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| 論文名稱 | Constrained-storage variable-branch neural tree for classification |
| 發表日期 | 2018-01-05 |
| 論文收錄分類 | SCI |
| 所有作者 | 楊雄斌 |
| 作者順序 | 第一作者 |
| 通訊作者 | 是 |
| 刊物名稱 | Neural Computing and Applications |
| 發表卷數 | |
| 是否具有審稿制度 | 是 |
| 發表期數 | |
| 期刊或學報出版地國別/地區 | NATUSA-美國 |
| 發表年份 | 2011 |
| 發表月份 | 12 |
| 發表形式 | 電子期刊 |
| 所屬計劃案 | 無 |
| 可公開文檔 |
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| 可公開文檔 |
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| 可公開文檔 | |
| 附件 |
NCA_revisedII.pdf
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[摘要] :
在這項研究中，提出了用於模式分類的約束存儲可變分支神經樹（CSVBNT）。 在CSVBNT中，每個內部節點被設計為用於分類輸入樣本的單層神經網絡（SLNN）。 提出了遺傳算法（GA）來尋找SLNN輸出層中適當數量的輸出節點。 此外，由於存儲約束，提出了增長方法來確定哪個節點具有在CSVBNT中分割的最高優先級。 增長方法根據CSVBNT的分類錯誤率和計算複雜度選擇一個節點在CSVBNT中分割。 在實驗中，當計算時間相同時，CSVBNT的分類錯誤率低於其他NT。
關鍵詞：神經樹，神經網絡，遺傳算法。
[英文摘要] :
In this study, the constrained-storage variable-branch neural tree (CSVBNT) is proposed for pattern classification. In the CSVBNT, each internal node is designed as a single layer neural network (SLNN) that is used to classify the input samples. The genetic algorithm (GA) is proposed to search for the proper number of output nodes in the output layer of the SLNN. Furthermore, the growing method is proposed to determine which node has the highest priority to split in the CSVBNT because of storage constraint. The growing method selects a node to split in the CSVBNT according to the classification error rate and computing complexity of the CSVBNT. In the experiments, CSVBNT has lower classification error rate than other NTs when they have the same computing time.
Key words: Neural trees, neural network, genetic algorithm.
[參考文獻] :
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