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 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 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) 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