HU Sheng-neng* Resources and Electric Power,Zhengzhou ,China

Transcription

1 do: / Applcaton of new neural network technology n traffc volume predcton Abstract HU Sheng-neng* 1 School of Cvl Engneerng &Communcaton, North Chna Unversty of Water Resources and Electrc Power,Zhengzhou ,Chna In vew of the dsadvantages of tradtonal neural network technology applcaton, neural network ntegraton technology s appled to traffc forecast for the frst tme. Neural network ntegraton s used to study the same problem wth a fnte number of neural networks, and the output of each network s syntheszed, whch sgnfcantly mproves the generalzaton ablty of the learnng system.based on Boostng and Baggng ntegraton method, the neural network ntegraton method s proposed based on dvde and conquer strategy, and dscussed the network weghts allocaton algorthm. Usng these three knds of neural network ntegraton predcton model, the real-tme traffc volume of a certan ntersecton n Zhengzhou cty s predcted, and the result s better than that of sngle neural network forecastng method. The experments show that the neural network ntegraton s better used n traffc forecastng. Keywords: weght, traffc volume predcton, neural network. 1. INTRODUCTION In recent years, many researchers conducted n-depth research on the traffc flow, and proposes some effectve methods, the neural network technology s regarded as a better non model method, has been wdely used(zhang et al., 2013; Lelthaet al., 2014; Park, 2012).Although the lterature (Hornket al., 2015)has proved that only a nonlnear hdden layer feed-forward network can be arbtrary precson approxmaton of arbtrary complexty functon, however, the confguraton and tranng of the network s the NP problem.in practcal applcatons, due to the lack of pror knowledge of the problem, often need to go through a lot of laborous and tme-consumng expermental exploraton, n order to determne the approprate neural network model, algorthm and parameter settngs, the effect depends entrely on the user experence (Kennedy et al., 2013; Krby et al., 2014; Zhuet al., 2008; Zhuet al., 2015), ths wll affect the generalzaton ablty of the network to mprove. Hansen and Salamon put forward a creatve method (network ntegraton neural), whch provdes a smple and feasble soluton for the above problems. The research results show that the neural network ntegraton method s not only easy to use, but also can sgnfcantly mprove the generalzaton ablty of the learnng system wth a very small computatonal cost (Doughetry et al., 2012; Smth et al., 2013; Cornne, 2015). Therefore, the technology has been successfully used n many felds. In ths paper, neural network ntegraton technology s frst ntroduced nto traffc flow predcton, and t s used to predct the real tme traffc flow of agrcultural road - huayuan road ntersecton n Zhengzhou cty. Usng three dfferent ntegraton methods of neural network predcton of real-tme traffc flow, the expermental results show that the accuracy of the model and the predcton results are deal. The neural network ntegraton for real-tme traffc predcton s feasble and effectve, and the predcton than the sngle neural network model s more superor. 75

2 2. NEURAL NETWORK INTEGRATION METHOD At present there s no unform defnton of neural network ntegraton, a wdely accepted defnton s as follows: the ntegraton of neural networks s a problem wth learnng a neural network wth lmted output, ntegrated output n a sample of the nput conssts of the ntegrated neural network n the sample under the jont decson (Zhuet al., 2012). There are two key problems n neural network ntegraton. One s how to generate the ndvdual network, and the two s how to combne the output of multple neural networks. The frst overvew of the famous Boostng and Baggng algorthm to solve the above two problems of the scheme, followed by a dscusson of an ndvdual network s effectve to determne the weght of new method based on ntegraton, fnally put forward a neural network ntegraton method based on dvde and conquer strategy. 2.1 Indvdual network generaton method In the generaton of ntegraton n the ndvdual network, the most classcal and mportant technology s the Boostng and Baggng methods. In the Boostng algorthm, the tranng set of each network s determned by the network performance whch s produced before t. The example of the of the exstng network wll appear n the tranng set of the new network wth great probablty. In ths way, the new network wll be able to handle a very good example of the exstng network s very dffcult (Hansen et al., 2010; Breman, 2012; Slveraet al., 2013). Baggng s smlar to Boostng technology, the bass of whch s repeatable samplng. In ths method, the tranng set of each neural network s randomly selected from the orgnal tranng set. The sze of the tranng set s usually equal to the orgnal tranng set. As a result, some samples of the orgnal tranng set may appear several tmes n the new tranng set, whle others may not appear at once. The Baggng method ncreases the dfference degree of the neural network ntegraton by the repeated selecton of tranng set, whch mproves the generalzaton ablty(ghasem et al., 2010; Despotovcet al, 2012; Kobayakawaet al., 2009).The man dfference between Baggng and Boostng methods s Baggng n the selecton of tranng set s random, the tranng set s ndependent of each other, the ndvdual network parallel generaton; and the Boostng tranng set selecton are not ndependent, choose the tranng set and n front of the learnng effect, the ndvdual network can only be generated sequentally. The theoretcal study of Krogh et al., showed that the larger the dfference of the network ntegraton, the better the effect of ntegraton, and the neural network whch s very smlar to each other may not have a role to mprove the generalzaton ablty of the ntegraton(yakubov, 2009; Bbk, 2007; Luet al., 2016). In the case that the generalzaton of the ntegrated network s kept constant, the of the neural network ntegraton can be reduced effectvely by ncreasng the dfference degree.lterature(wuet al., 2010)uses genetc algorthm to select the neural network whch has a dfferent degree of dfference, so as to form a neural network ntegraton, whch s a better way to select ndvdual network. 2.2 Concluson generatng method The ntegrated output s usually generated by the smple average (equal weght) or weghted average of the output of each network.baggng n the use of smple average, whle the use of Boostng weghted average; on whether the use of weghts, there are dfferent vews. It s consdered that the weghted average can get better generalzaton 76

3 ablty than the smple average. The other s that the optmzaton of weghts wll lead to over- fttng, whch can reduce the generalzaton ablty of the ntegraton. If the equal rght s regarded as a knd of weghted, the ntegraton of ndvdual network results becomes the problem of how to determne the weght. Concluson generaton s a combnaton of predcton, but here, the combnaton of the varous predcton methods are neural network. Therefore, a seres of methods and models to determne the weghts n the combnaton forecastng can be used here. The commonly used methods for solvng the combned forecastng weghts are lnear, nonlnear dynamc programmng and neural networks. In lterature (Chen, 2015), the concept of forecastng method valdty s proposed to predct the accuracy of forecastng method, whch s reasonable. Predcton method based on a combnaton of the maxmum avalablty as the optmzaton objectve, the mathematcal model for solvng the weght coeffcent s a relatvely new method, but the solvng process s very complex, the lterature (Wang, 2014) gves an approxmate method of ths optmzaton model to fnd the optmal soluton. In ths paper, based on the concept of effectve degree, usng a more drect and smple, physcal meanng of the method to determne the weght. The greater the effectveness of an ndvdual network, the hgher the accuracy of the network predcton, the more effectve the network, the larger the weght should be assgned. Consderng the normatve constrants of the weghted coeffcent, the effectveness of the network can be normalzed as a weghted factor. yˆ ( 1,2,, m, t 1,2,, N) Assumng that there are m neural network, t s the respectvelyth neural network to the real of the predctve of y t, so that A t s the th neural network of the accuracy of the sequence, then A t 1 yt yˆ t y t (1) The mean E of the sequence and mean square devaton were 1 E N N t1 1 At, N N t1 ( A t E ) (2) The valdty of the th neural network s defned as the s S E ( 1 ) Assumng that k s the weght of the th neural network, and s wll be normalzed to get the weghted coeffcent k, then (3) k S m S j j1 1,2,, m (4) 77

4 2.3 Neural network ntegraton based on dvde and conquer strategy Based on the classcal Boostng and Baggng method, the author proposes a neural network ntegraton method based on dvde and conquer, accordng to the dvson of the sample space tme characterstcs of traffc flow, each tme the tranng set s used to tran an ndvdual network, can better deal wth an nput space of each sub network to make; the results obtaned by usng the weghted network ntegraton, and accordng to the weght of fuzzy predctve tme calculaton. In order to facltate the descrpton, the orgnal tranng set s dvded nto 2 groups: day and nght, tranng 2 neural network. The day and the nght are 2 fuzzy sets, and ts membershp degree curve s shown n fgure 1, fgure 2. Fgure 1. Membershp degree of fuzzy set n the daytme. Fgure 2. Membershp degree of fuzzy set at nght. Assumng the predcton pont s t, the daytme, nght membershp was f 1 (t)and f 2 (t), the membershp normalzed weght calculaton k f t) / f ( t) f ( ) 1, 2 ( 1 2 t (5) Ths wll gve a sample groupng, neural network learnng method can reduce the scale of network partton, shorten the learnng tme, fast convergence, good learnng ablty. The followng experments show the effectveness of ths approach. 3. EXAMPLE OF TRAFFIC VOLUME PREDICTION As the applcaton of neural network ntegraton, usng Boostng, Baggng and partton ntegrated 3 schemes to predct the real tme traffc flow of agrcultural road - huayuan road ntersecton n Zhengzhou cty.the November 27, h to the ntersecton of traffc flow (a total of 96 data, the samplng tme s 15mn) as the tranng samples.set S={x =1,2,,N} as the ntal tranng, sets=n, N=96 for all tranng samples.the traffc volume data of November 28th s the test sample, and t s ndependent. 78

5 3.1 Neural network ntegraton based on Boostng Usng the back-propagaton learnng algorthm, the maxmum learnng tmes s 25000, the learnng rate of Ir=0. 01, =0. 1 and err goal square s learnng goals. Set the ntal of the network connecton weghts [-1, 1] random number. Usng Boostng technology to tran two networks, all tranng samples of S to tran frst network NN 1, the network structure s 4x4x1 BP net,usng the back propagaton learnng algorthm, the maxmum number of learnng s tmes, learnng rate Ir=0.01, learnng goal s the square goalerr_goal=0.1.set the ntal of the network connecton weghts [-1, 1] random number. After the studyofnn 1, so thats 2 ={x x fttng s greater than 0.10},set all the samples of S 2 as a tranng to tran the network NN 2, set the lower lmt of S 2,M=0.75N,when the concentraton of NN 2 tranng samples s less than M, the number of samples from the fttng s less than 0.10 n the sample set randomly selected to add to the S 2, so that the number of samples contanng S 2 to M.The structure of NN 2 and ts parameters are set to the same as NN 1. The ntegraton results of NN 1 and NN 2 usng weghted average, NN 1 and NN 2 weghts are calculated accordng to the network's effectveness. The ntal weght k 1 =k 2 =0.5,the output of the NN 1 and NN 2 weghted average as the predctve. The effectveness of the two networks s re calculated for each predcted, and then the new k 1 and k 2 are obtaned for the ntegraton of the next output of the two networks. On November 28th 7: 00 ~ 9: 30 tme perod traffc volume forecast results are shown n Table 1, at the same tme, the output of NN 1 and NN 2 for comparson, the relatve of the unt s %. 3.2 Neural network ntegraton based on Baggng Two networks are traned usng the Baggng method.s 1 and S 2 were randomly selected from the ntal sample set S,S 1 =0.75N,S 2 =0.75N,S 1 tranng network s NN 1, S 2 tranng network s NN 2, the NN 1 network structure s 4*4*1 forward to the network, NN 2 network structure s 4*6*1 forward to the network, other parameters settng up the same as above. The results of the ntegrated use of smple average, on November 28th 2: 30 ~ 5: 00 tme perod of traffc volume predcton results and the relatve n table 2. Tme perod Actual Table 1 Boostng neural network ntegraton predcton results NN 1 NN 2 Boostng 7:00~7: :15~7: :30~7: :45~8: :00~8:

6 8:15~8: :30~8: :45~9: :00~9: :15~9: Average relatve /% Root mean square Maxmum relatve /% Mnmum relatve /% Neural network ntegraton based on dvde and conquer Thought to dvde the sample above to ntroduce the use of dvde and rule, by nght and daytme traffc volume of each tranng a network.the two network structures are 4*1*4 forward network, and other parameters are set up as above.nn 1 and NN 2 ntegraton of the results of the weghted average, the weght based on the fuzzy membershp of the predcton pont calculaton, see above.in November 28th 4: 45~7: 15 tme perod traffc volume forecast result and the relatve s shown n table 3. Tme perod Actual Table 2 Baggng neural network ntegraton predcton results NN 1 NN 2 Boostng 2:30~2: :45~3: :00~3: :15~3: :30~3: :45~4: :00~4: :15~4: :30~4: :45~5: Average relatve /% Root mean square Maxmum relatve /% Mnmum relatve /%

7 Tme perod Table 3 Neural network ntegraton predcton results based on dvde and conque Actual NghtNN 1 DayNN 2 Relatv e Dvde and conquer Weght strategy k 1 K 2 7:00~7: :15~7: :30~7: :45~8: :00~8: :15~8: :30~8: :45~9: :00~9: :15~9: Average relatve /% Root mean square Maxmum relatve/% Mnmum relatve /% 4. CONCLUSION From the analyss of the above examples, t s shown that the applcaton of neural network ntegraton technology can acheve better results than the sngle neural network. In practcal applcaton, because t can not be known n advance whch network generalzaton s the smallest, so the neural network ensemble has practcal applcaton. The ntegrated effect of the method s remarkable, even ordnary engneerng engneer who lack the experence of neural computng canoperate properly. Therefore, ths technque s a very effectve method of engneerng neural computaton. The essence of neural network ntegraton method s combnaton forecastng, and t has been proved that the precson of combnaton forecastng s certanly better than that of the combned forecastng model. The lterature (Wuet al., 2010)has shown that the weghted generalzaton of neural network ntegraton s not greater than the average of the neural network generalzaton, namely n any case ntegrated performance can reach or exceed the average performance of each network composed of the ntegrated network. The generalzaton can be reduced by ncreasng the dfference among dfferent networks. Therefore, as far as possble ndependent tranng of neural networks, the use of dfferent tranng sets, network structure, learnng algorthm to generate the dfference between the network that can mprove the accuracy of neural network ensemble predcton. In specfc applcatons, the use of the two smlar network ntegraton structure,but n the neural network based on dvde and conquer strategy ntegraton, can also accordng to the traffc flow peak to non peak to dvde the sample and generate the ndvdual network. In addton, the author makes a comparson between the results of smple 81

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