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Pertemuan 7 Ketidakpastian dalam Rules

Pertemuan 7 Ketidakpastian dalam Rules. Matakuliah : H0383/Sistem Berbasis Pengetahuan Tahun : 2005 Versi : 1/0. Learning Outcomes. Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : Memilih suatu metode untuk mengatasi ketidakpastian pada rule based systems. Outline Materi.

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Pertemuan 7 Ketidakpastian dalam Rules

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  1. Pertemuan 7Ketidakpastian dalam Rules Matakuliah : H0383/Sistem Berbasis Pengetahuan Tahun : 2005 Versi : 1/0

  2. Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : • Memilih suatu metode untuk mengatasi ketidakpastian pada rule based systems

  3. Outline Materi • Sebab Ketidakpastian • Certainty Factor • Fuzzy Logic

  4. Ketidakpastian Sebab ketidak pastian: • Informasi partial • Informasi not fully reliable • Representation languages is inherently imprecise • Info come from multiple sources and conflict. • Info is approximate. • Non absolute cause effect relationship exists.

  5. Certainty Factor • Certainty Factor = Measure of Belief - Measure of Disbelief • CF[P,E] = MB[P,E] – MD[P,E] • P=probability • E= evidence

  6. Certainty Factor • If inflation is above 5% (CF=50%) and if unemployment rate is above 7% (CF 70%) and if bond prices decline (CF=100%) then stock prices decline. • CF = min CF(A,B,C). Then stock prices decline( CF = 50%). • OR- maximum CF(A,B,C)

  7. Certainty Factor R1 If the inflation rate is less than 5% then stock market price goes up (CF1=0.7) R2 If unemployment is less than 7% then stock market price goes up (CF2 = 0.6) • CF (R1,R2) = CF1 + CF2[1-CF1] = 0.88

  8. Fuzzy Logic • Generalisasi logika (tidak hanya 1/0) • Aplikasi penting: Sistem Pengaturan • Keuntungan: • Pengaturan Lebih “smooth” dari sekedar ON/OFF • Tidak memerlukan model matematika • Kekurangan: • Stabilitas sistem tidak terdefinisi secara eksakta.

  9. Fuzzy Logic Membership function dari usia µ(x) : membership function µ(15) = 1/muda + 0/dewasa +0/tua µ(24) = 0,6/muda + 0,4/dewasa +0/tua µ(40) = 0/muda + 1/dewasa +0/tua

  10. Fuzzy Logic Operasi Logika Fuzzy µA(x) AND µB(y) = minimum(µA(x) , µB(y)) µA(x) OR µB(y) = maximum(µA(x) , µB(y)) NOT µA(x) = 1 - µA(x) µA(x) = 0.7, µB(y) = 0.5 µA(x) AND µB(y) = 0.5 µA(x) OR µB(y) = 0.7 NOT µA(x) = 1 – 0.7 = 0.3

  11. Fuzzy Logic IF suhu = dingin THEN aruslistrik = kecil IF suhu = normal THEN aruslistrik = sedang IF suhu = panas THEN aruslistrik = besar

  12. Fuzzy Logic

  13. Fuzzy Logic • Fuzzy Control e(t) U(t) Regulator de(t)/dt

  14. Fuzzy Logic • Fuzzy Control Rule Base Fuzzy Reasoning (Inferensi) defuzzification Fuzzification

  15. e de u Fuzzy variable -1000 -500 -200 -5 -800 -400 -160 -4 -600 -300 -120 -3 NB PB NK NOL PK -400 -200 -80 -2 -200 -100 -40 -1 0 0 0 0 -5 -4 -3 -2 -1 0 1 2 3 4 5 200 100 40 1 400 200 80 2 600 300 120 3 800 400 160 4 1000 500 200 5 Fuzzy Logic • Fuzzy Position Control

  16. Fuzzy Logic • If e is PB & de is any THEN u is PB • If e is PK & de is NOL THEN u is PK • If e is PK & de is PK THEN u is PK • If e is NOL & de is PK THEN u is NOL • If e is NOL & de is NK THEN u is NK • If e is NK & de is NK THEN u is NK • If e is NB & de is any THEN u is NB

  17. NB PB NK NOL PK -5 -4 -3 -2 -1 0 1 2 3 4 5 Fuzzy Logic • Resoning w. Fuzzy Logic e de

  18. Fuzzy Logic • e= 0.8/NB + 0.2/NK (dari gambar) • de=0.4/NB + 0.6/NK • If e = NB and de = any THEN u=NB • If e = NK and de = NK THEN u=NK • If e = 0.8/NB and de = 0.4/NB THEN u=0.4NB • If e = 0.2/NK and de = 0.6/NK THEN u=0.2/NK

  19. NB PB NK NOL PK -5 -4 -3 -2 -1 0 1 2 3 4 5 Fuzzy Logic • Defuzzyfication (center of area method)

  20. Penutup • Merepresentasikan bahasa verbal manusia ke dalam suatu simbol logika dapat mengakibatkan ketidakpastian. • Certainty Factor dan Fuzzy Logic dapat mengatasi ketidakpastian dalam rule-based systems

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