一種序貫學(xué)習(xí)神經(jīng)網(wǎng)絡(luò)及其應(yīng)用
A SEQUENTIAL LEARNING ALGORITHM OF NEURAL NETWORK AND ITS APPLICATION
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摘要: 本文提出了一種序貫學(xué)習(xí)神經(jīng)網(wǎng)絡(luò),它主要由有界權(quán)值調(diào)整規(guī)則和網(wǎng)絡(luò)結(jié)構(gòu)自適應(yīng)調(diào)整規(guī)則所組成。該網(wǎng)絡(luò)具有在保持舊知識(shí)的前提下有效地序貫學(xué)習(xí)新輸入樣本知識(shí)的優(yōu)點(diǎn)。文中給出了這種網(wǎng)絡(luò)的一種序貫學(xué)習(xí)算法,詳細(xì)分析了其學(xué)習(xí)特性和識(shí)別性能。大量的理論分析和實(shí)驗(yàn)都證明了網(wǎng)絡(luò)的有效性。
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關(guān)鍵詞:
- 序貫神經(jīng)網(wǎng)絡(luò); 學(xué)習(xí)算法; 模式分類
Abstract: In this paper we propose a sequential learning neural net which consists of the bounded weight adjustment algorithm and structure adaptive adjustment method. This network is characterized of efficiently learning the knowledge of new samples in series. After we present an efficient sequential learning algorithm of this network, we analyze its learning feature and recognition performance in detail. The effectiveness of this network have also been shown by theoretical analyses and a lot of experiments in this paper. -
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