论文标题
部分可观测时空混沌系统的无模型预测
Feature selection intelligent algorithm with mutual information and steepest ascent strategy
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Remote sensing is a higher technology to produce knowledge for data mining applications. In principle hyperspectral images (HSIs) is a remote sensing tool that provides precise classification of regions. The HSI contains more than a hundred of images of the ground truth (GT) map. Some images are carrying relevant information, but others describe redundant information, or they are affected by atmospheric noise. The aim is to reduce dimensionality of HSI. Many studies use mutual information (MI) or normalised forms of MI to select appropriate bands. In this paper we design an algorithm based also on MI, and we combine MI with steepest ascent algorithm, to improve a symmetric uncertainty coefficient-based strategy to select relevant bands for classification of HSI. This algorithm is a feature selection tool and a wrapper strategy. We perform our study on HSI AVIRIS 92AV3C. This is an artificial intelligent system to control redundancy; we had to clear the difference of the result's algorithm and the human decision, and this can be viewed as case study which human decision is perhaps different to an intelligent algorithm. Index Terms - Hyperspectral images, Classification, Fea-ture selection, Mutual Information, Redundancy, Steepest Ascent. Artificial Intelligence
