SGER: New Approaches for Design of Nonlinear Discriminants

这项研究是由SGER（探索性研究小额资助）资助的。本计画的目标是加强设计、开发和实作一种电脑辅助的非线性模式识别鉴别器。该方法将采用代数几何和动态系统的技术。判别设计使用特征向量数据库，并将特征空间划分为不同类型模式对应的不相交区域。基于该方法，将开发并实现一种算法。该算法的性能将通过天气预报和生产系统中需要快速模式识别和控制的一系列测试问题进行基准测试。快速识别对象并对其采取行动，特别是在涉及自动装配和零件分类的情况下，是设计和实施有效的自动化生产系统的基本素质。该技术除了具有非线性判别特性外，还有望提高模式识别的速度和准确性。开发能够在短时间内高精度处理大量数据的快速算法是自动模式识别技术在许多应用中大规模采用的必要条件。这项研究的结果有可能推动这一关键领域的最新技术。

9527477 Prabhu This research is funded as an SGER (Small Grant for Exploratory Research). The objective of the project is to enhance the design, development, and implementation of a computer-aided nonlinear discriminant for pattern recognition. The approach will employ techniques from algebraic geometry and dynamic systems. Discriminant design uses a database of feature vectors and partitions the feature space into disjoint regions corresponding to different classes of patterns. Based on the methodology, an algorithm will be developed and implemented. The performance of the algorithm will be benchmarked through a series of tests problems in weather forecasting and production systems for which quick pattern recognition and control are required. To rapidly recognize and act on objects, especially in situations involving automatic assembly and part sortation, are essential qualities for the design and implementation of effective automated production systems. The technique, apart from its nonlinear discriminant nature, is expected to yield some improvement in both speed and accuracy in pattern recognition in general. The development of fast algorithms capable of processing large amounts of data in very short time and with high accuracy is essential for large scale adoption of automatic pattern recognition techniques in a host of applications. The outcome of this research has the potential to advance the state-of-the-art in this critical field.