| Summary: | 碩士 === 中原大學 === 資訊工程學系 === 85 === Since the concept of neural network was introduced, there are many neural network models developed and used broadly in different research areas. In 1973, Kohonen proposes a new generation of neural network models, called Self-Organizing Map (SOM). The SOM algorithm adds one kinds of fixed neighborhood relations among neurons into regular neural nets and learns new patterns under such neighborhood constraints.
Although SOM has been proven to be effective in many applications, the fixedness of its neighborhood relations bring many inconveniences. This dissertation analyzes SOM from the viewpoint of graph theory and proposes a couple of dynamic SOM algorithms: “Evolutionary Self- Organizing Map algorithm ”and “Mate Self-Organizing Map algorithm”.
In “Evolutionary Self-Organizing Map (EXOM) algorithm”, the weight functions of neurons and the neighborhood relations are combined as a neighborhood graph. As the training data are provided, this neighborhood graph can be dynamically evolved accordingly. Thus the initial state of neurons is not so critical and the final structure of this neural net will be adjusted to fit the training data automatically.
Based on genetic algorithm, “Mate Self-Organizing Map (MSOM)”is proposed as another choice of SOM evolution strategies. MSOM mates two different neural nets according to their genes and breeds two children neural nets with different gene structures from their parents'''' . As genetic algorithm, such children neural nets of MSOM have chance to escape from local minimum (local-minimum -free property).
From biology viewpoint, ESOM is one kind of asexual reproduction as well as MSOM is a sexual reproduction. With these two evolution strategies we can make SOM grow as biological population. Such flexible neural net structure not only patches the weakness of traditional SOM algorithm, but also makes possible some applications with uncertainty environment. For example, both evolution algorithms can be used in agent models to let agents with different knowledge or properties evolve and mate to produce their nest generations in an multiple agent architecture.
|
|---|
