基于混沌壓縮感知和深度學習網(wǎng)絡的壓縮感知新模型

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摘要：文章提出了一種基于混沌壓縮感知和深度學習網(wǎng)絡的壓縮感知新模型，稱為混沌深度壓縮感知模型。該模型將傳統(tǒng)壓縮感知中的迭代步驟轉(zhuǎn)化為深度網(wǎng)絡形式，并將相關混沌參數(shù)應用于測量矩陣生成和深度網(wǎng)絡訓練過程?；煦缟疃染W(wǎng)絡中的所有參數(shù)都將通過程序自動學習獲取，不再需要人工設計。

關鍵詞：壓縮感知；深度學習；神經(jīng)網(wǎng)絡；混沌理論

doi：10.3969/J.ISSN.1672-7274.2023.02.032

中圖分類號：TP 391.41               文獻標示碼：A               文章編碼：1672-7274（2023）02-00-03

A New Compressed Sensing Model Based on Chaotic Compressed Sensing and Deep Learning Network

CHEN Yixin， MA Zeng

（Basic Department of Naval Submarine Academy， Qingdao 266000， China）

Abstract： A new compressed sensing model based on chaotic compressed sensing and deep learning network is proposed， which is called chaotic deep compressed sensing. The iterative steps in traditional compressed sensing are transformed into deep network form， and the relevant chaotic parameters are applied to the measurement matrix generation and deep network training process. All parameters in the chaotic depth network will be acquired automatically through program learning， and no manual design is required.

Key words： compressed sensing; deep learning; neural network; chaos theory

0  引言

壓縮感知理論證明，當一個信號在某些變換域表現(xiàn)出稀疏性時，它能夠以較高的概率使用比奈奎斯特抽樣理論所確定的少得多的測量值重構(gòu)[1]。（剩余3722字）