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Khánh kiệt tài chính và ứng dụng và mô hình Z-Score trong dự báo khánh kiệt tài chính

Hệ số đối xứng của giản đồ Feynman và ứng dụng vào mô hình 3-3-1 tiết kiệm

Hệ số đối xứng của giản đồ Feynman ứng dụng vào hình 3-3-1 tiết kiệm

... của hình 3-3-1,việc phân chia này phụ thuộc vào phần lepton được đưa vào trong hình. Phiên bản thứ nhất, gọi là hình 3-3-1 tối thiểu, được đề xuấtbởi Pisano, Pleitez Frampton vào ... lượng giữa thang yếu thang Planck, giải thích sự không đối xứng của vật chất phảnvật chất trong vũ trụ là bằng chứng tin cậy cho thấy, hình chuẩn(dựa trên nhóm đối xứng chuẩn SU(3)CSU(2)LU(1)Y) ... viện hàn lâmkhoa học công nghệ vnviện vật lýHÀ THANH HÙNGhệ số đối xứng của giản đồ feynman ứng dụng vào hình 3-3-1 tiết kiệmChuyên ngành: Vật lý lý thuyết vật lý toánMã nghành:...
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Nghiên cứu ứng dụng mạng nơron nhân tạo trong dự báo phụ tải ngắn hạn cho thành phố đà nẵng

Nghiên cứu ứng dụng mạng nơron nhân tạo trong dự báo phụ tải ngắn hạn cho thành phố đà nẵng

... của TP ĐN trong 4 năm, kiểm tra tính chính xác ứng dụng vào thực tế. 3. ĐỐI TƯỢNG VÀ PHẠM VI NGHIÊN CỨU 3.1. Đối tượng nghiên cứu của ñề tài - Các hình phương pháp dự báo. - Các ... các khách hàng thương mại công nghiệp. 1.4 MỘT SỐ MÔ HÌNH VÀ PHƯƠNG PHÁP DỰ BÁO THÔNG DỤNG 1.4.1 Các hình xác suất (Mô hình nhân) Dạng chung của loại hình phụ tải ñiện này có thể ... trường. Các hình dự báo khác nhau ñã ñược dùng trong các hệ thống ñiện ñể ñạt ñược ñộ chính xác dự báo. Nằm trong số các hình là các phương pháp hồi quy, phương pháp thống kê phương...
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Luận văn:Nghiên cứu ứng dụng mạng nơron nhân tạo trong dự báo phụ tải ngắn hạn cho thành phố Đà Nẵng doc

Luận văn:Nghiên cứu ứng dụng mạng nơron nhân tạo trong dự báo phụ tải ngắn hạn cho thành phố Đà Nẵng doc

... của TP ĐN trong 4 năm, kiểm tra tính chính xác ứng dụng vào thực tế. 3. ĐỐI TƯỢNG VÀ PHẠM VI NGHIÊN CỨU 3.1. Đối tượng nghiên cứu của ñề tài - Các hình phương pháp dự báo. - Các ... các khách hàng thương mại công nghiệp. 1.4 MỘT SỐ MÔ HÌNH VÀ PHƯƠNG PHÁP DỰ BÁO THÔNG DỤNG 1.4.1 Các hình xác suất (Mô hình nhân) Dạng chung của loại hình phụ tải ñiện này có thể ... trường. Các hình dự báo khác nhau ñã ñược dùng trong các hệ thống ñiện ñể ñạt ñược ñộ chính xác dự báo. Nằm trong số các hình là các phương pháp hồi quy, phương pháp thống kê phương...
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Neuron Network - Ứng dụng mạng neuron truyền thẳng trong dự báo dữ liệu

Neuron Network - Ứng dụng mạng neuron truyền thẳng trong dự báo dữ liệu

... Minsky-Papert. -6- 1.1.2. Ứng dụng Trong quá trình phát triển, mạng nơron đã được ứng dụng thành công trong rất nhiều lĩnh vực. Dưới đây liệt kê ra một số ứng dụng chính của mạng nơron: 9 Aerospace: ... đích tìm hiểu làm sáng tỏ một số khía cạnh về mạng nơron truyền thẳng nhiều lớp, thuật toán lan truyền ngược ứng dụng chúng trong giải quyết các bài toán trong lĩnh vực dự báo dữ liệu. ... đầu vào đầu ra, chúng được coi như là các “bộ xấp xỉ đa năng”. Việc ứng dụng của loại mạng này chủ yếu là cho việc phân tích, dự báo, phân loại các số liệu thực tế. Đặc biệt đối với việc dự báo...
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BÁO CÁO MÔN KINH TẾ LƯỢNG ỨNG DỤNG MÔ HÌNH ARIMA TRONG DỰ BÁO GIÁ DẦU THÔ THẾ GIỚI

BÁO CÁO MÔN KINH TẾ LƯỢNG ỨNG DỤNG HÌNH ARIMA TRONG DỰ BÁO GIÁ DẦU THÔ THẾ GIỚI

... BÁO CÁO MÔN KINH TẾ LƯỢNG ỨNG DỤNG MÔ HÌNH ARIMA TRONG DỰ BÁO GIÁ DẦU THÔ THẾ GIỚI Sinh viên thực hiện: Đặng Thị Thu HiềnMã sinh viên: CQ500927 Giới thiệu về hình Arima Trong ... nắm bắt tình hình biến động đưa ra các chính sách phù hợp. Trong khuôn khổ đề tài, chúng tôi đề xuất sử dụng hình ARIMA phương pháp Box-jenkins để dự báo giá dầu thô thế giới trong ngắn ... nó trong công tác dự báo. Để trả lời câu hỏi này có nhiều phương pháp khác nhau trong đó có phương pháp sử dụng hình Arima để nghiên cứu cho kết quả khá tốt.Arima là hình trung bình trượt...
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Bài giảng sử dụng mô hình arima trong dự báo chuỗi thời gian  - cao hào thi

Bài giảng sử dụng hình arima trong dự báo chuỗi thời gian - cao hào thi

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Ử 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Kết Hợp Trung Bình Trượt Ứng dụng dự báo giá cá sông tại Tp. HCM 3GIỚI THIỆU GIỚI THIỆU  hình nhân quả hình chuỗi thời gian Hai loại hình dự báo chính: 4 Đối với các chuỗi ... được sử dụng để dự báo  Theo hình ARIMA, giá trị dự báo sẽ phụ thuộc vào các giá trị quá khứ tổng có trọng số các nhiễu ngẫu nhiên hiện hành các nhiễu ngẫu nhiên có độ trễ5MÔ HÌNH...
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392 Khánh kiệt tài chính và ứng dụng mô hình Z-Score trong dự báo khánh kiệt tài chính

392 Khánh kiệt tài chính ứng dụng hình Z-Score trong dự báo khánh kiệt tài chính

... GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC KINH TẾ TP. HỒ CHÍ MINH --- [  [ --- Huỳnh Cát Tường Khánh kiệt tài chính ứng dụng hình Z-Score trong dự báo khánh kiệt tài chính ... tư, áp dụng hình Z-Score cho các DN Việt Nam trong việc dự báo khả năng phá sản. Thứ năm, đề xuất một số biện pháp hỗ trợ dự báo giải quyết hiệu quả tình trạng khánh kiệt tài chính ở ... hữu kiệt quệ tài chính ..........................................68 3.1.2. Luật pháp/ chính quyền kiệt quệ tài chính. .............................70 3.1.3. Sự phát triển tài chính kiệt...
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Từ khóa: sử dụng mô hình arima trong dự báo giásử dụng mô hình arima trong dự báotài liệu cơ sở matlab và ứng dụngtài liệu giáo trình access và ứng dụngcác mô hình phân tích đánh giá rủi ro tín dụng và ứng dụng mô hình logistic trong xếp hạng tín dụng doanh nghiệp vay vốn tại ngân hàng gp bankđánh giá sự tiếp thu và ứng dụng kỹ thuật ipm của nông dân sản xuất lúa tại huyện quỳnh phụ tỉnh thái bìnhBáo cáo thực tập tại nhà thuốc tại Thành phố Hồ Chí Minh năm 2018Nghiên cứu sự biến đổi một số cytokin ở bệnh nhân xơ cứng bì hệ thốngNghiên cứu tổ chức pha chế, đánh giá chất lượng thuốc tiêm truyền trong điều kiện dã ngoạiNghiên cứu tổ hợp chất chỉ điểm sinh học vWF, VCAM 1, MCP 1, d dimer trong chẩn đoán và tiên lượng nhồi máu não cấpGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitĐỒ ÁN NGHIÊN CỨU CÔNG NGHỆ KẾT NỐI VÔ TUYẾN CỰ LY XA, CÔNG SUẤT THẤP LPWANNGHIÊN CỨU CÔNG NGHỆ KẾT NỐI VÔ TUYẾN CỰ LY XA, CÔNG SUẤT THẤP LPWAN SLIDEPhối hợp giữa phòng văn hóa và thông tin với phòng giáo dục và đào tạo trong việc tuyên truyền, giáo dục, vận động xây dựng nông thôn mới huyện thanh thủy, tỉnh phú thọPhát triển mạng lưới kinh doanh nước sạch tại công ty TNHH một thành viên kinh doanh nước sạch quảng ninhĐịnh tội danh từ thực tiễn huyện Cần Giuộc, tỉnh Long An (Luận văn thạc sĩ)Tìm hiểu công cụ đánh giá hệ thống đảm bảo an toàn hệ thống thông tinBT Tieng anh 6 UNIT 2Giáo án Sinh học 11 bài 15: Tiêu hóa ở động vậtGiáo án Sinh học 11 bài 14: Thực hành phát hiện hô hấp ở thực vậtGiáo án Sinh học 11 bài 14: Thực hành phát hiện hô hấp ở thực vậtTrách nhiệm của người sử dụng lao động đối với lao động nữ theo pháp luật lao động Việt Nam từ thực tiễn các khu công nghiệp tại thành phố Hồ Chí Minh (Luận văn thạc sĩ)