knowledge, the teacher is able to provide the neural network with a desired responsefor that training vector. Indeed, the desired response represents the "optimum" ac-tion to be performed by the neural network. The network parameters are adjustedunder the combined influence of the training vector and the error signal. The errorsignal is defined as the difference between the desired response and the actual re-sponse of the network. This adjustment is carried out iteratively in a step-by-stepfashion with the aim of eventually making the neural network emulate the teacher;the emulation is presumed to be optimum in some statistical sense. In this way,knowledge of the environment available to the teacher is transferred to the neuralnetwork through training and stored in the form of"fixed" synaptic weights, repre-senting long-term memory. When this condition is reached, we may then dispensewith the teacher and let the neural network deal with the environment completelyby itself.
The form of supervised learning we have just described is the basis of error-correction learning. From Fig. 24, we see that the supervised-learning process con-stitutes a closed-loop feedback system, but the unknown environment is outside theloop. As a performance measure for the system, we may think in terms of the mean-square error, or the sum of squared errors over the training sample, defined as a func-tion of the free parameters (i.e., synaptic weights) of the system. This function maybe visualized as a multidimensional error-performance surface, or simply error surface,with the free paiameters as coordinates.The true error surface is averaged over allpossible input-output examples. Any given operation of the system under theteachers supervision is represented as a point on the error surface. For the system toimprove performance over time and therefore learn from the teacher, the operatingpoint has to move down successively toward a minimum point of the error surface;the minimum point may be a local minimum or a global minimum. A supervisedlearning system is able to do this with the useful information it has about the gradient of the error surface corresponding to the current behavior of the system.
《神经网络与机器学习(英文版·第3版)》((加)海金(Haykin
来源:互联网 发布日期:2011-09-20 18:43:17 浏览:22930次
导读:神经网络与机器学习(英文版·第3版)作者:(加)海金(Haykin,S)机械工业出版社出版,京东人工智能图书网购,折扣超低。...
下一篇:没有了...
相关内容
AiLab云推荐
最新资讯
本月热点
热门排行
-
不被“机器狗之父”看好的人形机器人,未来要如何发展?
阅读量:72828
-
国产版达芬奇手术机器人价格跳水,是价格战要来了吗?
阅读量:67694
-
借势智元机器人,富临精工跨界入局人形机器人,准备好了吗?
阅读量:43585
-
实探全球首个核电灯塔工厂,这里有各式各样的机器人 | 碳访
阅读量:41863
-
追光十年|从工业产线到人形智能,上海抢占机器人产业新高地
阅读量:18060
-
傅盛:我不看好双足机器人的商业化
阅读量:13255
推荐内容
- 2024年德国汉诺威畜牧业展览会
- 2024山东国际玻璃工业技术展览会
- 2024沙特利雅得国际工程机械及混凝土展
- 2024沙特利雅得国际建材展Saudi Build
- 2024第29届欧洲法国(巴黎)国际海事防务展
- 2024 沙特国际照明电力能源展
- 第三届世界材料科学与工程研讨会(SMSE 2024)
- 2024第七届中国国际进口博览会(进博会 CIIE)
- 2024年西班牙国际电气电力照明展览会
- 2024中国(余姚)国际塑料博览会暨第二十五届中国塑料博览会
- 2024第三十届哈尔滨现代农业设施设备展 暨哈尔滨种业博览会/哈尔滨农资博览会
- 2024亚太新材料创新应用博览会(APAME2024)
- 2024第28届亚洲国际动力传动与控制技术展览(PTC)
- 2024年中东欧(塞尔维亚)国际能源展
- 2024第24届亚洲国际物流技术与运输系统展览会(CeMAT 亚洲物流展)
- Indomarine2024第七届印尼(雅加达)国际海事防务展
- 2024年意大利博洛尼亚国际农业及园林机械展EIMA International
- 2024年越南河内食品及食品加工包装展Vietfood&ProPack
- 2024第28届俄罗斯(莫斯科)国际军警展
- Indoaerospace2024第八届印尼(雅加达)国际航空航天展
- Indodefence2024第十届印尼(雅加达)国际防务展
- 2024第二届热管理材料技术博览会
- 2024年韩国釜山国际水产博览会
- 2024亚洲电子生产设备暨微电子工业展览会(NEPCON ASIA)
- 2024年美国盐湖城户外运动用品展览会(冬季)
- 2024深圳国际薄膜与胶带展(FILM & TAPE EXPO)
- 2024深圳国际全触与显示展(2024深圳全触展)
- 2024AMTS第二十届深圳国际汽车制造技术与装备及材料展览会
- 2024深圳国际全触与显示展览会
- 2024 深圳国际薄膜与胶带展览会
- 2024 中亚(哈萨克斯坦)照明及智慧城市展
- 第二届国际催化、化学科学与技术大会(ICCST 2024)
- 2024坦桑尼亚造纸包装、生活用纸和卫生用品展览会
- 2024第9届世界石油天然气装备博览会暨采购大会(WOGE2024)
- 2024第十七届厦门国际美业博览会
- 2024年坦桑尼亚造纸、包装、生活用纸 和卫生用品展
- 2024第十八届深圳国际金融博览会(金博会)
- 2024烟台国际能源低碳产业链展览会
- 2024第七届深圳海外置业移民留学展览会
- 2024(第十五届)重庆汽车消费节暨(第五届)房车生活节(CACF)
- 2024(第二十一届)中国西南(昆明)国际汽车博览会暨智能网联及未来出行汽车博览会
- 2024第十六届郑州国际汽车展览会暨新能源智能网联汽车展览会