APPLICATION OF NEURO-FUZZY METHOD FOR PREDICTION OF VEHICLE FUEL CONSUMPTION

Transcription

1 APPLICATION OF NEURO-FUZZY METHOD FOR PREDICTION OF VEHICLE FUEL CONSUMPTION RAMADONI SYAHPUTRA Department of Electrcal Engneerng, Faculty of Engneerng, Unverstas Muhammadyah Yogyakarta Jl. Rngroad Barat Tamantrto, Kashan, Yogyakarta INDONESIA E-mal: ABSTRACT Ths paper presents the applcaton of neuro-fuzzy method for predcton of vehcle fuel consumpton predcton. Predcton motor vehcle fuel consumpton has become a strategc ssue, because t s not only related to the ssue of avalablty of fuel but also the problem of the envronmental mpact caused. Ths study used automoble data,.e. number of cylnders, dsplacement, horsepower, weght, acceleraton, and model year, whle the output varable to be predcted s the fuel consumpton n MPG (mles per gallon). 'Weght' and 'Year' are selected as the best two nput varables. The tranng and checkng errors are gettng dstngushed, ndcatng the outset of overfttng. The results of ths research are expressed n three dmenson nput-output surface graph of the best two-nput ANFIS model for MPG predcton. It s a nonlnear and monotonc surface, n whch the predcted MPG ncreases wth the ncrease n 'Weght' and decrease n 'Year'. The tranng RMSE s 2.767; the checkng RMSE s The greater the weght of the motor vehcle, the greater the amount of fuel needed to travel the same dstance. In comparson, a smple lnear regresson usng all nput canddates results n a tranng RMSE of 3.453, and a checkng RMSE of Keywords: ANFIS, Fuel Consumpton, Fuel Predcton, RMSE. 1. INTRODUCTION The avalablty of fuels the less, requres us to thnk of antcpatory steps that must be done. Some antcpatory measures that have been done s to produce a vehcle that s fuel-effcent and use alternatve fuels such as bodesel, boethanol, hydrogen gas, and others. One of the measures related to fuel savngs s to manage well the use of motor vehcle fuel [1]. Management of fuel use s needed, especally for each ndvdual vehcle owners. In order to manage the use of fuel properly, the necessary nformaton regardng motor vehcle fuel consumpton and vehcle characterstcs concerned. Ths nformaton s useful for use as a foothold n predctng the fuel consumpton of motor vehcles. Predcton motor vehcle fuel consumpton has become a strategc ssue, because t s not only related to the ssue of avalablty of fuel but also the problem of the envronmental mpact caused. Some methods to predct the fuel consumpton of motor vehcles have been developed nclude methods based on data speeds and vehcle acceleraton [2], a method based on the characterstcs of multdmensonal engne [3], and methods based on statstcal models [4]. These methods can be categorzed as conventonal methods. Snce the ntroducton of the concept of fuzzy logc n the md-1960s, then ths concept has become a new dscourse n applcatons n varous felds [5] [6]. The next development was the emergence of artfcal neural network method, whch s one of ntellgent methods. Fuzzy logc s a development of the prmtve logc that are only recognze two states, namely "yes" or "no". Wth the fuzzy logc, t can be recognzed the lngustc varables lke rather large, large, very large, and so on. Thus, the applcaton of fuzzy logc wll lead to more adaptve systems [7]. For purposes of predcton and estmaton system, the use of ntellgent systems has become an 138

2 nterestng ssue. Therefore, ths study wll try to apply the concept of artfcal neural networks and fuzzy logc, whch s often also known as the method of ANFIS (Adaptve Neuro Fuzzy Inference System) to predct the fuel consumpton of motor vehcles. The purpose of ths study was to learn more profound method through concepts ANFIS adaptve network and fuzzy logc nference systems and to create a devce-predcton software motor vehcle fuel consumpton accurately usng ANFIS method, whch was developed n Matlab software devces. The man contrbuton of ths study s to the world of educaton and research or other communty (ndustry, banks, and companes) that have a great nterest or nterest, drectly or ndrectly. More concretely, these contrbutons are detaled as follows: 1) Usng the model to be made n ths study, users can learn the concepts and workngs ANFIS on ntellgent systems especally n the predcton problem, 2) Wth accurate predcton method, the use of fuel for motor vehcles can be more effcent, and 3) From the results of ths study are expected to be useful n growng new nspratons for ANFIS applcaton and development. Neuro-fuzzy method s a combnaton of artfcal neural network method and the method based on fuzzy logc. Adaptve neuro-fuzzy method has been wdely used n varous applcatons n varous felds [8] [9]. Applcatons neuro-fuzzy methods are ncludng for the purposes of control, estmaton and predcton [10]. In the current control system has been applyng the prncples of fuzzy logc called FLC (fuzzy logc controller). How t works s smlar to the control of an operator control, do not pay attenton to the nternal structure of the plant, and just observe the error as the dfference between the set-pont outputs and change system settngs control panel to mnmze the error. The subsequent development of an artfcal ntellgence system was ntegratng the artfcal neural network wth fuzzy logc, whch s known as the ANFIS. Adaptve Neuro Fuzzy Inference System (ANFIS) has been accepted as a relable method and s beleved to contnue to evolve n order to address the need for an ntellgent system. ANFIS s a fuzzy logc nference systems are mplemented on a system of adaptve network [11]. Understandng of the ANFIS can be started from the basc prncples of fuzzy logc system [12], artfcal neural networks [13], a network of neuro fuzzy [10, 12], to the concept of ANFIS and ts applcatons [10, 11]. Neuro-fuzzy system s a mult-layered network of connectons that realze the basc elements and functons of the control system / tradtonal fuzzy logc decson. Because neuro fuzzy system s an unverse approach operator then neuro fuzzy control system s also unverse approach operator, because of ts functons consttute a form (somorphc) wth tradtonal fuzzy logc control system. There are several knds of neuro fuzzy networks ncludng FALCON, GARIC, and other varatons [7]. By leveragng the network archtectures and learnng algorthms assocated, neuro-fuzzy system has been successfully appled to a varety of [14] [21]. Applcatons of neuro-fuzzy method can also be developed by combnng t wth other artfcal ntellgence methods lkely PSO [22] [25]. However, most of the neuro-fuzzy system shows some major defcences, namely the emergence of a decrease n performance. These defcences due to the number of fuzzy rules and ncapacty gan knowledge of a gven set of tranng data. Wth success n varous felds, draw ANFIS method to be appled n an ntellgent system that s for the purposes of predcton of the fuel consumpton of motor vehcles. 2. FUNDAMENTAL THEORY 2.1. Vehcle Fuel Consumpton Model The technologcal advances that accompaned rapd economc development make the energy becomng key ssues for the world communty. Car as a mode of transportaton today and the future also contnues to progress both n terms of quantty and qualty. The sze of the current car qualty s not only located the engne capablty and rde comfort, but also on ts fuel consumpton. The cars produced today are requred to use fuel economcally, or even have developed the car wth fuel s also non-fuel such as electrc cars, cars wth hydrogen fuel, and others. These steps are carred out because of the depleton of the avalablty of fuel. Regardless of the energy problem, t s undenable that the current vehcle operatng n ths world s stll the majorty of ol-fueled. The use of automatc fuel ol could not be avoded the problem of CO 2 emssons nto the atmosphere, whch s a major component n the combuston products. CO 2 s a gas that s not toxc, but ts presence s hghly contested because t ncreases the nfluence of greenhouse gases that lead to the depleton of the ozone layer. The burnng of fossl fuels also causes 139

3 a great nfluence n threatenng the avalablty of oxygen n the ar, because t wll be replaced by CO 2. Therefore t s the duty of the Engneer to thnk of antcpatory steps n addressng ths ssue. Related to ths ssue, there s a method of predcton of fuel consumpton s based on a multdmensonal engne characterstc. Fgure 1. Schematc of a causal model n the form of state equaton The multdmensonal engne characterstcs defned usng dynamc relatonshps commonly used n the graphc theory Bond and Equal Crcumstances [3], whch s n the form of the state equaton expressed by: X = f1 (X, U) (1) Y = f2 (X, U) (2) Machne parameters that are mportant to note n ths method s the angular velocty, torque, flud temperature, ol temperature, CO 2 emssons, emssons of HC and NO 2 emssons. Related to the mpact caused by the ol-fueled vehcles n the form of exhaust emssons endanger human health and the envronment, the use of fuel for vehcles need to be managed properly. One step fuel management s to know clearly the needs of the fuel consumpton. For ths reason n ths study predcted vehcle fuel consumpton ANFIS Method ANFIS method has been became a popular method n many applcaton. A bref descrpton of the prncples of Adaptve neuro-fuzzy nference system (ANFIS) whch are referred to [11] s descrbed n ths secton. The basc fundamental structure of the type of fuzzy nference system could be seen as a model that maps nput characterstcs to nput membershp functons. Then t maps nput membershp functon to rules and rules to a set of output characterstcs. Fnally t maps output characterstcs to output membershp functons, and the output membershp functon to a sngle valued output or a decson assocated wth the output. The neuro-adaptve learnng method works smlarly to that of neural networks. Neuro-adaptve learnng technques provde a method for the fuzzy modelng procedure to learn nformaton about a data set. It computes the membershp functon parameters that best allow the assocated fuzzy nference system to track the gven nput/output data. A network-type structure smlar to that of a neural network can be used to nterpret the nput/output map so t maps nputs through nput membershp functons and assocated parameters, and then through output membershp functons and assocated parameters to outputs,. The parameters assocated wth the membershp functons changes through the learnng process. The computaton of these parameters (or ther adjustment) s facltated by a gradent vector. Ths gradent vector provdes a measure of how well the fuzzy nference system s modelng the nput/output data for a gven set of parameters. When the gradent vector s obtaned, any of several optmzaton routnes can be appled n order to adjust the parameters to reduce some error measure (performance ndex). Ths error measure s usually defned by the sum of the squared dfference between actual and desred outputs. ANFIS uses a combnaton of least squares estmaton and back propagaton for membershp functon parameter estmaton. The suggested ANFIS has several propertes: 1. The output of ANFIS s zero-th order Sugenotype system. 2. ANFIS has a sngle output, obtaned usng defuzzfcaton of weghted average. 3. ANFIS has no rule for sharng. Dfferent rules do not share for output membershp functon that has the same value. 4. ANFIS has unty weght for each rule. Fgure 1 shows Sugeno s fuzzy logc model. Fgure 2 shows the ANFIS archtecture, comprsng by nput layer, fuzzfcaton layer, nference later, and defuzzfcaton layer. The network can be vsualzed as consstng of nputs, wth N neurons n the nput layer and F nput membershp functons for each nput, usng F*N neurons n the layer of fuzzfcaton. In ths case, there are FN rules wth FN neurons n the nference whle there are defuzzfcaton layers and one neuron n the output layer. It s assumed that the FIS under consderaton has two nputs x and y and one output z, as can be seen n Fgure 2. For a zero-order n Sugeno fuzzy model as used n ths research, a common rule set 140

4 wth two fuzzy f-then rules s the followng: Rule 1: If x s A1 and y s B1, Then f1 = r1 (3) Rule 2: If x s A2 and y s B2, Then f2 = r2 (4) µ µ A 1 B 1 w 1 f 1 = p 1 x + q 1 y + r 1 x y µ µ A 2 B 2 w 2 f 2 = p 2 x + q 2 y + r 2 x y = + + Fgure 1. Sugeno s fuzzy logc model Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 x y x A 1 A 2 w 1 N w 1 w 1f 1 f y B 1 B 2 w 2 N w 2 x y w 2f 2 Fgure 2. The archtecture of the 2-nput and 1-output ANFIS 141

5 The output of the node -th n layer n s denoted as O n, : Layer 1. Every node n ths layer s a square node wth a node functon: O = µa(x), for = 1, 2, (5) or, O = µb-2(y), for = 3, 4 (6) where x s the nput to node-, and A s the lngustc label (small, large, etc.) assocated wth ths node functon. In other words, O s the membershp functon of A and t specfes the degree to whch the gven x satsfes the quantfer A. Usually µa(x) s chosen to be bell-shaped wth maxmum equal to 1 and mnmum equal to 0, such as the generalzed bell functon: μ A 1 (x) = (7) 2b x c 1+ a The parameters are referred to as premse parameters. Layer 2. Every node n ths layer s a crcle node labelled Π whch multples the ncomng sgnals and sends the product out. For nstance, = w = µa(x) x µb(y), = 1, 2. (8) Each node output represents the frng strength of a rule. (In fact, other T-norm operators that performs generalzed AND can be used as the node functon n ths layer.) Layer 3. Every node n ths layer s a crcle node labeled N. The -th node calculates the rato of the -th rule s frng strength to the sum of all rules frng strengths: O w 3 = w=, = 1, 2. (9) w1+ w2 For convenence, outputs of ths layer wll be called normalzed frng strengths. Layer 4. Every node n ths layer s a square node wth a node functon: 4 O wf = w (px+ qy+ r) = (10) where w s the output of layer 3, and {p, q, r} s the parameter set. Parameters n ths layer wll be referred to as consequent parameters. Layer 5. The sngle node n ths layer s a crcle node labeled Σ that computes the overall output as the summaton of all ncomng sgnals,.e., O (11) 5 = wf 3. METHODOLOGY Intellgent systems desgn model for the predcton of the fuel consumpton of motor vehcles generally consst of three stages, namely the collecton of tranng data (Fgure 4), ANFIS tranng process (Fgure 5), and the use of predctors ANFIS (Fgure 6). In the process of data collecton tranng, data collected n the form of fuel ol consumpton for a wde range of vehcle brands wth regard sx nput attrbutes that the number of cylnders, dsplacement, power, weght, acceleraton, and the year of manufacture. Varable output fuel consumpton of vehcles s concerned, the archtectural desgn of ANFIS n ths study, based on Fgure 2 that can use Sugeno ANFIS models. x(k) u(k) Z -1 Vehcle Parameters x(k+1) = f(u(k), x(k) Fgure 4. Block Dagram Of The Input And Output Of Data Collecton In Fgure 4, the value of x (x + 1) s the output of a functon that has the nput u (k) and x (k). So n ths case a motor vehcle s a functon that depends on the prevous output. The next tranng s on ANFIS to the data nputs and outputs. The tranng structure s shown n Fgure

6 u(k) x(k+1) Vehcle Parameters + e n x(k) Z -1 ANFIS Fgure 5. ANFIS Tranng Process Tranng data used n the plant accordng Fgure 5 has the followng format: [x(k), x(k+1), u(k)] The frst two columns of data s the data beng nput ANFIS last column s the data output of the ANFIS. Tranng data s obtaned by nsertng a random value wth a magntude of between -1 and 1 to obtan x (k) and x (k + 1). Tranng s done by enterng the value x (k) and x (k + 1) whch have been obtaned ANFIS then ANFIS output compared wth the value u (k). Once the data s obtaned and ANFIS tranng has been traned, then ANFIS tranng results are used to predct the consumpton of fuel for motor vehcles, as shown n Fgure 6. Z -1 x d (k+1) x(k) ANFIS u(k) Vehcle model x(k+1) Fgure 6. Aplcaton Process Of ANFIS Predctor 4. RESULTS AND DISCUSSION 4.1 General In ths study addressed the use of methods ANFIS (Adaptve Neuro Fuzzy Inference Systems) to predct the fuel consumpton of motor vehcles, especally cars. ANFIS method n ths study utlzng the toolbox contaned n the software MATLAB, the Fuzzy Logc Toolbox. In ths case the functon used s ANFIS functon. Predcton fuel s needed n order to plan for the car especally for the purpose of the trp wth a very far dstance. Predcton fuel often referred to as a predcton MPG (mles per gallon), a predcton measure how far that can be reached by a car for every one gallon of fuel. 4.2 ANFIS Tranng Predcton MPG (mles per gallon) s a problem that s nonlnear regresson, n whch some of the completeness of the nformaton profles of a car s needed to predct the fuel consumpton n MPG. Therefore, a relevant data s pertanng to the use of the fuel of a car, and also for the cars of other 143

7 products. In ths study, used for ANFIS tranng data obtaned from data provded by the UCI (Unv. of Calforna at Irvne) Machne Learnng Repostory. The address contaned n the data collected from the cars wth dfferent models and brands, as shown n Table 1. The table shows some tuple of the data set MPG (mles per gallon). Sx attrbute nput conssts of a number of cylnders, dsplacement, power (HP), weght (kg), acceleraton, and model year. Furthermore, the output varables to be predcted are the fuel consumpton n MPG (mles per gallon). Brands and models of cars are shown n the frst column of Table 1 s not used n ths predcton, and s shown only for the supportng nformaton. The set of data obtaned from the orgnal data fle 'auto-gas.dat'. Then the data set s parttoned nto a tranng set (tuple ndexed odd) and the set of checks (tuple ndexed even), and use the functon 'exhsrch' to fnd the nput attrbutes that have better predctve power for ANFIS modellng. Automoble Name Chevrolet Chevelle Malbu Plymouth Duster Number of Cylnders Table 1. Techncal Data Of Cars Of Dfferent Brands Dsplacement Power (HP) Input Data Weght Acceleraton Years Output Data Fat Oldsmoble Cutlass Supreme MPG Toyota Honda Accord Ford Ranger To select the best nput attrbutes, 'Exarch' construct sx ANFIS, each wth a sngle nput attrbute. In ths case the results after executon exhsrch are (1, trn_data, chk_data, nput_name). It can be seen that 'Weght' s the most nfluental nput attrbute, then the attrbutes most nfluental nput two numbers s 'dsp', and so on. Error tranng and checkng than ts sze, whch mples that there s no overfttng and can be selected more nput varables. Based on ntuton, t can be smplfed wth selectng 'Weght' and 'dsp' drectly. But not requred two-anfis models wth mnmal tranng error. To prove ths, t can be done by actvatng the command exhsrch (2 trn_data, chk_data, nput_name) to select the two best entres of all combnatons. exhsrch(1, trn_data, chk_data, nput_name); wn1 = gcf; Furthermore traned 6 ANFIS models, each wth 1 nput elected from 6 canddates, resultng n the followng data: ANFIS model 1: Cylnder --> trn=4.6400, chk= ANFIS model 2: Dsp --> trn=4.3106, chk= ANFIS model 3: Power --> trn=4.5399, chk= ANFIS model 4: Weght --> trn=4.2577, chk= ANFIS model 5: Acceler --> trn=6.9789, chk= ANFIS model 6: Year --> trn=6.2255, chk=

8 Fgure 7. Tranng error and checkng of 6 ANFIS model To demonstrate the results of the two nputs s selected. It can be done by selectng the 'Weght' and 'Year' as the best two nput varables. Error tranng drect nspecton can be dstngushed, whch ndcates the begnnng of overfttng. As a comparson, t can be used exhsrch to choose three nputs, namely: nput_ndex = exhsrch(2, trn_data, chk_data, nput_name); new_trn_data = trn_data(:, [nput_ndex, sze(trn_data,2)]); new_chk_data = chk_data(:, [nput_ndex, sze(chk_data,2)]); wn2 = gcf; Furthermore, the tranng of 15 ANFIS models, each wth two nputs selected from sx canddates, namely: ANFIS model 1: Cylnder Dsp --> trn=3.9320, chk= ANFIS model 2: Cylnder Power --> trn=3.7364, chk= ANFIS model 3: Cylnder Weght --> trn=3.8741, chk= ANFIS model 4: Cylnder Acceler --> trn=4.3287, chk= ANFIS model 5: Cylnder Year --> trn=3.7129, chk= ANFIS model 6: Dsp Power --> trn=3.8087, chk= ANFIS model 7: Dsp Weght --> trn=4.0271, chk= ANFIS model 8: Dsp Acceler --> trn=4.0782, chk= ANFIS model 9: Dsp Year --> trn=2.9565, chk= ANFIS model 10: Power Weght --> trn=3.9310, chk= ANFIS model 11: Power Acceler --> trn=4.2740, chk= ANFIS model 12: Power Year --> trn=3.3796, chk= ANFIS model 13: Weght Acceler --> trn=4.0875, chk= ANFIS model 14: Weght Year --> trn=2.7657, chk= ANFIS model 15: Acceler Year --> trn=5.6242, chk= Fgure 8 shows the results of the electon of three entres, wth 'Weght', 'Year' and 'Acceler' were chosen as the best three nput varables. It can be seen that the mnmum tranng error (and checks) s not sgnfcantly reduced from 2-nput models best, ndcatng that the addton of new attrbutes 'Acceler' not able to mprove sgnfcantly predcted outcome. For better generalzaton, then t was preferably a model wth a smple structure. Therefore n ths case wll be appled two ANFIS nputs for further exploraton, namely: exhsrch(3, trn_data, chk_data, nput_name); n3 = gcf; Fgure 8. Tranng Error And Checkng Of 15 ANFIS Model Furthermore, traned 20 ANFIS models, each wth 3 nputs selected from sx canddates as follows: ANFIS model 1: Cylnder Dsp Power --> trn=3.4446, chk=

9 ANFIS model 2: Cylnder Dsp Weght --> trn=3.6686, chk= ANFIS model 3: Cylnder Dsp Acceler --> trn=3.6610, chk= ANFIS model 4: Cylnder Dsp Year --> trn=2.5463, chk= ANFIS model 5: Cylnder Power Weght --> trn=3.4797, chk= ANFIS model 6: Cylnder Power Acceler --> trn=3.5432, chk= ANFIS model 7: Cylnder Power Year --> trn=2.6300, chk= ANFIS model 8: Cylnder Weght Acceler --> trn=3.5708, chk= ANFIS model 9: Cylnder Weght Year -- > trn=2.4951, chk= ANFIS model 10: Cylnder Acceler Year --> trn=3.2698, chk= ANFIS model 11: Dsp Power Weght --> trn=3.5879, chk= ANFIS model 12: Dsp Power Acceler --> trn=3.5395, chk= ANFIS model 13: Dsp Power Year --> trn=2.4607, chk= ANFIS model 14: Dsp Weght Acceler --> trn=3.6075, chk= ANFIS model 15: Dsp Weght Year --> trn=2.5617, chk= ANFIS model 16: Dsp Acceler Year --> trn=2.4149, chk= ANFIS model 17: Power Weght Acceler --> trn=3.7884, chk= ANFIS model 18: Power Weght Year --> trn=2.4371, chk= ANFIS model 19: Power Acceler Year --> trn=2.7276, chk= ANFIS model 20: Weght Acceler Year --> trn=2.3603, chk= Fgure 8. Tranng Error And Checkng Of 20 ANFIS Model In Fgure 9 s shown the surface of the nputoutput model of the two-nput ANFIS best for predcton MPG. In the pcture t s shown that the model s nonlnear and monotonous surface, wth the predctable ncrease MPG n the event of an ncrease n 'Weght' dam drop n 'Year'. Value RMSE (root mean squared error) s tranng 2,767 and RMSE check s 2,996. As a comparson, performed by smple lnear regresson usng all the canddates nput produces tranng RMSE s , and the checkng RMSE s Vehcle Fuel Predcton In Fgure 10 has shown the results of predcton of fuel (LPG) vehcles based on weght n klograms (kg). Based on the graph, t s shown that the greater the weght of the vehcle, then the value of MPG (mles per gallon) s gettng smaller. Ths means that the greater the weght of the vehcle, the greater the amount of fuel needed to travel the same dstance. Year car output also affects fuel effcency, e the hgher the output, the hgher fuel effcency. As an example of the predcton of the graph n Fgure 10, for car output n 1970 wth a weght of 2000 kg, the fuel consumpton s 25 mles per gallon, whle the car to output the same year wth a weght of 4000 kg, the fuel consumpton of 10 mle per gallon. In comparson, for the car output n 1982 wth a weght of 2000 kg, the fuel consumpton s 38 mle per gallon, whle the car to output the same year wth a weght of 4500 kg, the fuel consumpton of 22 mle per gallon. 146

10 Fgure 10. MPG Chart Predcted Results Based On The Weght Of Motor Vehcles Fgure 12. Graph The Results Predcted MPG Vehcles Based On Two-Input Exhsrch functon s only ANFIS to tran every sngle epoch n order to fnd the correct nput brefly. Once the nput s set, t wll take longer for ANFIS tranng. Plot n Fgure 11 shows the error curve for 100 epoch ANFIS tranng. Green curve shows the error of tranng, whle the red curve shows the error-checkng. Mnmal error checkng occurs durng epoch 45, characterzed by crcle. Notce that the curve of error checkng to rse after 50 epochs, ndcatng that further tranng wll occur overfttng the data and produce a poor generalzaton. ANFIS chart for two-nputs at a mnmum measurement error s shown n Fgure 12. In the mage has shown nput-output surface. The checkng tranng error and lower than before, but t can be seen some spurous effects at the end of the end surface. On the curve can be stated also that car producton for the year hgher, the greater the weght of the car wll be obtaned by the use of fuels that are relatvely effcent. Ths result contradcts the prevous results as shown n Fgure 10, whch s caused by a shortage amount of data used n tranng. Ths lack of data due to the dffculty of gettng data about the parameters of the car that wll be used n tranng ANFIS. Fgure 13 shows the dstrbuton of the tranng data and checkng ANFIS. Shortcomngs n terms of the amount of tranng data on the top rght corner of the spurous cause ANFIS surface as descrbed earler. Therefore, fuel consumpton predcton usng ANFIS should be nterpreted wth due regard to the dstrbuton of the data as shown n Fgure 13. Fgure 11. Error Curve For 100 Epochs Of ANFIS Tranng 147

11 Fgure 13. Dstrbuton Of Tranng Data And Checkng Of ANFIS 5. CONCLUSION Based on the results of the tranng and checkng ANFIS, the chosen model of a two-nput ANFIS best for predcton MPG,.e. Weght and Year, has a mnmum value of both tranng RMSE and RMSE checkng. The tranng RMSE s 2,767 and checkng RMSE check s 2,996. The greater the weght of the motor vehcle, the greater the amount of fuel needed to travel the same dstance. Year car output also affects fuel effcency,.e. the hgher the output, the hgher fuel effcency. For example predcted results, for car output n 1970 wth a weght of 2000 kg, the fuel consumpton s 25 mles per gallon, whle the car to output the same year wth a weght of 4000 kg, the fuel consumpton of 10 mles per gallon. In comparson, for the car output n 1982 wth a weght of 2000 kg, the fuel consumpton s 38 mles per gallon, whle the car to output the same year wth a weght of 4500 kg, the fuel consumpton of 22 mles per gallon. REFRENCES: [1] R. Syahputra, (2012), Dstrbuted Generaton: State of the Arts dalam Penyedaan Energ Lstrk. LP3M UMY, Yogyakarta, [2] K. Ahn, H. Rakha, A. Tran, M. Van-Aerde, (2001), Estmatng Vehcle Fuel Consumpton and Emssons Based on Instantaneous Speed and Acceleraton Levels, IEEE Pappers, New York. [3] J. Kropwnck, (2002), The Possbltes of Usng of The Engne Multdmensonal Characterstc n Fuel Consumpton Predcton, Journal of KONES Internal Combuston Eng nes 2002 No [4] A. Cappello, I. Chabn, E.K. Nam, (2003), A Statstcal Model of Vehcle Emssons and Fuel Consumpton, MIT Papers, Massachusetts. [5] R. Syahputra, (2013), A Neuro-Fuzzy Approach For the Fault Locaton Estmaton of Unsynchronzed Two-Termnal Transmsson Lnes, IJCSIT, Vol. 5, No. 1, pp [6] A. Jamal, R. Syahputra, (2011), Model Power System Stablzer Berbass Neuro- Fuzzy Adaptf, Jurnal Ilmah Semesta Teknka, Vol. 14, No. 2, , 2011, pp [7] J.S. Wang, C.S.G. Lee, (2002), "Self- Adaptve Neuro-Fuzzy Inference Systems for Classfcaton Applcatons", IEEE Trans. on Fuzzy Systems, 10, 6, Dec, [8] R. Syahputra, (2012), Fuzzy Mult-Objectve Approach for the Improvement of Dstrbuton Network Effcency by Consderng DG, IJCSIT, Vol. 4, No. 2, pp [9] R. Syahputra, I. Roband, M. Ashar, (2012), Reconfguraton of Dstrbuton Network wth DG Usng Fuzzy Mult-objectve Method, Internatonal Conference on Innovaton, Management and Technology Research (ICIMTR), May 21-22, 2012, Melacca, Malaysa. [10] M. Brown, C. Harrs, (1994), Neurofuzzy Adaptve Modellng and Control, Prentce- Hall Internatonal, Inc., UK. [11] J.S.R. Jang, (1993), "ANFIS: Adaptve- Network-based Fuzzy Inference System", IEEE Trans. Syst., Man, Cybern., 23, , June. [12] Kartalopoulos S.V.,1996, Understandng Neural Networks and Fuzzy Logc, IEEE Press, New York. [13] R. Syahputra, I. Roband, M. Ashar, (2014), Dstrbuton Network Effcency Improvement Based on Fuzzy Mult-objectve Method. IPTEK Journal of Proceedngs Seres. 2014; 1(1): pp [14] R. Syahputra, M. Ashar, I. Roband, (2011), Modelng and Smulaton of Wnd Energy Converson System n Dstrbuted Generaton Unts. Internatonal Semnar on Appled Technology, Scence and Arts (APTECS). 2011; pp

12 [15] R. Syahputra, I. Roband, M. Ashar, (2011), Control of Doubly-Fed Inducton Generator n Dstrbuted Generaton UntsUsng Adaptve Neuro-Fuzzy Approach. Internatonal Semnar on Appled Technology, Scence and Arts (APTECS). 2011; pp [16] R. Syahputra, (2012), Dstrbuted Generaton: State of the Arts dalam Penyedaan Energ Lstrk. LP3M UMY, Yogyakarta, [17] H. Afrsal, M. Fars, G.P. Utomo, L. Grezelda, I Soesant, M.F. Andr, (2013), Portable smart sortng and gradng machne for fruts usng computer vson, Proceedng Internatonal Conference on Computer, Control, Informatcs and Its Applcatons: "Recent Challenges n Computer, Control and Informatcs", IC3INA [18] A. Jamal, S. Surpto, R. Syahputra, (2015), Mult-Band Power System Stablzer Model for Power Flow Optmzaton n Order to Improve Power System Stablty, Journal of Theoretcal and Appled Informaton Technology (JATIT), Vol. 80, No. 1, 2015; pp [19] R. Syahputra, I. Roband, M. Ashar, (2014), Optmzaton of Dstrbuton Network Confguraton wth Integraton of Dstrbuted Energy Resources Usng Extended Fuzzy Mult-objectve Method, Internatonal Revew of Electrcal Engneerng (IREE), vol.9, no.3, 2014, pp [20] R. Syahputra, I. Roband, M. Ashar, (2014), Optmal Dstrbuton Network Reconfguraton wth Penetraton of Dstrbuted Energy Resources, n Proceedng of ICITACEE 2014, Semarang, Indonesa. [21] R. Syahputra, I. Roband, M. Ashar, (2014), Performance Analyss of Wnd Turbne as a Dstrbuted Generaton Unt n Dstrbuton System, IJCSIT, Vol. 6, No. 3, pp [22] R. Syahputra, I. Roband, M. Ashar, (2015), Performance Improvement of Radal Dstrbuton Network wth Dstrbuted Generaton Integraton Usng Extended Partcle Swarm Optmzaton Algorthm, Internatonal Revew of Electrcal Engneerng (IREE), vol.10, no.2, pp [23] R. Syahputra, I. Roband, M. Ashar, (2015), Reconfguraton of Dstrbuton Network wth DER Integraton Usng PSO Algorthm, TELKOMNIKA, vol.13, no.3, pp [24] R. Syahputra, I. Roband, M. Ashar, (2015), PSO Based Mult-objectve Optmzaton for Reconfguraton of Radal Dstrbuton Network, Internatonal Journal of Appled Engneerng Research (IJAER), vol.10, no.6, pp [25] I. Soesant, A. Susanto, R. Syahputra, (2015), Applcaton of Partcle Swarm Optmzaton Method for Batk Producton Process, Proceedng of Internatonal Conference on Vocatonal Educaton and Electrcal Engneerng (ICVEE) 2015, pp

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