Hasil Pencarian  ::  Simpan CSV :: Kembali

"This greatly expanded third edition of Survival analysis : a self-learning text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. The third edition continues to use the unique "lecture-book" format of the first two editions with one new chapter, additional sections and clarifications to several chapters, and a revised computer appendix. The Computer Appendix, with step-by-step instructions for using the computer packages STATA, SAS, and SPSS, is expanded this third edition to include the software package R."

"This very popular textbook is now in its third edition. Whether students or working professionals, readers apprciate its unique "lecture book" format. They often say the book reads like they are listening to an outstanding lecture. This edition includes three new chapters, an updated computer appendix, and an expanded section about modeling guidelines that consider causal diagrams. --Like previous editions, this textbook provides a highly readable description of fundamental and more advanced concepts and methods of logistic regression. It is suitable for researchers and statisticians in medical and other life sciences as well as academicians teaching second-level regression methods courses. --The Computer Appendix provides step-by-step instructions for using STATA (version 10.0), SAS (version 9.2), and SPSS (version 16) for procedures described in the main text. --Book Jacket."

"Pengendali logika fuzy memiliki kelemahan yaitu segala sesuatu harus didefnisikan terlebih dahulu secara eksplisit, termasuk aturan-aturan kendalinya. Masalah timbul jika aturan-aturan kendali tidak dimiliki dan tidak adanya seorang expert atau operator untuk membentuk aturan-aturan kendali yang sesuai. Masalah ini dapat diatasi dengan menggunakan jaringan syaraf buatan pada bagian inferensi. Jaringan syaraf memiliki kemampuan untuk dilatih dengan sekumpulan data pelatihan, sehingga setelah pelatihan selesai jaringan ini dapat digunakan tanpa harus mengetahui aturan-ataran kendali secara lengkap. Peuggunaan jaringan syaraf menimbulkan masalah bare, yaitu jika data pelatihan tidak tersedia, becarti konsep jaringan syaraf tidak dapat digunakan. Untuk itu digunakan mekanisme belajar (self-learning) untuk menghasilkan data pelatihan yang selanjutnya digunakan untuk melatib jaringan syaraf. Skripsi ini membahas mengenai algoritma dan penerapan pengendali self-learning neurofuzzy pada sistem overdamped dan underdamped serta membahas pengaruh perubahan parameter-parameter pengendali terhadap basil pengendalian."