Adaptive Self-organizing Map Network Pattern Recognition

It is very difficult to understand or analyze the large-scale data in many scientific domains, because it has the characteristic of large-scale quantities, complex features when dealing with, and it is more difficult to get knowledge from it. So it is absolutely necessarily to do science data mining(SDM) now. The main point of this project is to research the theories and applications of artificial neural network(ANN) which is suitable for large scale science data mining. Especially, our research focus include: dimension reduction techniques based on Independent Component Analysis(ICA) and wavelet-based denoising or compressing techniques for feature extraction in scientific datasets which have complex features; Classify and clustering techniques of ANN combination with data grid , Self-Growing Multilevel Self-Organizing Map for large scale knowledge founding in SDM. We propose the ANN model for special application--Molecular Dynamics numerical value simulation , especially aim at classifying ,clustering and pattern cognition for scientific dataset. Construct the utility SDM system and give a new method to mine valuable information from large-scale numerical value simulation data. This paper focuses on the self-organizing map presenting a review on the basic model. The paper puts forward the pattern recognition method that based on self-Growing multilevel self-organizing map on the foundation of traditional self-organizing map. It can overcome many limitations, which are related to the static architecture of traditional model. For example, traditional model uses a fixed network architecture in terms of number and arrangement of neural processing elements, which has to be defined prior to training, also, if it has error in the classification for the first time, its effects can not be corrected, etc. What is more, the new model can intuitive represent the hierarchical relations in the data, in particular, it can benefits the high dimension data analysis greatly. So, self-growing multilevel self-organizing map can promote the research of large-scale pattern recognition greatly.