鍔犳帹瀹炴垬涔嬪叏鏍圝S绁炵粡缃戠粶
Posted 鍔犳帹绉戝闄?/a> 鍔犳
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鍔犳帹瀹炴垬涔嬪叏鏍圝S绁炵粡缃戠粶
鉂?/span>鑴戞満鎺ュ彛鏃朵唬宸茬粡鍒版潵锛熶綔涓轰竾鑳藉垎绫诲櫒锛屽悇绫荤缁忕綉缁滃湪杩戝勾鏈変簡闀胯冻鐨勫彂灞?/p>
姣斿鍗风Н绁炵粡缃戠粶锛屼篃鍦ㄥ浠婄殑鍥剧墖璇嗗埆棰嗗煙鍙栧緱鏈€濂界殑鎴愮哗
浠婂ぉ鐨勭洰鏍囨槸JS鎵嬫挄閫氱敤绁炵粡缃戠粶馃敟锛屾棤浠讳綍搴撲緷璧?/p> 鉂?/span>
绁炵粡缃戠粶姒傝堪
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浜哄伐绁炵粡缃戠粶锛?Artificial Neural Networks锛?绠€鍐欎负ANNs锛変篃绠€绉颁负绁炵粡缃戠粶锛圢Ns锛夋垨绉颁綔杩炴帴妯″瀷锛圕onnectionist Model锛?锛屾槸瀵逛汉鑴戞垨鑷劧绁炵粡缃戠粶锛圢atural Neural Network锛? 銆岃嫢骞层€?/strong>鍩烘湰鐗规€х殑鎶借薄鍜屾ā鎷熴€? -
淇轰滑鐨勭缁忓厓鐪嬩笂鍘诲緢鍘夊 -
鐩墠鍦ㄧ缁忕綉缁滅爺绌舵柟娉曚笂宸插舰鎴愬涓祦娲撅紝鏈€瀵屾湁鎴愭灉鐨勭爺绌跺伐浣滃寘鎷細澶氬眰缃戠粶BP绠楁硶锛孒opfield缃戠粶妯″瀷锛岃嚜閫傚簲鍏辨尟鐞嗚锛岃嚜缁勭粐鐗瑰緛鏄犲皠鐞嗚绛夈€備汉宸ョ缁忕綉缁滄槸鍦ㄧ幇浠g缁忕瀛︾殑鍩虹涓婃彁鍑烘潵鐨勩€傚畠铏界劧鍙嶆槧浜嗕汉鑴戝姛鑳界殑鍩烘湰鐗瑰緛锛屼絾杩滀笉鏄嚜鐒剁缁忕綉缁滅殑閫肩湡鎻忓啓锛岃€屽彧鏄畠鐨勬煇绉嶇畝鍖栨娊璞″拰妯℃嫙銆? -
浠婂ぉ澶ц嚧鐨勫涔犳祦绋嬶紝鏃ュ父闂=>鎰熺煡鏈?>鍚戝墠浼犳挱=>鍚戝悗浼犳挱=>澶氬眰BP绁炵粡缃戠粶->妗堜緥=>灏忕粨 -
涓哄暐瑕佸涔狅紵鏈哄櫒涓€鐩村湪瀛︿範锛屼綔涓轰汉绫昏淇濇寔瀛︿範馃槃
鏃ュ父鏁版嵁鍒嗙被闂
绠€鍗曟劅鍙椾竴涓?/span>
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鍏堢湅涓姩鍥? -
涓婂浘鐨勮繃绋嬶紝灏辨槸閫氳繃鏈哄櫒瀛︿範锛屾渶缁堢‘瀹氫竴缁勫弬鏁帮紝鏈€鍚庡皢2缁勬暟鎹敖鍙兘鍒嗗紑馃槃 -
閫氳繃涓€鏉$洿绾胯繘琛屽垎绫伙紝灏辨槸绾挎€у垎绫诲櫒
鍐嶆潵鐪嬩竴涓粡鍏稿垎绫讳緥瀛?/span>
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閫昏緫鐢佃矾涓垜浠兘瀛﹁繃涓庨棬锛屾垨闂紝涓庨潪闂紝鎴栭潪闂紝寮傛垨闂? -
銆岄澶栬瘽銆?/strong>锛氶棬鐢佃矾褰㈡垚缁勫悎閫昏緫锛岃Е鍙戝櫒缁勫悎褰㈡垚鏃跺簭閫昏緫锛?鑰呭叡鍚屽疄鐜版暟瀛楅€昏緫锛屾渶缁堝舰鎴愪亢浠殑CPU馃挜銆傘€傘€? -
杈撳叆=>杈撳嚭 鐨勭湡鍊艰〃锛屽涓嬪浘 -
闄や簡XOR锛屽叾浠栭€昏緫鎿嶄綔鐪熷€艰〃 閮藉彲浠ヨ涓€鏉$洿绾垮垝鍒嗭紝濡備笅鍥? -
1鏉$洿绾垮熀鏈棷澶╀笅锛屼絾XOR鍛紝鏃犳硶鐢ㄤ竴鏉$洿绾挎悶瀹氾紝閭e簲璇ュ浣曪紵馃樁涓€鏈靛皬涔屼簯锛? -
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绫讳技XOR鍒嗙被鐨勮В鍐虫柟妗?/span>
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鎴戜滑鍙互鐢ㄥ悇绉嶆満鍣ㄥ涔犲畬鎴愶紝绫籜OR鏁版嵁闆嗗垎绫伙紝濡備笅鍥?/p>
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浠婂ぉ瑕佺敤鐨勬槸涓婂浘涓紝 宸︿笅妯″紡-绁炵粡缃戠粶锛屽疄鐜伴潪绾挎€у垎绫诲櫒馃敟
浜哄伐绁炵粡缃戠粶
鎰熺煡鏈?/span>
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鎰熺煡鏈猴紙perceptron锛夋槸鐢辩編鍥藉鑰?Frank Rosenblatt 鍦?1957 骞存彁鍑烘潵鐨勶紝鎰熺煡鏈烘槸绁炵粡缃戠粶锛堟繁搴﹀涔狅級鐨勮捣婧愮畻娉?/p>
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鍙互绠€鍗曠殑璁や负涓€涓劅鐭ユ満灏辨槸涓€涓缁忓厓锛屾帴鍙楄緭鍏ワ紝澶勭悊鍚庤緭鍑猴紝涔熷彲浠ヨ緭鍏ュ埌涓嬩竴涓劅鐭ユ満馃槃
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涓€涓劅鐭ユ満锛岀敱澶氫釜杈撳叆锛屽叾涓紝鍙傛暟w鍙仛鏉冨€煎悜閲?weight)锛宐绉颁负鍋忕疆(bias)锛宎ctivation function鏄縺娲诲嚱鏁帮紝鏈€缁堝緱鍑篛j
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娉ㄦ剰b 鍦ㄤ笂鍥炬病鏈夋槑纭爣娉ㄥ嚭,鍙互璁や负鏄竴涓埅璺?姣忎釜鎰熺煡鏈哄搴?涓猙
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鏁翠釜鎰熺煡鏈猴紝鐞嗚В璧锋潵鏄緢绠€鍗曠殑锛屼粬妯℃嫙浜嗙敓鐗╃缁忓厓鐨勭壒瀹氬姛鑳金煒?/p>
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涓婇潰鐨刦锛坸锛?锛屼綘鍙互绠€鍗曡涓哄氨鏄竴鏉$洿绾匡紝鎵€浠ュ浜庨櫎浜哫OR鐨勯€昏緫闂紝鎴戜滑鍙互閫氳繃1涓劅鐭ユ満杩涜鍒嗙被
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涓嬮潰鎴戜滑浼氱敤JS寤虹珛涓€涓劅鐭ュ崟鍏冪被銆?/p>
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绁炵粡缃戠粶涓昏鏈変笁涓熀鏈绱狅細銆屾潈閲嶃€佸亸缃拰婵€娲诲嚱鏁般€?/strong>锛屾垜浠殑鐩爣灏辨槸璋冩暣杩?涓弬鏁?/p>
婵€娲诲嚱鏁?/span>
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璧烽潪绾挎€ф槧灏勭殑浣滅敤锛屽叾鍙皢绁炵粡鍏冪殑杈撳嚭骞呭害闄愬埗鍦ㄤ竴瀹氳寖鍥村唴锛屼竴鑸檺鍒跺湪锛堬紞1锝?锛夋垨锛?锝?锛変箣闂淬€傛渶甯哥敤鐨勬縺娲诲嚱鏁版槸Sigmoid鍑芥暟銆? -
Sigmoid鍑芥暟鍦ㄤ箣鍓嶆枃绔犱腑鏈夋彁鍙? -
涓嬮潰浠g爜涓垜浠細瀹炵幇锛?绉嶆縺娲诲嚱鏁帮紝鍙婂叾瀵兼暟
澶氬眰鎰熺煡鏈?绁炵粡缃戠粶
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鍏堢湅鍔ㄥ浘 -
涓婂浘鏈?灞傝緭鍏ュ眰锛?灞傞殣钘忓眰锛?灞傝緭鍑哄眰 -
杈撳叆灞傛湁2涓淮搴﹁緭鍏ワ紝杈撳嚭灞傛湁2涓淮搴﹁緭鍑? -
澶ч儴鍒嗙殑鍒嗙被宸ヤ綔锛岀敤2灞傞殣钘忓眰缃戠粶瓒崇煟
姝e悜浼犳挱
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澶氬眰绁炵粡缃戠粶鐨勬鍚戜紶鎾ぇ绾︽槸杩欐牱鐨? -
杈撳叆鐨勬墜鍐欐暟瀛楋紝閫氳繃2灞傦紝闅愯棌灞傚悗锛岃幏寰椾簡 0-9锛?0涓垎绫? -
涓哄暐浼氬緱鍒?-9锛岄偅鏄敱閲岄潰鐨剋 鍙傛暟锛宐鍙傛暟锛?婵€娲诲嚱鏁帮紝纭畾鐨?銆備綘瑕佸仛鐨勫彧鏄箻璧锋潵姹傚拰 -
784鏄粈涔堥锛?8x28锛屾瘡寮犲浘鐗囩殑鍍忕礌涓暟 馃槃锛岃繖閲屼娇鐢ㄤ簡 one-hot鐙儹缂栫爜 -
濡備綍纭畾杩欎簺鍙傛暟鍊煎憿锛熶娇鐢ㄥ弽鍚戜紶鎾紝涔熷氨鏄父璇寸殑瀛︿範锛堣缁冿級
鍙嶅悜浼犳挱
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鎴戜滑灏藉彲鑳界畝鍗曠殑鍙欒堪杩欎釜杩囩▼
澶嶅悎鍑芥暟鐨勫亸瀵?閾惧紡娉曞垯
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涓哄暐瑕佽繖涔堝鏉傦紵 -
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鎴戜滑閫氳繃鍑犵櫨涓婂崈娆$殑寰皟 杩欎簺w 鍜?b 鏉ヨ娈嬪樊锛堝疄闄呭拰棰勬祴鐨勫樊锛夊彉灏? -
鑰岃繖浜涘井璋冿紝鎴戜滑鐢ㄦ暟瀛﹁瑷€灏辨槸姹侺鍑芥暟鐨剋鍋忓 b鍋忓 -
鏁翠釜杩囩▼濡備笅锛? -
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涓婂浘鐨勎?灏辨槸姊害锛屽埆鎼為敊鏄竴涓悜閲忓摝馃槃 -
鐞嗚В杩欎釜杩囩▼锛屽啓鍑轰唬鐮佸苟涓嶉毦馃樁 -
鑰岃繖涓笉鏂井璋冪殑杩囩▼鎴戜滑鍙搴︿笅闄嶆硶锛? -
涓婂浘瀵笿鍑芥暟姹傦紝胃0 鍜?胃1鐨勫亸瀵硷紝姣忔瀵瑰涓弬鏁拌皟涓€鐐圭偣锛屾懜绱㈢潃锛屾壘鍒板鍚堝嚱鏁扮殑鏈€灏忓€?馃挜 -
鏈€鍚庯紝鏍规嵁杩欎簺姊害鍊硷紝鏇存柊w锛宐鍙傛暟璋冩暣鍚庣殑鏂板€硷紝杩涘叆涓嬩竴杞鍚戜紶鎾? -
澶氬眰绁炵粡缃戠粶濂芥瘮涓圭倝锛岃€屾垜浠緱鍒扮殑鍙傛暟濂芥瘮閲戜腹銆備簬鏄疉I宸ョ▼甯堬紝鍙堣鎴愪负鐐间腹甯?
骞茶揣锛屼笂浠g爜
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棣栧厛鏄緟鍔╁嚱鏁?
function activeFunc (x, type = 'relu') {
let lamb = 1.0507009873554804934193349852946
let alpha = 1.6732632423543772848170429916717
let temp = 0
switch (type) {
case 'sign':
return x >= 0 ? 1 : -1
case 'sigmoid':
return 1 / (1 + Math.exp(-x))
case 'tanh':
return (Math.exp(x) - Math.exp(-x)) / (Math.exp(x) + Math.exp(-x))
case 'relu':
return x > 0 ? lamb * x : lamb * alpha * (Math.exp(x) - 1)
case 'diffsigmoid':
x = 1 / (1 + Math.exp(-x))
return x * (1 - x)
case 'difftanh':
x = Math.tanh(x)
return 1 - x ** 2
case 'diffrelu':
return x > 0 ? 1 : alpha * Math.exp(x)
}
}
function mse (realArr = [], predArr = []) {
return (
realArr.reduce((a, b, idx) => a + (b - predArr[idx] || 0) ** 2, 0) /
realArr.length
)
}
//浜ゅ弶鐔垫崯澶卞嚱鏁板閫?/span>
function crossEntropy (a, y) {
return -y * Math.log(a) - (1 - y) * Math.log(1 - a)
}
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3涓父鐢ㄦ縺娲诲嚱鏁板強鍏跺鏁板嚱鏁帮紝鍋氫簡涓€涓?鍚?鐨勫嚱鏁帮紝鐩殑涓轰簡澶勫鍙井锛屽苟鏄緭鍑洪潪绾挎€у寲銆備綘涔熷彲浠ュ垎寮€鍐? -
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mse骞虫柟宸眰鍜岋紝鐩殑涓轰簡璁$畻娈嬪樊锛屾洿濂界殑瑙傚療妯″瀷閫愭鏀舵暃鐨勮繃绋? -
鎰熺煡鏈虹被
class Perceptron {
constructor (inputObj = {}) {
this.config = inputObj
}
set config (o) {
this.type = o.type || 'hidden' // input hidden output 绁炵粡鍏冪殑绫诲瀷 cov pool
this.inputDimNum = o.inputDimNum || 2 //杈撳叆缁村害
this.outputDimNum = o.outputDimNum || 2 //杈撳嚭缁村害
this.inputArr = []
this.beforeOutput = 0
this.output = 0
this.activeFunc = o.activeFunc || 'sigmoid'
this.wb =
o.wb ||
$.math.genRange(1, this.inputDimNum, 0).map(x =>
randMinMax(
-Math.sqrt(1 / this.inputDimNum), //浼犵粺鐨勫垵濮嬪寲绉嶅瓙
Math.sqrt(1 / this.inputDimNum)
)
) //w 鍚戦噺 0.5
if (!o.wb) this.wb.push(1) //b 璁剧疆鎴?nbsp;1
this.wbT = $.math.mat.transpose([this.wb]) // w+b杞疆鐭╅樀
this.derivative = 0 //瀵兼暟鍊?/span>
}
setWb (a) {
this.wb = a
this.wbT = $.math.mat.transpose([this.wb]) // w+b杞疆鐭╅樀
}
predict (inputArr) {
if (inputArr.length < this.inputDimNum) throw new Error('杈撳叆缁村害澶皯')
this.inputArr = inputArr
this.beforeOutput = $.math.mat.mul([[...inputArr, 1]], this.wbT)[0][0]
this.output = activeFunc(this.beforeOutput, this.activeFunc)
return this.output
}
}
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鎰熺煡鏈虹被锛屼富瑕佸疄鐜?鍚戝墠浼犳挱锛屽氨鏄睘鎬у锛屽叾浠栧緢绠€鍗曫煒? -
杩欓噷浣跨敤 predict 鏉ュ疄鐜?鍚戝墠浼犳挱 -
鍚戝悗浼犳挱锛岄渶瑕佷箻浠ヨ宸煩闃碉紝涓庡亸瀵肩殑涔樼Н锛屼綔涓轰笂涓€灞傜殑璇樊鐭╅樀锛屼粠灏鹃儴鎯崇缁忕綉鐨勫ご閮ㄤ紶杈?
function backPropagation (realArr) {
if (typeof realArr === 'number') {
realArr = [realArr]
}
//绗琇琛?/span>
let L = networkArr.length - 1
let deltaArr = []
// 璁$畻鍋忓
for (let i = L; i > 0; i--) {
deltaArr[i] = []
if (i === L) {
for (let d = 0; d < networkArr[i]; d++) {
deltaArr[i].push(
(networkObj[i][d].output - realArr[d]) *
activeFunc(networkObj[i][d].beforeOutput, 'diffsigmoid') //鏈€鍚庝竴灞傜殑璇樊
)
}
} else {
let weightArr = []
for (let k = 0; k < networkArr[i + 1]; k++) {
weightArr.push(networkObj[i + 1][k].wb)
}
let weightArrT = $.math.mat.transpose(weightArr)
for (let d = 0; d < weightArrT.length - 1; d++) {
deltaArr[i][d] = 0
weightArrT[d].map((x, idx) => {
deltaArr[i][d] +=
deltaArr[i + 1][idx] *
x *
activeFunc(networkObj[i][d].beforeOutput, 'diffsigmoid') // 鐭╅樀鐐逛箻
})
}
}
}
//鏇存柊姣忎竴灞傜殑姣忎釜wb鍊?/span>
for (let i = L; i > 0; i--) {
for (let k = 0; k < networkArr[i]; k++) {
let wbArr = networkObj[i][k].inputArr.map((x, idx) => {
return x * deltaArr[i][k]
})
wbArr.push(deltaArr[i][k])
networkObj[i][k].wb.map((x, idx) => {
wbArr[idx] = x - learnRate * wbArr[idx]
})
networkObj[i][k].setWb(wbArr)
}
}
}
-
瀛︿範Train鐨勮繃绋嬶紝闈炲父绠€鍗曟毚鍔?
let epoch = 1e5 //涓栦唬 浣犱篃鍙互璁や负鏄凯浠f鏁?/span>
for (i = 0; i < epoch; i++) {
let mseArr = []
for (let k = 0; k < data.length; k++) { //鏁版嵁琛屾暟
forwardPropagation(data[k]) // 姣忎釜鏁版嵁鐨勭淮搴?/span>
backPropagation(dataTag[k]) // 姣忎釜鏁版嵁鐨勫垎绫绘爣璁?/span>
}
}
-
鍏舵浣犻渶瑕佷竴涓猚reateNetwork鍑芥暟寰幆浜х敓澶氫釜锛屾劅鐭ユ満瀹炰緥銆備唬鐮佸お绠€鍗曞氨缃楀垪浜? -
瀵癸紝杩欏氨瀹屼簡锛屼綘鐨勪腹鐐夊凡缁忔悶瀹氾紝鍙互寮€濮嬬偧涓逛簡銆傪煍? -
浠ヤ笂鐭╅樀鍩烘湰鎿嶄綔锛屽皝瑁呭湪meeko鐨勪唬鏁版牳蹇冨簱涓€?
瀹炰緥鎵ц
XOR鍒嗙被
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xor鏃犳硶閫氳繃鏅€氬洖褰掕繘琛屽垎鍓诧紝鎴戜滑鐪嬬湅鑷繁鐨勪腹鐐夋晥鏋? -
2,2,2 1涓殣灞傜綉缁? -
鍦?4485娆¤凯浠e悗锛岃宸獊鐒跺噺灏忥紝杩欎篃鏄ぇ閮ㄥ垎绁炵粡缃戠粶瀛︿範鐨勶紝鐗瑰緛锛岃鑰愬績鐐间腹
-
鏈€鍚庤緭鍑虹殑 灏辨槸閲戜腹浜嗭紝鐩存帴锛屾嬁杩欎簺w 鍜?b 鐨勫弬鏁帮紝灏卞彲浠ョ洿鎺ユ柇瑷€XOR鎿嶄綔浜?/p>
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鏈変簡涓硅嵂锛岃緭鍑烘槸鐬棿鐨勩€傝繖涔熸槸澶ч儴鍒嗗悓瀛﹀湪缃戜笂涓嬭浇鍒汉璁粌濂界殑妯″瀷鐨勭敤娉曫煒?/p>
楦㈠熬鑺卞垎绫?span class="mq-443">
-
鎴戜滑浣跨敤涔嬪墠鐨?50涓?楦㈠熬鑺辨暟鎹泦锛屽鑺辩殑鍝佺杩涜鍒嗙被 -
鎴戜滑鎶?绫昏姳锛岃繘琛宱ne-hot缂栫爜 [1,0,0] [0,1,0] [0,0,1] 浠h〃3绫伙紝杈撳叆4缁? -
鍦ㄤ箣鍓嶇殑銆婄壒寰佸伐绋嬫枃绔犱腑銆嬶紝鎴戜滑浣跨敤PCA灏嗛涪灏捐姳鐨勭淮搴︿粠4缁撮檷浣庡埌浜?缁? -
鏈锛屾垜浠厛涓嶉檷缁达紝鐩存帴鏆村姏浣跨敤绁炵粡缃戠粶鍒嗙被馃敟 -
鎴戜滑鍒涘缓涓€涓?10涓缁忓厓鐨勯殣钘忓眰
let networkArr = [4, 10, 3]
networkObj = createNetwork(networkArr)// 寰幆浜х敓澶氬眰澶氫釜鎰熺煡鏈?/span>
-
139绉掑悗锛屾垜浠湪87170娆¤凯浠e悗锛屽緱鍒颁簡婊¤冻闃€鍊糴ps鐨勪腹鑽? -
鎴戜滑璋冩暣鍙傛暟鍒涘缓鏂扮殑绁炵粡缃戠粶锛?涓殣钘忓眰锛?1涓湁10涓缁忓厓鐨勶紝1涓湁3涓缁忓厓
let networkArr = [4, 10, 3, 3] // 杩欎釜缁撴瀯鏄湅鍒癿atlab 楦㈠熬鑺变緥瀛愮敤浜嗚繖涓弬鏁?/span>
networkObj = createNetwork(networkArr)
-
鍥犱负闅忔満鍙傛暟閫犳垚鐨勫樊寮傦紝鏅亶鏉ヨ 4,10,3,3缁撴瀯浼氭瘮 4,10,3 濂戒竴浜涳紝璁粌鏃堕棿闅忕綉缁滃彉澶ц€岃繀閫熷鍔犮€?/p>
-
浣嗗悓鏍风殑epoch锛屽灞傚彲浠ョ敤鏇村鐨?鍙傛暟鍙皟锛屽綋鐒跺浜庝笉鍚岀殑鏁版嵁闆?鎯呭喌鏈夋墍涓嶅悓銆?/p>
-
鍑犳杩愯涓嬫潵锛屼細鍙戠幇缁忓父鏈?涓偣 [ 6, 2.7, 5.1, 1.6 ] 娌℃湁琚纭瘑鍒?/p>
-
鎴戜滑浣滃浘鐪嬩竴涓? -
杩欎釜鐐规槸鍦?涓姳鍝佺涔嬮棿锛屼汉宸ョ瓫閫変篃浼氬嚭鐜伴敊璇€傚お楂樼殑缃戠粶灞傛暟 涓€鏍蜂細閫犳垚杩囨嫙鍚堬紝杩欎釜涓硅嵂锛堝弬鏁帮級锛屾渶缁堟槸闈炲父婊℃剰鐨勩€? -
鐢?84,100,10 杩欐牱鐨勭綉缁滄ā鍨嬶紝鍙互璁粌鎵嬪啓 MINST鏁版嵁闆?鍒?7%+ 鐨勭簿鍑嗙巼锛岃繖鏄潪甯稿ソ鐜╃殑浜嬫儏銆? -
鑷虫鑷缓鐨勫灞傜缁忕綉缁滐紝杩樻槸鏈変竴瀹氱殑绮惧噯鎬х殑馃敟
鉂?/span>娴忚鍣ㄤ腑杩愯涔熸槸濡ュΕ鐨勷煉?/p> 鉂?/span>
杩涗竴姝ュ涔?/span>
-
涓绘祦绁炵粡缃戠粶鍔犲叆浜嗘洿澶氫笉鍚屽姛鑳界殑鑺傜偣锛屼絾涓囧彉涓嶇鍏跺畻
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姣斿甯哥敤鐨勫嵎绉缁忕綉锛屽氨鍔犲叆浜嗭紝鍗风Н灞傚拰姹犲寲灞傪煒?/p>
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鍙傛暟鐨勪紭鍖栵紝涓昏鏈夊姩鎬佸涔犵巼锛屽弬鏁板垵濮嬪寲鏂规硶锛屽綊涓€锛屽姩閲忥紝鏀瑰彉涓嬮檷鏂瑰紡绛?/p>
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瀵逛簬绁炵粡缃戠粶鎴戜滑宸茬粡鐜伴緳鍦ㄧ敯浜?/p>
-
鍏朵粬绫诲瀷鐨勭缁忕綉缁?/p>
鎬荤粨
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浜嗚В閫氱敤浜哄伐绁炵粡缃戠粶鐨勬銆佸弽鍚戜紶鎾? -
浜嗚В澶氬眰绁炵粡缃戠粶锛岀缁忕綉缁滃氨鏄腹鐐夛紝鑾峰緱鐨勫弬鏁板氨鏄噾涓? -
閫氳繃JS瀹炵幇涔嬶紝灏嗙畻娉曠敤鍒板疄闄呬笟鍔″満鏅紝鑷姩鍖栦竴鍒囷紝鍑忓皯浜哄伐 -
閫愭鎺屾彙澶氬眰绁炵粡鍒嗙被鐨勬妧宸? -
浜嗚В鍏朵粬涓绘祦绁炵粡缃戠粶
绁濆ぇ瀹?020鎻愬崌鎶€鏈紝寮€蹇冿紝鑾峰緱閲戜腹锛屽彂璐?馃槃
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