基于軌道空間壓縮的混沌神經(jīng)網(wǎng)絡(luò)控制
Controlling Chaos in a Neural Network Based on the Orbit Space Compression
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摘要: 該文提出了基于軌道空間壓縮的混沌神經(jīng)網(wǎng)絡(luò)控制方法，利用該方法對(duì)混沌神經(jīng)網(wǎng)絡(luò)進(jìn)行控 制，使神經(jīng)網(wǎng)絡(luò)的輸出穩(wěn)定地收斂于與網(wǎng)絡(luò)起始模式有最小漢明距離的存儲(chǔ)模式或其反相模式上。該控制方法簡單易行，物理意義明確。
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關(guān)鍵詞:
- 混沌控制; 混沌神經(jīng)網(wǎng)絡(luò); 軌道空間壓縮
Abstract: In this paper, a controlling chaos method of the orbit space compression is proposed for a Chaotic Neural Network(CNN). The computer simulation of the chaotic behaviors of the CNN proves that each pattern can be controlled using the orbit space compression. Starting from any initial state the CNN can converge in a stored pattern or its inverse pattern, which has the smallest Hamming distance with the initial state. The controlling method of the orbit space compression shows clear physical meaning and can be easily carried out. -
曹志彤.混沌神經(jīng)網(wǎng)絡(luò)的動(dòng)態(tài)聯(lián)想記憶.浙江大學(xué)學(xué)報(bào)(自然科學(xué)版),1998,增刊:330-335.[2]Aihara K, Takabe T, Toyoda M. Chaotic neural networks[J].Phys. Lett.1990, 144(6,7):333-340[3]Adachi M, Aihara K. Associative dynamics in a chaotic neural network[J].Neural Networks.1997,10(1):83-98[4]Kobori E R, Ikoda K, Nakayama K. A model of dynamic associative memory. IEEE International Conference on Neural Networks Conference Proceedings, Washington, DC, USA, 1996, 2: 804-809.[5]何國光,曹志彤.混沌神經(jīng)網(wǎng)絡(luò)的信息搜素.物理學(xué)報(bào),2001,50(11):2103-2107.[6]Ott E, Grebogi C, Yorke J A. Controlling chaos[J].Phys. Rev. Lett.1990, 64:1196-1199[7]羅曉曙.利用相空間壓縮實(shí)現(xiàn)混沌與超混沌控制.物理學(xué)報(bào),1999,48(3):402-407. -
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