用分維神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)短期記憶
FRACTAL NEURAL NETWORKS FOR SHORT TERM MEMORY
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摘要: 神經(jīng)網(wǎng)絡(luò)中的突觸連接矩陣儲(chǔ)存有限的信息。當(dāng)神經(jīng)網(wǎng)絡(luò)在線使用時(shí),基于Hebb規(guī)則的網(wǎng)絡(luò)模型將不再適用。本文提出用分維神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)短期記憶,要求舊的模式隨時(shí)間而遺忘,最近存儲(chǔ)的模式可以可靠記憶。實(shí)驗(yàn)結(jié)果表明,該網(wǎng)絡(luò)模型適用于在線工作,且優(yōu)于對(duì)比方法。
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關(guān)鍵詞:
- 分維神經(jīng)網(wǎng)絡(luò); 短期記憶; 突觸連接矩陣
Abstract: Synaptic connection matrix encodes limited information. It is well known that neural network memory with storage prescriptions based on Hebb s rule will collapse as more patterns are stored. By requiring that old patterns are automatically forgotten and the memory recalls only the most recent ones, a new short-term neural network memory based on Y.Baram s fractal neural network is proposed. Comparison is made with Morris and Wong's method and the experimental results are shown to be rather satisfactory and encouraging. -
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