Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Các Thuật Toán Học Tập Hợp Tác Phân Tán Theo Sự Kiện Trên Mạng Thông Qua Phương Pháp Xấp Xỉ Wavelet
Tóm tắt
Bài báo này nghiên cứu vấn đề học tập hợp tác phân tán theo sự kiện (DCL) qua các mạng dựa trên lý thuyết xấp xỉ wavelet, trong đó mỗi nút chỉ có quyền truy cập vào dữ liệu cục bộ do cùng một mẫu (bản đồ hoặc hàm) chưa biết sinh ra. Tất cả các nút hợp tác học mẫu chưa biết này bằng cách trao đổi thông tin đã học với các nút lân cận của mình theo chiến lược dựa trên sự kiện nhằm loại bỏ các giao tiếp không cần thiết, nhằm tránh lãng phí tài nguyên mạng. Đối với vấn đề trên, hai thuật toán DCL theo sự kiện mới lạ trong thời gian liên tục và rời rạc được đề xuất để xấp xỉ mẫu chưa biết bằng cách sử dụng hàm cơ sở wavelet. Các thuật toán DCL theo sự kiện được đề xuất được sử dụng để đào tạo ma trận hệ số trọng số tối ưu của chuỗi wavelet. Hơn nữa, sự hội tụ của các thuật toán được đề xuất được trình bày bằng phương pháp Lyapunov, và hành vi Zeno cũng bị loại trừ bởi khoảng thời gian lấy mẫu dương. Các ví dụ minh họa được đưa ra để chỉ ra hiệu quả và sự hội tụ của các thuật toán được đề xuất.
Từ khóa
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