gas mileage prediction 1

7/31/2019 Gas Mileage Prediction 1

1/26

TRNG I HC CNG NGIP Tp. H CH MINHKHOA CNG NGH IN T

*********

TIU LUN

L Thuyt iu Khin Hin i

TI

Gas Mileage Prediction

GVHD : Trn Th H. Oanh

Lp hc phn: 210221301 - Khoa : Cng Ngh in T

Hc k: 3 -Nm hc: 2011 2012

Thc hin:

Nguyn nh Dng - MSSV: 09090421

Nguyn Tn Trung MSSV: 09068611

Nguyn V Bch - MSSV:09068811

Tp. H Ch Minh, Thng 6 nm 2012


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Danh sch nhm:

Nguyn nh Dng ............................................................................ MSSV: 09090421

Nguyn Tn Trung ................................................................................ MSSV: 09068611

Nguyn V Bch ................................................................................... MSSV:09068811

Mc Lc

1. Gii thiu chung ................................................................................................. 32. Phn vng d liu ............................................................................................... 43. La chn ng vo ............................................................................................... 54. Hun luyn ANFIS ........................................................................................... 125. ANFIS v m hnh hi quy tuyn tnh (Linear Regression) ............................ 146. Phn tch h thng ANFIS ................................................................................ 167. Hn chv lu ............................................................................................... 178. Run in the Command Window ......................................................................... 199. S lc v file gasdemo.m ............................................................................... 2010.File 'auto-gas.dat' .............................................................................................. 25


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1. Gii thiu chungGas Mileage Prediction trong Fuzzy Logic Toolbox ca phn mm MatLAB l mt m

hnh minh ha mc tiu hoa nhin liu ca cc loi xe trn c sda vo m hnh ANFIS

(Mng thch nghi da trn c ssuy lun m- Mt s kt hp gia mng noron v logic

m). Vic tin hnh kho st cng nh kim tra trong m hnh ny c tin hnh da

trn d liu c thu thp ttrc .

M hnh ny hay ni cch khc l d n ny s gip chng ta hiu r hn cc yu tnh

hng n mc tiu hao nhin liu ca mi xe bng vic tin hnh kho st vi cc dng

xe tiu biu. Nhng d liu ca qu trnh kho st ny c cung cp bi Phng Nghin

Cu My Mc trc thuc i Hc California ti Irvine.

Cc yu tu vo bao gm:

Dung tch xi-lanh. S xi-lanh. M lc. Gia tc xe. Khi lng.Nm ra i ca xe.

Ng ra l mc tiu hao nhin liu tnh bng qung ng i c trn lng nhin liu

tiu thu. n v l dm trn mi gallon(MPG). Lu rng mt gallon bng 3.78541178

lt, 1 dm bng 1.609 Km.

Cc thng s th hin c th trong bng sau:

Thuc tnh ng vo Ng ra

Tn xeS xi-

lanh

Dung

tch xi

lanh

M lcTrng

lngGia tc Nm MPG


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Chevrolet

Chevelle

Malibu

8 307 130 3504 12 70 18

PlymouthDuster

6 198 95 2833 15.5 70 22

Fiat 128 4 90 75 2108 15.5 74 24

Oldsmobile

Cutlass

Supreme

8 260 110 4060 19 77 17

Oldsmobile

Cutlass

Supreme

8 260 110 4060 19 77 17

Honda

Accord4 107 75 2205 14.5 82 36

Ford Ranger 4 120 79 2625 18.6 82 28

2.

Phn vng dliu

Nh cp t trc d liu ca qu trnh kho st c nghin cu t trc v

trong MatLAB chng c lu trong file auto-gas.dat trong folder ci t ca phn mm.

Ni dung ca file ny sc nh km trong ph lc. Trong file ny chng c chia

thnh 2 phn vng l d liu tin hnh kho st hun luyn v d liu kim tra.

tin hnh load d liu ln Workspace chng ta thc hin cc th tc sau:

[data, input_name] = loadgas;

trn_data = data(1:2:end, :);


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chk_data = data(2:2:end, :);

Nh vy d liu s c lu vo 2 phn vng l trn_data phc v chon vic kho st

hun luyn h thng ANFIS. Vng th 2 l chk_data l phn vng phc v cho vic kim

tra nhng kt qu hun luyn kho st.

3. La chn ng voVic tin hnh la chn s cc yu tu vo sc thc hin bi hm exhsrch. Hm

exhsrch thc hin mt tm kim y trong cc yu tu vo c sn la chn tp

hp cc yu tu vo m nh hng hu ht n tiu th nhin liu. Cc tham su

tin quy nh c th slng cc t hp u vo sc th trong khi tm kim. Vc

bn, exhsrch xy dng mt m hnh ANFIS cho tng s kt hp v hun luyn chng cho

mt giai on no v bo co hiu sut t c. Trong v dsau y, exhsrch c

s dng xc nh thuc tnh u vo c nh hng nht trong vic don lng tiu

th nhin liu.

Vic tin hnh hun luyn v kim tra sc tin hnh mt cch ng thi v s th

hin mt cch y trong th. Hnh trn l k hiu chra l kt qu qu trnh hun

luyn, cn hnh sao l kt qu ca qu trnh checkkim tra.

nh gi mc nh hng vi tng yu t ng vo:Thc hin hm exhsrch nh sau:

>> exhsrch(1, trn_data, chk_data, input_name);

+ Trong ca s command windown ca matlab s nhn c phn hi nh sau:

Train 6 ANFIS models, each with 1 inputs selected from 6 candidates...


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ANFIS model 1: Cylinder --> trn=4.6400, chk=4.7255

ANFIS model 2: Disp --> trn=4.3106, chk=4.4316

ANFIS model 3: Power --> trn=4.5399, chk=4.1713

ANFIS model 4: Weight --> trn=4.2577, chk=4.0863

ANFIS model 5: Acceler --> trn=6.9789, chk=6.9317

ANFIS model 6: Year --> trn=6.2255, chk=6.1693

Tc MatLAB ni rng s c mt yu t ng vo tt nht c chn t 6 ng c vin.

+ Ngoi ca kt qu trn ca s Command Windown chng ta nhn c kt qu

t hnh sau: s thy r hn cng nh s dtng tng hn.

Lu l cc i lng c gii thch nh sau:


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RMS Errors c hiu l thang o mc li nh hng n lng tiu th nhin

liu.

Weight: l khi lng xe.

Disp: - ( vit tt ca Displacement ) tc l dung tch xi-lanh.

Power: (vit gn ca Horsepowe) tc l m lc ca xe.

Cylind: (Cylinders) s xi-lanh ca my.

Year: nm sn xut(model).

Acceler: Gia tc xe.

Nhn thy rng u vo bin bn tri trong hnh 1 c li t nht hoc ni cch khc l n

lin quan nht i vi u ra l tiu hao nhin liu.

Biu cho thy thuc tnh trng lng u vo c nh hng nht. Hun luyn v kim

tra cc li c th so snh, m ng rng c overfitting khng. C ngha l chng ta c th

y xa hn mt cht v tm hiu nu c th la chn nhiu hn mt thuc tnh u vo

xy dng cc m hnh ANFIS.

nh gi mc nh hng bng vic kt hp 2 yu t ng vo l mt:Mt cch trc quan, ta ch c th chn 'Khi lng' v 'dung tch xi-lanh mt cch trc

tip t lc n c li t nht nh trong biu . Tuy nhin, iu ny khng nht thit phi

l s kt hp ti u ca hai yu tu vo m kt qu trong cc li hun luyn ti thiu.

xc minh iu ny, chng ta c th s dng exhsrch tm kim s kt hp ti u ca

2 thuc tnh u vo.

Th tc vi MatLAB nh sau:



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Kt qu phn hi ca MatLAB nh sau:


ANFIS model 1: Cylinder Disp --> trn=3.9320, chk=4.7920

ANFIS model 2: Cylinder Power --> trn=3.7364, chk=4.8683

ANFIS model 3: Cylinder Weight --> trn=3.8741, chk=4.6764

ANFIS model 4: Cylinder Acceler --> trn=4.3287, chk=5.9625

ANFIS model 5: Cylinder Year --> trn=3.7129, chk=4.5946

ANFIS model 6: Disp Power --> trn=3.8087, chk=3.8594

ANFIS model 7: Disp Weight --> trn=4.0271, chk=4.6349

ANFIS model 8: Disp Acceler --> trn=4.0782, chk=4.4890

ANFIS model 9: Disp Year --> trn=2.9565, chk=3.3905

ANFIS model 10: Power Weight --> trn=3.9310, chk=4.2974

ANFIS model 11: Power Acceler --> trn=4.2740, chk=3.8738

ANFIS model 12: Power Year --> trn=3.3796, chk=3.3505

ANFIS model 13: Weight Acceler --> trn=4.0875, chk=4.0095

ANFIS model 14: Weight Year --> trn=2.7657, chk=2.9954

ANFIS model 15: Acceler Year --> trn=5.6242, chk=5.6481


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Ngoi phn hi trn ta nhn c th sau:

Nh vy kt qu t hm trn cho ta bit rng khi lng v nm l kt hp ti u ca hai

thuc tnh u vo. Cc li o to v kim tra vic phn bit, cho thy s khi u ca

overfitting. N c thkhng c thn trng s dng nhiu hn hai yu tu vo

xy dng m hnh ANFIS. Chng ta c th kim tra tin xc minh gi tr ca n.

nh gi mc nh hng bng vic kt hp 3 yu t ng vo l mt:Vi vic kt hp 3 yu t ng vo lm mt ny chng ta s c 20 trng hp, lu l

vic nh gi vi 3 yu tu vo khng c ngha l s s dng v chn la. Vic chn

la s quyt nh sau ty thuc vo kt qu ca mi ln hun luyn.

Th tc tng t.



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Ta c kt qunh sau:


ANFIS model 1: Cylinder Disp Power --> trn=3.4446, chk=11.5329

ANFIS model 2: Cylinder Disp Weight --> trn=3.6686, chk=4.8923

ANFIS model 3: Cylinder Disp Acceler --> trn=3.6610, chk=5.2384

ANFIS model 4: Cylinder Disp Year --> trn=2.5463, chk=4.9001

ANFIS model 5: Cylinder Power Weight --> trn=3.4797, chk=9.3761

ANFIS model 6: Cylinder Power Acceler --> trn=3.5432, chk=4.4804

ANFIS model 7: Cylinder Power Year --> trn=2.6300, chk=3.6300

ANFIS model 8: Cylinder Weight Acceler --> trn=3.5708, chk=4.8376

ANFIS model 9: Cylinder Weight Year --> trn=2.4951, chk=4.0434

ANFIS model 10: Cylinder Acceler Year --> trn=3.2698, chk=6.2616

ANFIS model 11: Disp Power Weight --> trn=3.5879, chk=7.4916

ANFIS model 12: Disp Power Acceler --> trn=3.5395, chk=3.9953

ANFIS model 13: Disp Power Year --> trn=2.4607, chk=3.3563

ANFIS model 14: Disp Weight Acceler --> trn=3.6075, chk=4.2318

ANFIS model 15: Disp Weight Year --> trn=2.5617, chk=3.7860

ANFIS model 16: Disp Acceler Year --> trn=2.4149, chk=3.2480


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ANFIS model 17: Power Weight Acceler --> trn=3.7884, chk=4.0479

ANFIS model 18: Power Weight Year --> trn=2.4371, chk=3.2848

ANFIS model 19: Power Acceler Year --> trn=2.7276, chk=3.2580

ANFIS model 20: Weight Acceler Year --> trn=2.3603, chk=2.9152

V th:

Nhn thy nu kt hp 3 yu tu vo th khi lng, gia tc v nm l yu t c nh

hng n mc tiu hao nhin liu nht.

Tuy nhin, hun luyn ti thiu v kim tra li khng gim ng k t m hnh 2 - u

vo tt nht, m ch ra rng, thuc tnh gia tc mi c thm vo khng ci thin nhiu


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s dbo. khi qut tt hn, chng ta lun lun mun c mt m hnh vi mt cu

trc n gin. V vy, chng ta s bm vo m hnh 2 ng vo ANFIS tip tc tm

hiu.

Chng ta cn tin hnh lu kt qu ln 2 vo workspace th tc nh sau:

>> input_index = exhsrch(2, trn_data, chk_data, input_name);

Nh vy phn vng input_index s l phn vng cha d liu ca qu trnh kho st

hun luyn 2 ng vo.

Sau ta gii nn cc thuc tnh u vo c la chn t vic o to ban u v tp

hp d liu kim tra.

>> close all;

>> new_trn_data = trn_data(:, [input_index, size(trn_data,2)]);

>> new_chk_data = chk_data(:, [input_index, size(chk_data,2)]);

4. Hun luyn ANFIS

Hm exhsrch ch dnh cho vic kha st h ANFIS c mt cng on duy nht vi mc

ch l nhanh chng tm ra ng vo ti u nht.Nhng by gi th d liu ng vo c hiu chnh, chng ta phi thc hin qu trnh hun luyn ln n 100 cng on.


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Hm genfis1 gip to mt hFIS ban u t d liu kho st, ci m chng ta tm

c t vic, ci m chng ta s tm v hiu chnh chng c c m hnh ANFIS

cui cng.

Thc hin trong ca sCommand Windown nh sau:

in_fismat = genfis1(new_trn_data, 2, 'gbellmf');

[trn_out_fismat trn_error step_size chk_out_fismat chk_error] = ...

anfis(new_trn_data, in_fismat, [100 nan 0.01 0.5 1.5], [0,0,0,0], new_chk_data, 1);

ANFIS s tr v cho chng ta cc sai lch trong qu trnh kho st v kim tra cc tham

su ra ca h. t tuyn sai lnh s cung cp cho chng ta nhiu thng tin ca qu

trnh kho st.


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Nhn thy rng th trn cho thy cc ng cong li cho 100 chu k hun luyn ca

ANFIS. Cc ng cong mu xanh l cy cung cp cho vic ci to li v cc ng

cong mu cung cp cho vic kim tra li. Kim tra li ti thiu xy ra mc thi gian

45, c chnh bi mt vng trn. Ch rng ng cong kim tra li i ln sau mc

50, ch ra rng overfits ci to thm cc v a ra cc d liu qut hn.

5. ANFIS v m hnh hi quy tuyn tnh (Linear Regression)Mt bi kim tra tt vo thi im ny s kim tra c hiu sut ca m hnh ANFIS

vi mt m hnh hi quy tuyn tnh.

Cc don ANFIS c thc so snh vi mt m hnh hi quy tuyn tnh bng cch

so snh gi tr RMSE ca n (gc: l hnh vung) so vi kim tra d liu.


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% Thc hin hi quy tuyn tnh

N = size(trn_data,1);

A = [trn_data(:,1:6) ones(N,1)];

B = trn_data(:,7);

coef = A\B; % Gii quyt cho cc thng s hi quy t cc d liu ci to

Nc = size(chk_data,1);

A_ck = [chk_data(:,1:6) ones(Nc,1)];

B_ck = chk_data(:,7);

lr_rmse = norm(A_ck*coef-B_ck)/sqrt(Nc);

% In kt qu

fprintf('\nRMSE against checking data\nANFIS : %1.3f\tLinear Regression : %1.3f\n', a,

lr_rmse);

RMSE : Chng li kim tra d liu

ANFIS: 2,978 Hi quy tuyn tnh: 3,444

C th thy rng m hnh ANFIS nhanh hn so vi m hnh hi quy tuyn tnh.


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6. Phn tch h thng ANFISChk_out_fismat l bin i din cho nh chp ca m hnh ANFIS kim tra li trong qu

trnh ci to ti thiu. B mt u vo-u ra ca m hnh c th hin trong thdi

y:

>> chk_out_fismat = setfis(chk_out_fismat, 'input', 1, 'name', 'Weight');

>> chk_out_fismat = setfis(chk_out_fismat, 'input', 2, 'name', 'Year');

>> chk_out_fismat = setfis(chk_out_fismat, 'output', 1, 'name', 'MPG');

% To th ng ra FIS

>> gensurf(chk_out_fismat)


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B mt u vo - u ra trn l mt b mt phi tuyn v n iu v minh ha cho

chng ta thy m hnh ANFIS, v s nhng thay i ca ng ra so vi sthay i gi tr

ca 'Weight v 'Year'

7. Hn chv lu

Chng ta c th thy mt s b hng gc xa ca b mt. Gc cao ni rng cc xe t

nng hn, s dn nhin liu hiu quhn. iu ny l hon ton phn trc quan, v n l

mt kt qu trc tip do thiu d liu.


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chng minh c s thiu ca d liu trong qu trnh hun luyn chng ta s v th

phn b ca 2 yu t ng vo l khi lng v nm sn xut. Hay ni cch khc l mi

im trn th l mt loi xe c thng s khi lng v nm sn xut nm trn trc

honh v tung tng ng.

Th tc trn MatLAB nh sau:

plot(new_trn_data(:,1), new_trn_data(:, 2), 'bo', ...

new_chk_data(:,1), new_chk_data(:, 2), 'rx');

xlabel('Weight');

ylabel('Year');

title('Training (o) and checking (x) data');

T d liu cho trc MatLAB v ra s phn b ca 2 yu tu vo nh sau:


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R rng ta thy d liu phn b rt khng u, vng pha trn gc phi thiu rt nhiu d

liu, c thy cng l do thc t v xu th nhng chic xe c sn xut vo u nhng

nm 80, khi lng gim ng k.

8. Run in the Command Window


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L nt lnh nm trn gc phi mn hnh Gas Mileage Prediction demo h trcho bn

cng c thc hin d n bng cch thc hin tng bc (step) bn ch cn kch vo next

l tt ccc bc sc thc hin bn khng cn thit phi nhp tng lch mt.

9. S lc v file gasdemo.m

Nh bit, MatLAB l mt cng c khng ch h trcho ngi dng thc hin cc thao

tc trn ca s lch Command Windown m cn h trngi dng thc hin cc thao tc

lp trnh trn mt trnh Editor. Trnh Editor trong matlab ny c gi chung l trnh bintp M-file. Trong M-flie ngi dng c th vit tt c cc lnh m h c th thc hin sau

mi chy chng. M-file c ti u ha nhm mc ch trgip ngi dng thc hin

cc cng c lp trnh giao din GUI. V ng thi chng khng n thun l mt lch

na m c nng ln tm mt d n c thlu li di mt file .m.


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Vi Gas Mileage Prediction MatLAB cung cp cho chng ta file gasdemo.m ghi chp li

tt c cc lnh trong qu trnh thc hin kho st hun luyn.

chy tt c cc lnh ny trn trnh Editor ta ch cn nt Run hay n thun l nhn F5

th tt c qu trnh kho st hun luyn kim tra sc thc hin. ng thi trn ca s

lnh Command Windown s xut hin cc kt qunh qu trnh thc hin tng lnh.

Ni dung trong file gasdemo.m nh sau: ( c nhm rt ngn ch thch v vit ha):

%% Gas Mileage Prediction

% Xy dng m hnh dbo nh mc tiu hao nhin liu

%

%

% Bn quyn thuc The MathWorks, Inc. 1994-2006

% Phin bn : 1.9.2.5 $ $Date: 2006/09/30 00:18:32 $

%% C sd liu c cung cp bi i hc California

% tham kho thm ti http://www.ics.uci.edu/~mlearn/MLRepository.html. It

% D liu vo ra thit lp nh sau:,

% Gm 6 ng vo l no. of cylinders, displacement , horsepower, weight, acceleration,

and model year.

%Ng ra l nh mc tiu hoa nhin liu

%% C sd liu c lu trong : 'auto-gas.dat'


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%% Tin hnh load d liu

[data, input_name] = loadgas;

trn_data = data(1:2:end, :);

chk_data = data(2:2:end, :);

%% Thc hin chn ng vo ti u

%% nh gi vi mt ng vo

exhsrch(1, trn_data, chk_data, input_name);

%% Vi 2 Ng vo


%% Vi 3 yu t ng vo.


%% Do kt qu thu73c nghim ln 2 l ti u v trc quan nht nn ln 2 sc chn:

input_index = exhsrch(2, trn_data, chk_data, input_name);


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%% ng cc ca s th v chuyn qua bc hun luyn

close all;

new_trn_data = trn_data(:, [input_index, size(trn_data,2)]);

new_chk_data = chk_data(:, [input_index, size(chk_data,2)]);

%% Training the ANFIS Model

%% tin hnh hun luyn vi th tc nh sau: bao gm 100 cng on

in_fismat = genfis1(new_trn_data, 2, 'gbellmf');

[trn_out_fismat trn_error step_size chk_out_fismat chk_error] = ...

anfis(new_trn_data, in_fismat, [100 nan 0.01 0.5 1.5], [0,0,0,0], new_chk_data, 1);

%% Thc hin vth nh gi

[a, b] = min(chk_error);

plot(1:100, trn_error, 'g-', 1:100, chk_error, 'r-', b, a, 'ko');

title('Training (green) and checking (red) error curve');


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xlabel('Epoch numbers');

ylabel('RMS errors');

%% so snh vi h hi quy tuyn tnh

N = size(trn_data,1);

A = [trn_data(:,1:6) ones(N,1)];

B = trn_data(:,7);

coef = A\B; % Solving for regression parameters from training data

Nc = size(chk_data,1);

A_ck = [chk_data(:,1:6) ones(Nc,1)];

B_ck = chk_data(:,7);

lr_rmse = norm(A_ck*coef-B_ck)/sqrt(Nc);

% in ra kt qu

fprintf('\nRMSE against checking data\nANFIS : %1.3f\tLinear Regression : %1.3f\n', a,

lr_rmse);

%% Phn tch h thng ANFIS

chk_out_fismat = setfis(chk_out_fismat, 'input', 1, 'name', 'Weight');

chk_out_fismat = setfis(chk_out_fismat, 'input', 2, 'name', 'Year');

chk_out_fismat = setfis(chk_out_fismat, 'output', 1, 'name', 'MPG');


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%% V th FIS

gensurf(chk_out_fismat);

%% V biu phn b ca c sd liu.

plot(new_trn_data(:,1), new_trn_data(:, 2), 'bo', ...

new_chk_data(:,1), new_chk_data(:, 2), 'rx');

xlabel('Weight');

ylabel('Year');

title('Training (o) and checking (x) data');

10.File 'auto-gas.dat'L file cha d liu cn thit cho qu trnh hun luyn d liu. File ny c cung cp

bi i Hc California. nh dang file l .dat tuy nhin bn vn c th xem v chnh sa

di trnh notepad. iu ny c th thc hin khi bn tin hnh mt d n tng t. V

khi bn khng mt qu nhiu thi gian cho vic phi xy dng li tu thay v bn

ch cn sa s liu.

Cch thc thit lp ca file trn trnh notepad nh sau:

Mi loi xe bn vit di nh dang nh sau:

18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu"


26/26

Trong thc t ca s liu l: Ng ra s xi lanhdung tch xilanh m lckhi

lnggia tcnm 1Thng hiu.

gas mileage prediction 1

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