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

## Presentasi berjudul: "1 Pertemuan 7 Ketidakpastian dalam Rules Matakuliah: H0383/Sistem Berbasis Pengetahuan Tahun: 2005 Versi: 1/0."— Transcript presentasi:

1 Pertemuan 7 Ketidakpastian 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 R1If the inflation rate is less than 5% then stock market price goes up (CF1=0.7) R2If 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 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 Membership function dari usia

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) de(t)/dt U(t) Regulator

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

15 Fuzzy Logic Fuzzy Position Control edeuFuzzy variable -1000-500-200-5 -800-400-160-4 -600-300-120-3 -400-200-80-2 -200-100-40 0000 200100401 400200802 6003001203 8004001604 10005002005 -4345-3-5-2201 NB NK PB PKNOL

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 Fuzzy Logic Resoning w. Fuzzy Logic -4345-3-5-2201 NB NK PB PKNOL 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 Fuzzy Logic Defuzzyfication (center of area method) -4345-3-5-2201 NB NK PB PKNOL

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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