Research On Adaptive Self-Organizing Feature Mapping

Neural networks are the information processing systems constructed bysimulating the structure and function of the biological neural system. Itappeared at 1960s. After the development of more than 40 years, neuralnetworks have become a group of the important methods in the field of theinformation science, such as machine learning, pattern recognition and signalprocessing.This thesis firstly summarized the development experience of neuralnetworks. After three dramatic development stages, neural networks havebecome one of the focus research area in information science. Thecharacteristics, classification, learning types and learning algorithms werealso discussed.Secondly, a famous unsupervised learning neural network, SOM, wasintroduced that is proposed by Kohonen. Essentially, SOM is a featureprojection, which can be applied in revealing the nonlinear statisticalrelationship hidden in the high-dimensional data.Thirdly, this thesis proposed an improved version of SOM to enhance theadaptability, robustness and the ability of mining the dataset's clusteringtendency. The new neural network was name as Adaptive SOM (ASOM). Themajor algorithmic feature of ASOM was that all the neuron units in the outputplane decided to execute the evolutionary working mechanisms according totheir own local environments. The evolutionary mechanism includes splitting,vanishing, combining and etc. The microcosmic analysis showed that thoseevolutionary mechanisms provided a solution to the problems that faced in theconventional learning of competitive networks. We also discussed ASOMneural network from the viewpoints of multi agent system and human immunesystem, and found some interesting relationships between them.The empirical evidences showed the proposed algorithm was adaptiveand robust. This work could be viewed as an important development of classicKohonen self-organizing feature map.