臺灣博碩士論文加值系統

類神經網路結構的建立一直是應用類神經網路的難題。我們在這篇論文中
提出一種建構類神經網路的方法，這個想法是源自於生物建構神經系統的
方式，生物神經系統的建構是由基因所控制，而這些基因所含的資訊就是
建構神經系統的規則。藉由這個想法，我們設計建構類神經網路結構的規
則，然後將這些規則編碼成基因，利用遺傳演算法以演化的方法找到最佳
的建構神經網路法則。建構類神網路法則的主要精神源自於自然界中資源
競爭的規則，在我們的假設下，神經元需要資源才能生存，而輸入資料即
被視為資源。在這種互動關係下，我們設計了建構類神經路的基本通則。
為了驗証這個想法是可行的，我們在這篇論文中以RCE(Reduced Coulomb
Energy)、Kohonen*s SOM(Self-Organizing Map)網路為實例，用建構法
則建構了這兩種不同類型的類神經網路。在RCE網路部份我們的測試應用
是IRIS分類，而SOM網路部份則是使用手寫數字。實驗結果顯示，我們提
出的方法可以有效的建構類神經網路的結構。

The construction of a proper network architecture is always a
tough challenge while we apply neural networks. In this thesis,
we propose a construction method inspired by biological nervous
systems, which is developed based on the construction rules and
information encoded in genes. According to this idea, we design
the construction rules for neural networks and encode the rules.
Then, evolve the optimal rules by using genetic algorithms.The
spirit of rule construction is mainly from the competition rules
for resource in the nature. Based on this hypothesis, neurons in
our model need resources to survive, and input data are
considered as the resources. After investigating the interaction
between the neurons and the resources, we organize the general
construction rules for neural networks.In this thesis, we prove
the usefulness of the proposed model by constructing two
different neural network models, RCE (Reduced Coulomb Energy)
and Kohonen*s SOM(Self-Organizing Map),with the application of
IRIS training data, and hand-written digit recognition.
Simulation results show that the neural network architectures
can be efficiently constructed by the proposed model.