演化式類神經網路結構發展與最佳化

Abstract

類神經網路和遺傳演算法是兩種很有名的智慧型計算模型。在相當多的 研究領域中它們都已經證實了它們的能力。在本文中, 我們結合這兩項技 術來解決圖形辨識問題。生物學上, 細菌會改變它們的基因表現來節省能 量。根據這項觀察, 我們結合一種新的方法叫條件式基因和遺傳演算法來 達成類神經網路架構的自我組織。我們應用這個方法來解決數字辨識問題 。 Kohonen 的自我組織特徵 圖可以拓樸的改變它的輸出節點。我們利用這個特性來動態的調整類神經 網路的形狀並且發展一個新的辨識方 法。在和條件式基因結合後, 類神 經網路可以成功的辨識手寫數字。 Neural Networks and Genetic Algorithms are two kind of famous Computational Intelligence Models. They have shown their ability in many research domains. In this thesis, we combine these two technologies to solve pattern recognition problems. In biology, bacteria can change their expression of genes in order to save energy. Based on this observation, we combine a new approach called conditional genes with genetic algorithmsto achieve neural network structure self-organization. We applythis method to solve numerals recognition. Kohonen's Self-Organizing Feature Maps can adapt the output node topologically. We take advantages of this characteristic to adapt the structure of neural networks dynamically and a newrecognizing method is developed. After combining with conditional genes, neural networks can successfully recognize hand-written numerals.