Need help with Matlab neural networks

p1=[90000;2;2;2;34;0;0;0;0;0;0;29239;14027;13559;14331;14948;15549;1518;1500;1000;1000;1000;5000];t1=(0);
p2=[50000;2;2;1;37;0;0;0;0;0;0;46990;48233;49291;28314;28959;29547;2000;2019;1200;1100;1069;1000];t2=(0);
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p299=[190000;1;2;2;40;2;2;2;2;2;2;145613;156765;159386;161870;165725;169928;15000;6600;6500;6500;7000;7000];t299=(1);
p300=[20000;2;3;1;41;-1;-1;-1;0;0;0;780;0;732;642;1252;643;0;732;300;1000;500;1000];t300=(1);

input=[p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14 p15 p16 p17 p18 p19 p20 p21 p22 p23 p24 p25 p26 p27 p28 p29 p30 p31 p32 p33 p34 p35 p36 p37 p38 p39 p40 p41 p42 p43 p44 p45 p46 p47 p48 p49 p50 p51 p52 p53 p54 p55 p56 p57 p58 p59 p60 p61 p62 p63 p64 p65 p66 p67 p68 p69 p70 p71 p72 p73 p74 p75 p76 p77 p78 p79 p80 p81 p82 p83 p84 p85 p86 p87 p88 p89 p90 p91 p92 p93 p94 p95 p96 p97 p98 p99 p100 p101 p102 p103 p104 p105 p106 p107 p108 p109 p110 p111 p112 p113 p114 p115 p116 p117 p118 p119 p120 p121 p122 p123 p124 p125 p126 p127 p128 p129 p130 p131 p132 p133 p134 p135 p136 p137 p138 p139 p140 p141 p142 p143 p144 p145 p146 p147 p148 p149 p150 p151 p152 p153 p154 p155 p156 p157 p158 p159 p160 p161 p162 p163 p164 p165 p166 p167 p168 p169 p170 p171 p172 p173 p174 p175 p176 p177 p178 p179 p180 p181 p182 p183 p184 p185 p186 p187 p188 p189 p190 p191 p192 p193 p194 p195 p196 p197 p198 p199 p200 p201 p202 p203 p204 p205 p206 p207 p208 p209 p210 p211 p212 p213 p214 p215 p216 p217 p218 p219 p220 p221 p222 p223 p224 p225 p226 p227 p228 p229 p230 p231 p232 p233 p234 p235 p236 p237 p238 p239 p240 p241 p242 p243 p244 p245 p246 p247 p248 p249 p250 p251 p252 p253 p254 p255 p256 p257 p258 p259 p260 p261 p262 p263 p264 p265 p266 p267 p268 p269 p270 p271 p272 p273 p274 p275 p276 p277 p278 p279 p280 p281 p282 p283 p284 p285 p286 p287 p288 p289 p290 p291 p292 p293 p294 p295 p296 p297 p298 p299 p300];  
target=[t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12 t13 t14 t15 t16 t17 t18 t19 t20 t21 t22 t23 t24 t25 t26 t27 t28 t29 t30 t31 t32 t33 t34 t35 t36 t37 t38 t39 t40 t41 t42 t43 t44 t45 t46 t47 t48 t49 t50 t51 t52 t53 t54 t55 t56 t57 t58 t59 t60 t61 t62 t63 t64 t65 t66 t67 t68 t69 t70 t71 t72 t73 t74 t75 t76 t77 t78 t79 t80 t81 t82 t83 t84 t85 t86 t87 t88 t89 t90 t91 t92 t93 t94 t95 t96 t97 t98 t99 t100 t101 t102 t103 t104 t105 t106 t107 t108 t109 t110 t111 t112 t113 t114 t115 t116 t117 t118 t119 t120 t121 t122 t123 t124 t125 t126 t127 t128 t129 t130 t131 t132 t133 t134 t135 t136 t137 t138 t139 t140 t141 t142 t143 t144 t145 t146 t147 t148 t149 t150 t151 t152 t153 t154 t155 t156 t157 t158 t159 t160 t161 t162 t163 t164 t165 t166 t167 t168 t169 t170 t171 t172 t173 t174 t175 t176 t177 t178 t179 t180 t181 t182 t183 t184 t185 t186 t187 t188 t189 t190 t191 t192 t193 t194 t195 t196 t197 t198 t199 t200 t201 t202 t203 t204 t205 t206 t207 t208 t209 t210 t211 t212 t213 t214 t215 t216 t217 t218 t219 t220 t221 t222 t223 t224 t225 t226 t227 t228 t229 t230 t231 t232 t233 t234 t235 t236 t237 t238 t239 t240 t241 t242 t243 t244 t245 t246 t247 t248 t249 t250 t251 t252 t253 t254 t255 t256 t257 t258 t259 t260 t261 t262 t263 t264 t265 t266 t267 t268 t269 t270 t271 t272 t273 t274 t275 t276 t277 t278 t279 t280 t281 t282 t283 t284 t285 t286 t287 t288 t289 t290 t291 t292 t293 t294 t295 t296 t297 t298 t299 t300];
net=newff(minmax(input),[4,1]);
net=train(net,input,target);
out=sim(net,input);

now how do I test it ???

[Walter Roberson]

You appear to be using a form of newff that has been obsolete for rather some time. Which MATLAB version are you using?

[Ahsan Raza]

2017 I'm a new user but matlab site says that's its old and obsolete but it still works meaning give right result

I'm a lil new in this that way I read code that already been used I just want to now how to test this and see my values are right

also what does perf =1.36 mean

Answer 1

[Greg Heath]

Is this for regression/curvefitting or classification/patternrecognition?

The respective current functions are FITNET and PATTERNNET (special cases of FEEDFORWARDNET).

The obsolete functions NEWFIT and NEWPR are the corresponding special cases of NEWFF.

 [ I N ] = size(input)  = [ ? ? ]
 [ O N ] = size(target) = [ ? ? ]

For regression, the best measure of goodness is the normalized mean-square-error

 0 <= NMSE <= 1:
 error = target-output;
 NMSE = mse(error)/mean(var(target',1))

For classification, the best measure of goodness is the percent error rate ( 0 <= PCTERR <= 100).

Hope this helps.

Thank you for formally accepting my answer

Greg