How can i simulate my trained time series neural network?

My model consists of an (1xInput) input (voltage) and (1xOutput) output (angle of an arm), after training the network and generating the function to simulate the model, it asks me for 2 inputs (2xTs) with their respective delays.

What is this additional input and delay this function is asking for?

function [Y,Xf,Af] = Simular_Brazo(X,Xi,~)
%SIMULAR_BRAZO neural network simulation function.
%
% Auto-generated by MATLAB, 11-Jun-2021 00:52:39.
% 
% [Y,Xf,Af] = Simular_Brazo(X,Xi,~) takes these arguments:
% 
%   X = 2xTS cell, 2 inputs over TS timesteps
%   Each X{1,ts} = 1xQ matrix, input #1 at timestep ts.
%   Each X{2,ts} = 1xQ matrix, input #2 at timestep ts.
% 
%   Xi = 2x11 cell 2, initial 11 input delay states.
%   Each Xi{1,ts} = 1xQ matrix, initial states for input #1.
%   Each Xi{2,ts} = 1xQ matrix, initial states for input #2.
% 
%   Ai = 3x0 cell 3, initial 11 layer delay states.
%   Each Ai{1,ts} = 20xQ matrix, initial states for layer #1.
%   Each Ai{2,ts} = 20xQ matrix, initial states for layer #2.
%   Each Ai{3,ts} = 1xQ matrix, initial states for layer #3.
% 
% and returns:
%   Y = 1xTS cell of 2 outputs over TS timesteps.
%   Each Y{1,ts} = 1xQ matrix, output #1 at timestep ts.
% 
%   Xf = 2x11 cell 2, final 11 input delay states.
%   Each Xf{1,ts} = 1xQ matrix, final states for input #1.
%   Each Xf{2,ts} = 1xQ matrix, final states for input #2.
% 
%   Af = 3x0 cell 3, final 0 layer delay states.
%   Each Af{1ts} = 20xQ matrix, final states for layer #1.
%   Each Af{2ts} = 20xQ matrix, final states for layer #2.
%   Each Af{3ts} = 1xQ matrix, final states for layer #3.
% 
% where Q is number of samples (or series) and TS is the number of timesteps.

%#ok<*RPMT0>

% ===== NEURAL NETWORK CONSTANTS =====

% Input 1
x1_step1.xoffset = -4;
x1_step1.gain = 0.250000000000001;
x1_step1.ymin = -1;

% Input 2
x2_step1.xoffset = -1.22568411949813;
x2_step1.gain = 0.882802087508233;
x2_step1.ymin = -1;

% Layer 1
b1 = [0.00079290479943929263393;0.1305572645190053449;-0.016716733883341550193;0.039441836090246901181;-0.0082309933022506661521;0.0075823868718269569686;0.0047321385856114520085;0.057033707788083587431;0.14737031574700668046;3.2286947369939314836e-05;-0.070775393047497317522;0.1476821968233786031;0.021780475237721266812;-0.0056876548691055508686;0.013009287579592191536;-0.00077692643760469168139;-0.012256229152082494596;0.0035434151785441380875;-0.023530045312407599223;0.013971772563190151123];
IW1_1 = [0.00067087380741671952863 0.00020704268333290612946 -5.1237304984196559219e-05 -0.00015265340676941235995 -5.1193127640646874635e-06 -0.00019962850103916280286 -4.7455494842273642083e-05 3.7574419234254912738e-05 -9.7731743063562574519e-05 -0.00027191800431793902628 -0.00034344099757131264262 2.7634214563509117667e-06;-0.017373887646530288692 -0.037591437506898528476 -0.014774103732526569152 -0.024478814095779035526 -0.015803729389895555812 -0.019303957973309027185 -0.020826207593853163919 -0.015070634356991040315 -0.0085043855076501612134 -0.0057531534064110441407 -0.0010225964729650858977 0.0017033496283741405338;0.0044518081916348015842 0.014981716980332111452 -0.0028922130953937742921 0.012244063255659827852 0.0010482020463426354896 0.013242033944280412819 0.018690671113994358071 0.0081795722766231093726 0.0013497194188876158755 0.01549095481851686662 0.017943322087871412668 0.026926077407902735544;0.0045368650120229331649 0.018910942237425360651 0.012800505562106389429 -0.0087928087551139653272 0.0080318663722130038268 0.0037612637240247398344 -0.0062078747018898235502 -0.0249768084043917038 -0.010814879129776485897 -0.020079278191379185819 -0.027482578765991513836 -0.037485813305203410928;-0.0054544524557494176303 -0.0017987876253453747005 -0.0087160702634079701562 -0.019133242932411247128 -0.016670732229097970367 -0.010630861153091024363 -0.0023184574868607863915 0.0081282870271839214454 0.0055253072291077641454 0.017396827611438965555 0.013334769842838680581 0.017925735334431876222;-0.00010976111668376994088 0.0077914942902010076928 -0.0043835824101325845947 0.0075247348207965946906 -0.002653045968494908112 0.0047587042775379170129 0.0061116087863617458292 0.0044576744902853210339 -0.0015709410742585337446 0.009869618131584363338 0.0059572112723254916009 0.0074906183072620856692;0.004533400727937068192 0.0070985723660795755491 0.00032874713667265230985 -0.0096846558053707062358 0.0098964086740860133379 0.0033014066000058451406 0.00057669795766027338608 -0.0025152652748266089884 0.0077280725334721855901 -0.0097729235507360238261 -0.0059944136697777676212 -0.011862828855404884687;0.00049598232842833786613 -0.0026155785470582866049 0.0044222605801983502077 -0.0017820910136253976554 0.0003711006528492402385 5.0767182862341063518e-05 -0.0031880320565259544219 -0.0029543651612382228894 -0.0038356474677841913452 -0.0069892182265558931362 -0.0056682299768617879637 -0.008398439833632934115;0.013516097473595597989 0.01676033128032176564 0.032470328910678886158 0.016440540760665842829 0.022410132883738168724 0.0048414560748075534391 0.00049543272677563844619 -0.012518765012257697736 -0.0018253580468546408422 -0.027681157085400389523 -0.04106796450577419183 -0.04062363486169801724;3.7045873624562301667e-05 -0.00030747056400846201323 -0.00047187611328028096838 0.00038877874005842534555 0.00011090689932541480923 0.00083065426676503281277 0.0008177927674100383135 0.0019098944764453945101 6.1115948106653408237e-05 6.0779865879660922206e-05 -0.0013618561137289529519 -0.0013374541627485182944;-0.022452152502154997665 -0.022739665167637836823 -0.0081350775921706502958 -0.000504823542302899257 -0.0052578629357218839568 -0.0090274365642551944455 -0.0083843094751372901352 0.0048070586625206385145 0.013145161725628856306 0.010763805483768760715 0.007122874123258585155 0.0082160816068455912176;0.0025223241350361435588 0.0056259723481128197375 0.00078231042168743403133 0.0048208420140157049252 -0.00018590529758185441139 0.0049572105678022627614 0.0075779065278420250015 0.0043942484540687165584 0.0005375956320557277214 0.0062380359825682995742 0.0086497408237912413315 0.011745610283559337547;-0.0090387800748789465244 -0.0052027049586229603614 -0.011453636793412963479 0.013437972200899943667 -0.0084895426939029863811 -0.0012675703758645792425 0.0014020595755194673569 -0.0012329927594266465972 -0.0075109174632013971443 0.0139938298485361972 0.0022793793673502441878 0.013181853727888099073;-0.0041610364288221981383 -0.0079892534360357543577 -0.0011790621662487871246 0.010919052722943026895 -0.01257885664415666141 -0.0045245579351171428054 -0.00081297959023069604756 0.0032328740088762536291 -0.0093872487970512886318 0.012224135853640015134 0.0072940635193692081664 0.014621644306201464839;0.0045428100396301442088 0.0048166175204796650433 0.0031096464882440651677 0.0033042227439594763772 0.0039830725985686007626 0.0079199439924137766261 0.011647197266338230742 0.0023670010019179814863 0.0024625881617194472252 0.0038056732714617964863 0.011593762594883748676 0.019365587296219521213;0.0086983588233284642177 0.014866417850628829353 3.3703547331992620936e-05 -0.003053749649214820925 0.0043474162492943904668 0.011836236307288248645 0.0047923346941034037499 -0.006627246557641990575 -0.0069041699678766398768 -0.0021622747217304770853 -0.0015134817476713704597 -0.0016028929805426879191;-0.016840659964406635291 -0.0032962218305700070886 0.0011208719504524219078 0.019368176836573901267 -5.0671368566499995511e-05 -0.0070154159221482081596 -0.011915883555977077254 0.011119446839393710633 0.0036566625371444168129 0.010576848763413141535 0.0085211816436206455627 0.011348951821814123181;-0.024885962699057347786 -0.036775830987659872962 0.002928554403860402465 0.0086096295343036696041 -0.012609150119944127072 -0.031627777100218863737 -0.013086875015933027017 0.01369521021556849294 0.018477884829170981851 0.0062576235430953277422 0.0068771527382507835133 0.0070271037527190015337;-0.0042789646121386658295 -0.015781329773969392238 0.0032276153663072675894 -0.013906887569234348614 -0.00013201702064709092306 -0.014619485304668406594 -0.020184057821737804578 -0.0087597901538768348229 -0.00022716207331355506055 -0.017751314279948741948 -0.0197233582069421369 -0.029551609287150781835;-0.0037213020965323057032 -0.012491247628477688755 0.0024247396154484920569 -0.010245974223954011292 -0.00089810894208567542199 -0.011101816892655800434 -0.015646744554204828487 -0.0068378464172021251322 -0.0011095554878170911083 -0.012917154050074063087 -0.014978289952937033636 -0.022516478250749712153];
IW1_2 = [0.55819047950489097953 -0.25835362435538705705 -0.38229556713200130158 -0.19640836501062594599 0.0015970407074585080999 0.094331609693016960083 0.10023037260088688005 0.068880860647868413782 0.030516158883139660185 0.003210020002340246547 -0.0034013609845095879765 -0.016001448073560620849;0.2778347461958575737 0.16182254108607566079 0.083240819988889117043 0.028855365133037581826 -0.011770989597731247242 -0.043389079171217995179 -0.066635399116362181715 -0.08124561004999231173 -0.087474843988109521331 -0.085834248438428420447 -0.076995533346333203117 -0.062811462168614565216;-0.86077289662048594998 0.03433511991241921385 0.25402822195639951364 0.14887752667580936294 0.0013696929799426440025 -0.066661120653443983741 -0.061673971811587721681 -0.026093100598727891198 0.0098253320809339635133 0.025521927634210028052 0.010668801353949227892 -0.0098326752884499730306;0.025098105735092177127 0.045315648675939193546 0.052630176629037898894 0.05341908628707431328 0.052536176993394176349 0.051842765386156917928 0.051070864734316753775 0.049492074603300094393 0.046672960582551777098 0.042331454435614854215 0.03631030517800337698 0.029085504046998628819;-0.039899272231535991284 -0.028325739972006522965 -0.018560691035643506197 -0.01029899004905178439 -0.0032626115712373442157 0.0027565974433586453232 0.0079061571310427344178 0.012299441123570949846 0.016050159754098371895 0.019261928220796441236 0.022016167980875217536 0.024402260552443145319;0.4181705712981658718 -0.048980200403416454535 -0.13618339990643657522 -0.061652222768414678689 0.014063812022327730131 0.036251382993527994647 0.022573298980589212581 0.0013162535856828356477 -0.014101504759702614133 -0.016655107087873894517 -0.0015434231241832387094 0.015925833431585965233;0.32535092839018059374 -0.083180388557079973366 -0.16000687356313703091 -0.088785882013479991426 -0.0090695535601236203921 0.026579623989247638804 0.02807335834478053016 0.016913499735438642518 0.003339924567493897118 -0.0079512911526278748364 -0.014658799326808103283 -0.030803295605259087275;0.19415346403057545666 0.090163474726661391179 0.035417954510274435309 0.010816491001625924348 0.0005184303874438076767 -0.0037338017423854453526 -0.0041364678973755146171 -0.0010702647439567981001 0.0046493900066422845854 0.011883180025522879081 0.019193594550672998106 0.023542798521702035935;0.025816633296384779855 0.013430809419343969421 0.015915559979374542532 0.025862459522841906023 0.037762658164269602423 0.049691750179670694798 0.062225733656654257098 0.076405193080354247281 0.092875604473002568162 0.1120908688919898949 0.13437268730516022108 0.15929125268704927287;-0.10590086961420291933 -0.0072289403462536998626 0.033015540526434759838 0.040047284909522487539 0.035177688187915258367 0.028772279448034902144 0.022151285784698132586 0.013925450540813994957 0.0035460022633556484081 -0.0087502563831204603628 -0.022059848295843779986 -0.033052375166197710266;0.026790060702024155043 0.021750317368652367228 0.016662614571852564505 0.011534260693562892186 0.0063712361080244077349 0.0011795238383897190069 -0.0040332411269656323616 -0.0092640522832469675979 -0.014504787074368433175 -0.019750224561507892518 -0.024993577688282121729 -0.03022946068141021958;0.93297183744227640823 -0.18515028757339854759 -0.36700973404400699662 -0.15589470794045304225 0.048648292734782078162 0.11157934145434710016 0.079502834295105392903 0.025459926018281498622 -0.016594400764227159456 -0.030262726196545717411 -0.0047558780432243900546 0.023193924101602300669;0.20937910375025348131 -0.024222153922172209861 -0.069869209460289918856 -0.032714291910488746407 0.0083793681957540343958 0.024217591815281068468 0.020590900869745593776 0.0093644430782017663156 -0.0042945755694623393914 -0.018405096917498996179 -0.032134117656396066209 -0.053755560863843657171;-0.10464146861060850957 -0.046707100423262107314 -0.012115365660731826358 0.0069101383184794256884 0.017165993819138993665 0.022898309355206283161 0.02588300691516328203 0.026435863821520574207 0.024330992956220962725 0.019307955056202953659 0.011547952180319219842 0.0021318609155130457608;0.39783271734844122269 0.056162719695695216549 -0.032889277050983989348 -0.0091919584212236914167 0.023093117126579815884 0.026654047461689200915 0.010729873649700917213 -0.0077656275204019721559 -0.020772574885314795679 -0.024230594935941384555 -0.015638285544730162052 -0.0053877832840703342512;-0.08067231617008115141 -0.022383492001252159459 -3.969980919353941894e-05 0.0032041477657139189918 0.0014356506851120158508 0.0014212520677454104287 0.0037572089516588291595 0.0070941637207253558164 0.010480313463384398343 0.01340241891379226509 0.015918117840496646964 0.01983878263638181802;0.11166191060362271048 0.076677185821643775721 0.048938063751903161658 0.02754626185929147375 0.011585053244755109947 0.00023382416813345048372 -0.0071799847836911522697 -0.011242288401584520433 -0.012488056516001799431 -0.011431672897282351212 -0.0085774151648385747737 -0.0044431468779653465057;0.11107542660186128791 0.076268185946908761963 0.047465385887961439315 0.024096607839402189172 0.0055312930303592251768 -0.0088478401509631595528 -0.019596441799540117307 -0.027180765186549313284 -0.03200898623996706327 -0.034462335759566239957 -0.034909084390596964442 -0.033709059481565266381;-0.91295893562135277399 -0.071238598317655540959 0.1713384712442283242 0.11583790555242741427 0.0074087762841915641085 -0.043895854105501654063 -0.038219739241290705267 -0.0091236074012077932743 0.018534617746230157287 0.028737629626655020187 0.015215135941541542072 0.0018630944762309730942;0.77523927504933443089 -0.089311959391425957411 -0.2511713167421353865 -0.11346723327293074812 0.02804229539156912962 0.071975703911121985401 0.050095483850377757473 0.014090130608378369573 -0.012793941845960939835 -0.02090938785981938039 -0.0050056949016976867717 0.0048814681792059789675];

% Layer 2
b2 = [-0.016390303154156921767;-1.5477097225984222644e-05;-0.28650785495161118499;-0.062044619438524441224;-0.15650632033417130606;0.04437249050947754403;-5.992651310544273313e-07;-0.0009207784960539462096;0.0030365017159792104251;-6.1546161319474511167e-07;-1.1054567756868616547e-05;-0.0099346412998198817268;-0.11204011690680967805;-0.01227127829185436364;-0.047981159256041051864;-0.014420546311961823793;-3.278419512285961262e-07;-2.3172548649264364311e-05;0.0025322237681223839535;-0.19770619660115751337];
LW2_1 = [-0.099558500043941081104 0.035967612305274010431 0.51418169434681015861 -0.020396630991509852315 -0.0010441973886391626991 0.017192994884868816746 0.023799731112069118927 -0.0020265795034358105413 0.026234330955140449521 0.033704448928870804625 0.0014645544139642136753 0.070944255203357065032 -0.018384990141536782654 0.042849766076749173926 -0.064148354836286933245 0.013398487258937523056 -0.00050054718726678525294 -0.0054174310410576784203 0.40247299552590193317 -0.045999485446605072048;1.1076192273595847194e-05 2.0811293553575004108e-07 7.6999286780580994898e-06 2.7569930893796230692e-05 1.5683182264758873834e-05 -2.2134901394235333925e-06 -1.4660405388222975411e-05 -6.1726733140892609352e-07 4.6410277326009897014e-06 4.5762673456675452293e-07 -5.2633343060306962666e-06 -3.7841211733059130953e-05 6.4822894081920828021e-06 -1.6190199009033440561e-05 1.1253541036038028931e-06 3.5169783462811964822e-07 -2.6268711627663771959e-05 -2.4858604411254299948e-05 -1.7478876086139117881e-05 8.498541056504621457e-06;-0.001122404110024829765 0.032325817725069892761 -0.040561528377206876317 -0.018865231456984963426 0.008752085008289908416 0.020674363885886278686 0.00094206110284309114468 -0.053067120942136718209 -0.077675563822129006786 -0.013504477403976746994 0.031045281059792564221 -0.18078467941896445015 0.015431478779171222543 -0.0097785457668956091148 0.018862865596065439555 -0.0098527052128740636588 0.065375877216794983648 0.023406458128440249533 -0.0043093824628616887035 -0.025658543256576675506;-0.01354847913290628536 0.054295344037969116291 -0.012305006005834525848 -4.6472371962474835335e-06 -0.010965348868265082857 0.034714786361581768948 0.051653340295860043774 -0.014354414796650023095 -0.058457088920099438412 -0.0033617263949738168845 0.026885405440588858172 -0.081356024488895728464 -0.014644786269334755802 -0.0098950487155872665634 -0.035060584357226580454 -0.010548560330221954176 0.029938216130204631932 0.046556139439205349029 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0.10158655985472364591 -0.018510627765228271358 0.15647186765444859313 0.013642482014018309996 -0.0036653993109692670042 -0.0038146815959646906795 0.020284565083950042291 0.49123951549529082383;-6.1567909658072236853e-07 -3.0396949141305439383e-07 -1.3044587567402104024e-07 2.8671974632885391866e-07 6.3182985889021662071e-07 -1.2796160964979396926e-07 -4.3010073442074819362e-07 -2.9800768449378693171e-07 -1.761410294619421644e-07 -9.854368240074049557e-08 4.5861485552149688834e-07 -3.4479703922532636416e-08 2.3514318781218071126e-07 7.0473364180770961212e-08 -1.8631338687799068405e-07 6.0118819352008501423e-07 4.3362510077316316311e-07 -5.8156289146488093657e-07 -5.6367104141064886051e-07 -4.5347706049620189025e-07;0.080036032894200442867 0.016063636483192249294 -0.0018428383364870635445 0.0032505475363719007857 0.01190460643503858662 0.024499495834457471477 -0.0093714412036894900737 -0.016401049524691336395 -0.018334376195832443462 0.019905713355498108608 -0.0017725817061005125479 0.037030712433518631066 0.012211182251807499444 -0.0051378788649590646787 0.049284453012519720272 0.020000964448482570074 -0.012428349113382515176 0.021009061617543400846 0.00021811847722860114741 0.047392977632714113068;-0.073473339141255200602 0.0203068022037760261 0.032379153453323511469 0.00044099361675503634915 -0.016407870472787862426 -0.0070572648598355453703 -0.0096611321090328185907 0.011923938796628241049 0.018340957384659770796 -0.010153688187465995107 0.012110321951213554384 -0.053233486891486027359 0.0012638019594763316624 0.0089445205087575474501 0.03901767918453141748 -0.0043774280551153793226 0.035121073787848476611 0.004798125145781757632 0.072943912217176637425 -0.039084612665055043501;-0.0046704839427934158325 -0.027011539702675668728 -0.039700065364220854525 -0.027358907919895997934 -0.0026782926710473727028 0.010097795050142584217 -0.077449173025366621625 -0.072874118982869953709 -0.17686552064120900085 -0.015851271992030808305 0.0124262270679488529 -0.098023194137702898687 0.011821224079840660093 -0.028214540141599558226 -0.022746105402460199685 -0.024392594278378882527 0.066134539050817744976 -0.00068792097665005462557 0.012650067200509935994 -0.11820376456966849577];

% Layer 3
b3 = -0.1665239426782349208;
LW3_2 = [-0.63244335551487773639 -0.00011181392839542574416 0.033827315169474792 0.068136394960732774018 -0.016783166129040766323 0.42620205782335257583 -5.4306974522404633554e-07 -0.00065773877942655145472 -0.001828895870350139485 -4.9463322654984056403e-07 -6.2551692617768522419e-05 0.61292070690785116494 -0.91711922143534374552 -0.14213569272591969339 -0.39461505674065439075 0.74569159197196810407 -2.4385146757820176701e-07 0.091818406659224485744 -0.11173343929298673594 -0.036661985627214108141];

% Output 1
y1_step1.ymin = -1;
y1_step1.gain = 0.882802087508233;
y1_step1.xoffset = -1.22568411949813;

% ===== SIMULATION ========

% Format Input Arguments
isCellX = iscell(X);
if ~isCellX
  X = {X};
end
if (nargin < 2), error('Initial input states Xi argument needed.'); end

% Dimensions
TS = size(X,2); % timesteps
if ~isempty(X)
  Q = size(X{1},2); % samples/series
elseif ~isempty(Xi)
  Q = size(Xi{1},2);
else
  Q = 0;
end

% Input 1 Delay States
Xd1 = cell(1,12);
for ts=1:11
    Xd1{ts} = mapminmax_apply(Xi{1,ts},x1_step1);
end

% Input 2 Delay States
Xd2 = cell(1,12);
for ts=1:11
    Xd2{ts} = mapminmax_apply(Xi{2,ts},x2_step1);
end

% Allocate Outputs
Y = cell(1,TS);

% Time loop
for ts=1:TS

      % Rotating delay state position
      xdts = mod(ts+10,12)+1;
    
    % Input 1
    Xd1{xdts} = mapminmax_apply(X{1,ts},x1_step1);
    
    % Input 2
    Xd2{xdts} = mapminmax_apply(X{2,ts},x2_step1);
    
    % Layer 1
    tapdelay1 = cat(1,Xd1{mod(xdts-[0 1 2 3 4 5 6 7 8 9 10 11]-1,12)+1});
    tapdelay2 = cat(1,Xd2{mod(xdts-[0 1 2 3 4 5 6 7 8 9 10 11]-1,12)+1});
    a1 = poslin_apply(repmat(b1,1,Q) + IW1_1*tapdelay1 + IW1_2*tapdelay2);
    
    % Layer 2
    a2 = poslin_apply(repmat(b2,1,Q) + LW2_1*a1);
    
    % Layer 3
    a3 = repmat(b3,1,Q) + LW3_2*a2;
    
    % Output 1
    Y{1,ts} = mapminmax_reverse(a3,y1_step1);
end

% Final Delay States
finalxts = TS+(1: 11);
xits = finalxts(finalxts<=11);
xts = finalxts(finalxts>11)-11;
Xf = [Xi(:,xits) X(:,xts)];
Af = cell(3,0);

% Format Output Arguments
if ~isCellX
  Y = cell2mat(Y);
end
end

% ===== MODULE FUNCTIONS ========

% Map Minimum and Maximum Input Processing Function
function y = mapminmax_apply(x,settings)
  y = bsxfun(@minus,x,settings.xoffset);
  y = bsxfun(@times,y,settings.gain);
  y = bsxfun(@plus,y,settings.ymin);
end

% Linear Positive Transfer Function
function a = poslin_apply(n,~)
  a = max(0,n);
  a(isnan(n)) = nan;
end

% Map Minimum and Maximum Output Reverse-Processing Function
function x = mapminmax_reverse(y,settings)
  x = bsxfun(@minus,y,settings.ymin);
  x = bsxfun(@rdivide,x,settings.gain);
  x = bsxfun(@plus,x,settings.xoffset);
end

Answer 1

[Anshika Chaurasia]

Hi,

Here X is shifted input and Xi is Initial input delay states.

Consider number of delays used is 2. Suppose your input (voltage) is 1x100 and output (angle of arm) is 1x100.

Then X will be 2x98 where 1st row will be elements from input(3:100) and 2nd row will be elements from output(3:100).

The Xi will be 2x2 where 1st row will be elements from input(1:2) and 2nd row will be elements from output(1:2).

The tapped delay lines in the time-series time delay network need to be filled with initial conditions, which requires that part of the original data set be removed and shifted. You can use preparets that uses the network object to determine how to fill the tapped delay lines with initial conditions, and how to shift the data to create the correct inputs and targets to use in training or simulating the network.

Refer to this documentation for more details and example.

Hope it helps!

[Fabián Flores]

Thank you Anshika for answering my question, however, how can I predict outputs without knowing the actual outputs? or at least knowing only the first 12, since in my case I used 12 delays.