Avaabe onne www.jocpr.com Journa of Chemca and Pharmaceutca Research, 04, 6(6):8-5 Research Artce ISSN : 0975-784 CODEN(USA) : JCPRC5 Neura networ-based athetcs performance predcton optmzaton mode apped research Le Song, Mnggang Shen, Xuebo Chen and Junsheng Wang Schoo of Eectronc and Informaton Engneerng, Laonng Unversty of Scence and Technoogy, Anshan, Laonng, Chna Anshan Iron and Stee Corporaton, Anshan, Chna ABSTRACT There are many factors affect athetcs competton, so t needs better predcton mode to mae predcton on athete each event performance, the paper just based on such thought, t ntroduces BP neura networ mode, RBF neura networ mode and Eman neura networ mode, after comparng, t gets that BP neura networ of the three has hgher accuracy, n addton, by combnng the three nds of neura networ modes and then ntroduces t nto specfc exampes, t gets that mode after combnng accuracy s hgher than any one nd of snge predcton mode, so t proves such mode s superorty, t w pay mportant drvng roes n neura networ s sports performance predcton deveopment. Key words: neura networ, predcton mode, sports performance, nerve ce INTRODUCTION Accordng to prevous performance, t maes predcton on performance that to be generated, t s generay used to major sportng events, predct future sports compettve eves s partcuar more mportant for athete beng n best tranng eve, so the sports performance predcton becomes more and more mportant, but there are many nds of modern predcton methods, from whch neura networ s more popuar n contemporary predcton and anayss aspect. Regardng sports aspect each nd of events predcted research, formers have made efforts, such as: Wang Hao and others n order to mprove sports performance predcton accuracy, t combnes BP neura networ wth RBF to mae predcton on Lu Xang s performance, and fnay got that mode after combnaton was obvous hgher than snge predcton mode before combnaton; Zhong Wu and others constructed shot speca performance predcton n 004, meanwhe they got that ts accuracy s obvous hgher than mutpe near regresson mode. The paper based on prevous research resuts, t anayzes sports performance nfuence factors, and by three nds of neura networ predcton mode s mutua combnaton to predct sports performance, the resut proves that combned predcton mode s more accurate than snge mode, and shows estabshed combnatve neura networ predcton mode has mportant sgnfcance n sports aspect performance researchng. Three nds of predcton modes formng. BP neura networ theoretca formng In predcton fed, neura networ s one nd of hgher appcatons, from whch the most mportant beongs to forward neura networ that s aso BP neura networ mode, the mode s composed of three nds of forms that are output ayer, nput ayer and hdden ayer such three nds, ts structure s as Fgure show: 8
Le Song et a J. Chem. Pharm. Res., 04, 6(6):8-5 Fgure : BP neura networ structure Though there are no any connectons among them, ther nerve ces are mutua correated. The agorthm earnng process s composed of two drectons that are respectvey forward drecton process and reverse such two propagaton processes, from whch, forward propagaton s: In above formua, represents number of ayers, s expressed by nodes, the nput s the sampe, then: Reverse propagaton: If nput unt node s j, then: = net ω o () j o, and when output j peces of unts o = f ( net ) () o = y () Among them, use j as actua output unt whch s expressed by y. t / t δ = ( y y ) f ( net ) (4) If nput unt node s not j, then: t + + / t δ = δ ω f ( net ) (5) m m mj E ω j = δ o (6) Revse weght: Here: ω j = ω IJ E µ ω j E ω, µ f 0 j = N E K = ωj (7) Among them, the process from nput ayer to hdden ayer and then transfer to output ayer s nformaton forward drecton propagaton, but once end cannot get correspondng output resut, t w automatcay turn to reverse propagaton; one nerve ce s expressed by foowng formua. u = m t = w x t (8) 9
Le Song et a J. Chem. Pharm. Res., 04, 6(6):8-5 y = f u + b ) (9) ( Among them, n near combnaton, nput sgna s output, nerve ce threshod vaue are respectvey usng u and b to express, nput sgna and output sgna are respectvey usng x and y to express, w represents protruded weght, and meanwhe actvated functon s F (), correspondng functon formua s: f ( (0) v) = + e Due to BP neura networ nerve ce does not change, nput end s : v net = x w + x w + L + x w n n () w w,, w In above formua, connecton weght vaue: L n, Input vaue: x x,,, x n L These nerve ces a actvated functons use S type functon, the functon not ony s contnuous but aso can derve. Defne that between 0 and s BP neura networ node vaue, f nput nformaton hasn t arrved at hdden ayer, then the node s 0, so as to avod the faut status, we adopt standardzaton handng wth these orgna data, adopt: Hdden pont nta number vaue can be defned by formua (), that s:, = m = n + a () = 0.4 + 0.n +.54m + 0.77n + 0.5 + 0.5 nm () n, m are the number Among them, n above two formuas, a s a constant, and s a number between and 0, of output and nput nodes. We wor out an nta vaue by formua (), and then sove t graduay, after that, adopt: x ' = x x x x max mn mn (4) Ony need to do normazaton processng. For the mode, tae one sports schoo trpe jump reatve data as exampes references, and et x to be fna 5m runnng-up nstant speed, x to be horzonta speed before tang off, x s maxmum runnng-up speed, x 4 s dstance between startng pont and poston at ths tme when runnng-up arrves at maxmum vaue, y represents trpe jump fna resuts, as foowng Tabe show: 0
Le Song et a J. Chem. Pharm. Res., 04, 6(6):8-5 Tabe : Trpe jump performance ndcator y / m No s s s 4 9.69 9.7 9.6.00 6.0 9.65 9.84 9.84.90 6.0 0.05 0.0 9.8 0.00 6.04 4 0.04 9.74 9.94 4.90 6.05 5 9.90 9.9 9.89.00 6. 6 0.44 0. 0.5.00 6. 7 0.06 0.06 0.0.00 6.5 8 9.74 9.74 9.58 0.00 6.8 9 0.98 9.75 9.94 4.00 6.9 0 9.90 9.76 9.79 4.90 6. 0.0 9.94 9.95.00 6. 0.6 0.0 0..00 6.7 9.78 9.60 9.66.90 6.0 4 9.97 9.98 9.94 0.00 6.40 5 9.9 9.5 9..90 6.40 6 0.00 9.66 9.9.90 6.40 7 9.65 9.65 9.49 0.00 6.4 8 0. 0. 9.96.0 6.44 9 0. 0.06 9.94.00 6.46 0 0. 9.99 9.97.00 6.50 9.76 9.50 9.59.90 6.56 9.87 9.66 9.7 4.90 6.60 0.08 0.0 9.98.00 6.60 4 9.80 9.80 9.80 0.00 6.60 5 9.9 9.89 9.89 4.00 6.68 By utzng prncpa component anayss method, t handes wth above tabe and gets correspondng ndcators feature vaues that are respectvey: λ =.047 λ =0.867 λ =0.075, by above tabe, t can get that space curve s: x 9.94 0.59 x = 9.84 + 0.57 x 9.84 0.567 (5) x, x as nput foatng around, so above three nds of ndcators have great connectons, therefore we can use 4 functon, then y s output, accordng to BP agorthms to tran, ts tranng sampes seect 0 ndcators to mae research, as foowng Tabe and Tabe show: Tabe : Test sampe Ρ x x y Ρ x x 9.7.00 6.0 9.65 0.00 6.0 9.6.90 6.0 0.00.90 6.40 0.0 0.00 6.0 0..0 6.4 4 9.9 4.90 6. 4 0..00 6.44 5 0.06.00 6. 5 0..00 6.46 6 0.0.00 6.8 6 9.78.90 6.50 7 9.9.00 6.7 7 9.87 4.90 6.56 8 9.48 0.00 6.0 8 9.80 0.00 6.60 9 0.6.90 6.0 9 0.08.00 6.60 0 0.8.00 6.0 0 9.9 4.00 6.68 y Tabe : Test sampe actua resut p 4 5 m s 9.75 0.46 0.0 0.08 9.9 p x / ( ) x m 0.00.00.90 4.00 4.90 4 /
Le Song et a J. Chem. Pharm. Res., 04, 6(6):8-5 Before tranng, t shoud frsty mae transformaton, mae standard devaton as and mean as 0 transformaton on varabes n above tabe, and then mae tranng, after tranng for 945 tmes, ts weght threshod vaue, weght can be soved as foowng show : 54.5 5.645 0.4756 0.600.5589 0.846.56 5.447 0.0.0999 0.047 0.085 0.54.747 0.75 5.589 0.05.45.658 0.7994 40.6.676 0.49 0.465 4.7895 40.94.00.965.4789 5.059 0.060 0.76 0.4765 0.85 5..58 4.4.459 0.50 b 0.046 = W = 6.665 W = 7.456 0.456.56.966 0.9 80.5896 0.00 40.4587.0789 0.9087 0.6 6.950 0.0598 4.89 0.4546.6456 0.4064 68.5588 0.980 0.987.9648 5.6969 0.47 4.478 0.478 0.56 0.6889, 4.7 0.4456 4.0987 0.654 7.654 0.56 80.58 0.098.597 0.4789 4.456 0.55 b = ( 6.658) After sovng threshod vaue and weght, nput above Tabe sampe nto neura networ mode, and then get resut as foowng Tabe 4 show: Networ nput m s x / ( ) x / m 4 Tabe 4: Networ output / Output resut p y m Expected output y / m Error /cm 9.74 0.00 6.45 6.7.4 0.4.00 6.08 6.4-5.8 9.8.90 6.6 6.8 -.7 0.08 4.00 6. 6..6 9.94 4.90 6.50 6.5 0.0 By above Tabe 4, we can see that the frst and second test resut have bg dfferences, and fna nd error s zero, so t proves that ots of data s needed to more accuratey deduce predcton resut and then beng more accurate, meanwhe t aso proves that the mode has certan superortes.. RBF neura networ mode The mode smary s composed of above three forms, ts structure s as foowng Fgure show:
Le Song et a J. Chem. Pharm. Res., 04, 6(6):8-5 Fgure :RBF neura networ structure The mode can create a very hgh precse mode, and ts hdden functon s sef seected, so that ts error s then prevous tmes of performance s x, x, L x, output s performance at ths tme. 0, and. Eman neura networ mode The mode s reatve speca, ts structure s composed of four eements that are output ayer, undertae ayer, hdden ayer, nput ayer, and ts output ayer ncudes one nerve ce, nput ayer ncudes sx nerve ces, hdden ayer s defned as seventeen nerve ces, ts structure s as foowng Fgure show: Fgure :Eman neura networ mode Mode combnatons We et f f f respectvey represent above three nds of neura networ predcton mode, and estabsh the three mutua reatons equaton : f = α f + β f + γ f (6) In above formua, coeffcents respectvey represents three nds of predcton modes weghts, t can sove such
Le Song et a J. Chem. Pharm. Res., 04, 6(6):8-5 vaue by estabshng foowng formua that: mn h (7) = ( ) α β γ f = y f f f Among them, n above formua: α + β + γ = s, t 0 α, β, γ (8) So, combnng above formua wth one schoo athetes competton performances, t can sove ther weghts, ther competton performances as foowng Tabe 5show: Tabe 5: Athetes competton performances tabe No. Actua performance No. Actua performance No. Actua performance No. Actua performance.45 8. 5. 5.9. 9.9 6.08 5.0.45 0.0 7.05 54.04 4..4 8. 55.9 5.6.8 9.86 56.89 6..7 40.84 57. 7.75 4.9 4.9 58.04 8.7 5.4 4.7 59.8 9.87 6. 4. 60. 0. 7. 44.96 6.79.0 8.6 45.0 6.8.5 9.97 46.08 6.9.4 0.89 47.65 64.0 4.6.45 48.84 65.95 5..9 49.4 66. 6..78 50.8 67.0 7. 4.4 5.87 So we nput above data nto combned mode, t can sove three nds of modes weghts, that: α =0.46 β =0.4 γ =0.94, thereupon we can get combnaton mode s:. Estabsh optma combnaton predcton mode We et sampe actua vaue use f = 0.46 f + 0.4 f + 0.94 f (9) y to express, predcted vaue use y t =,, L n to express, ts equaton s : t n y a b y = + (0) t = After that, use near regresson method, t can respectvey sove above coeffcents, and then sove ther predcted vaue, ts formua s:. Predcton mode resut and anayss We et y to represent actua performance, and and estabsh mean absoute percentage error(), that: y a b y + t+ = n = + () x to represent predcted performance, from whch =,, L 0, x y = 00% () n y By above formua, nput data and then t can get combnaton mode s predcton fttng vaue, as foowng Tabe 6 4
Le Song et a J. Chem. Pharm. Res., 04, 6(6):8-5 show: Tabe 6: Predcton resut comparson Frst nd of Second nd of Thrd nd of Optma weghtng Optma near Actua networ predcton networ predcton networ predcton combnaton combnaton No. performance Predcted Predcted Predcted Predcted Predcted vaue vaue vaue vaue vaue..05.56.05 0.7896.06.0698.98.0845.066.0546.08.04.5.09..04.45.0754 4.0..86.0908.0948.99 5.8.947.0988.07.98.9858 0.547 0.74 0.565 6.8.057.086.079.0659.0748 0.56 7.9.094.44.9877.9598.74 8..66.068.6.0877.0 9..474.6.0867.078.0676 0.5.04.44.0546.047.0777 By above Tabe 6, we can get three nds of neura networ predcton methods maxmum error vaues that are dfferences between actua resuts and predcted resuts, so that t can get that n three nds of neura networ predcton methods, BP neura networ predcted vaue and actua vaue dfference s mnmum, therefore t proves BP neura networ s a nd of reatve accurate predcton method, and combnaton mode after handng has better effcency n predcton than snge mode. CONCLUSION The paper predcts athetes performances, ther performances predcton s affected by ots of factors, we utze three nds of networ predcton methods to evauate ndvdua predcton that shows BP neura networ has obvous superortes and good predcton effcency, n addton, we combne the three predcton ways, and get optma combnaton pattern after handng, and after optma weghtng, t can et the combnaton mode has much hgher accuracy than snge mode, so the combned predcton mode has feasbtes. REFERENCES [] ZHU Hong-bng, LIU Jan-tong, WANG Gang, etc,. Journa of Capta Coege of Physca Educaton, 00, 5():8-. [] WANG We. Journa of Nanjng Insttute of Physca Educaton, 004, 8(6):85-87. [] ZHAO Yun hong, ZHOU Yao. Chna Sport Scence and Technoogy, 00, 8():9-40. [4] CHEN Lang, SAI Qng-bn. Journa of Capta Coege of Physca Educaton, 006, 8(5):85-88. [5] SONG A-Lng, CHEN Ka. Journa of Capta Coege of Physca Educaton, 00, 5(4):68-69, 6. [6] CHEN Lang, TIAN De-bao. Journa of Capta Coege of Physca Educaton, 006, 8(6):7-8. [7] Tong Lpng, Yuan Janguo.GM(, ).Journa of Shangha Physca Educaton Insttute, 00, 5():44-47. [8] LIU Ja-jn. Journa of Guangzhou Physca Educaton Insttute, 006, 6():54-56. [9] LIU Ja-jn et a. Zhejang Sport Scence, 999, ():60-64. 5