据存储在一个数据库中的形式的k-d trie和非线性局部模型来回答每个查询的正则化理论的基础上。一个有效的时间更新计划的数据库内容，以遵循非平稳数据。一个存储过程，允许一个简单而有效的更新，减少处理时间和存储要求。仿真数据和真实的语音信号均表明了该方法的有效性。此外，自动着陆系统的基础上开发的人的技能模型。该模型表示为一个非线性的I/O映射从飞机状态的控制命令提供的人类专家。所有由人类专家提供的I/O数据被先验地存储在数据库中，并且建立响应当前飞机状态的局部模型以产生接近人类操作的控制输入。为了提高鲁棒性，引入了增益自适应技术。

An approach to intelligent control and signal processing based upon case-based reasoning is presented. The wavelet transform is used to extract I/O characteristics of nonlinear plant, and the multiresolution pattern analysis method is applied to distinguish global and local features of the I/O signals. Using a hierarchical image coding technique, the extracted feasters are compressed without any degradation and their efficient storage procedure is designed. A nonlinear adaptive estimation method based on the n-nearest neighbor approach is derived to estimate the output for a query from the I/O data seen so far. Observed I/O data are stored in a database in the form of a k-d trie and a nonlinear local model to answer each query is derived based upon regularization theory. An efficient time-updating scheme of the database contents to follow nonstationary data is derived. A storage procedure allowing a simple and efficient update is developed for reduction in processing time and storage requirement. The effectiveness of the proposed method is demonstrated with both simulation data and real speech signals. Furthermore, an automatic landing system is developed on the basis of a human skill model. The model is expressed as a nonlinear I/O mapping from the aircraft state to the control command provided by a human expert. All the I/O data provided by the human expert are stored in a database a priori, and a local model to answer the current aircraft state is built to generate a control input close to the human operation. A gain adaptation technique is introduced for improving the robustness.