Phương pháp phân tách nguồn mù thiếu thông tin dựa trên thuật toán tối ưu hóa Archimedes lượng tử

Springer Science and Business Media LLC - Tập 53 - Trang 13763-13800 - 2022
Hongyuan Gao1, Zhiwei Zhang1, Shihao Wang1, Helin Sun1
1College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

Tóm tắt

Hiệu suất của các phương pháp phân tách nguồn mù thiếu thông tin hiện có rất nhạy cảm với các tham số ban đầu, trong khi đó, các phương pháp thiết lập hoặc lựa chọn tham số ban đầu hiện có cần được cải thiện. Do đó, một phương pháp phân tách nguồn mù thiếu thông tin hiệu quả được đề xuất trong bài báo này để giải quyết các vấn đề kỹ thuật nêu trên. Dựa trên thuật toán tối ưu hóa Archimedes và lý thuyết tính toán lượng tử, bài báo đề xuất một thuật toán thông minh mới được gọi là thuật toán tối ưu hóa Archimedes lượng tử, giải quyết các hàm mục tiêu cho các vấn đề kỹ thuật. Sau đó, giải pháp tối ưu thu được thông qua thuật toán tối ưu hóa Archimedes lượng tử được sử dụng làm các trung tâm cụm ban đầu cho thuật toán phân cụm K-means nhằm đạt được ước lượng ma trận trộn. Thêm vào đó, việc thiết lập tín hiệu ước lượng ban đầu của quá trình phục hồi nguồn dựa trên mạng hàm cơ sở tia được chuyển đổi thành một giải pháp ban đầu trong quần thể cho thuật toán tối ưu hóa Archimedes lượng tử. Giải pháp tối ưu thu được từ thuật toán tối ưu hóa Archimedes lượng tử được sử dụng như tín hiệu ước lượng ban đầu mới để đạt được phục hồi nguồn. Kết quả mô phỏng cho thấy phương pháp phân tách nguồn mù thiếu thông tin được đề xuất có độ chính xác cao hơn so với các phương pháp trước đó. Phương pháp đề xuất, mạnh mẽ và có khả năng áp dụng hơn, giúp việc thiết lập và lựa chọn tham số ban đầu hợp lý hơn, từ đó khiến hiệu suất không còn bị giới hạn bởi các tham số ban đầu.

Từ khóa

#phân tách nguồn mù #phương pháp tối ưu hóa Archimedes #thuật toán lượng tử #ước lượng ma trận trộn #phục hồi nguồn

Tài liệu tham khảo

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