Classification Mapping and Its Application on Chemical Systems

Abstract

In this paper, the conception and the construction of classification maps (CMs) are discussed. Methods called Fp mapping and Sp mapping are proposed based on discriminant analysis (DA) and projection pursuit (PP). By these methods, an experimental data set with classification information can be mapped from multidimensional data space onto a series two-dimensional projection keeping the original classification structure in the process. These projections show intuitively the semiquantitative relationships between the study object and its controlling factors that we cannot observe directly from multivariate data space. Based on existing experimental data, these methods are useful in giving valuable suggestions on further experimental works. As an example, they were applied to the study of a kind of propellant material and a kind of welding material and were proven effective in the optimization of the material's performance.

Optical Nano-Tomography on Photosensitive Single-Wall Carbon Nanotube Arrays in Zeolite Crystals

“…(1) The object being researched has too many controlling factors and is too complex to analyse; (2) the object is controlled by some unexplainable mechanisms and difficult to analyse; (3) the mathematical expression has strong nonlinearity and it is difficult to resolve strictly. The method and correlation techniques have been proved effective in several fields of application, including composition optimization of a composite material and welding material [11], alloy steel design and optimization [12], chrome plating process optimization [13], and material development for rocket use [14].…”

“…, x N , and the linear discriminant function [11] of the data set with two classes + , − is known as…”

“…Boundary conditions (equation (11)) are considered in the encoding process of a genetic algorithm. An eight-bit code is used here to cover the numeric area of a variable x j .…”

“…Systematic methods and new algorithms have been discussed in this paper to actualize the automodelling and auto-optimizing process for material research. The method and correlation techniques have been proved effective in several application fields [11][12][13][14].…”

A systemic self-modelling method and its application to material design and optimization

“…. , x N , and the spheroidal discriminant function [11] of the data set with two classes + , − is known as…”