Feedforward Neural Network Methodology(Information Science and Statistics)

概率论

原   价:
1442.5
售   价:
1154.00
优惠
平台大促 低至8折优惠
发货周期:外国库房发货,通常付款后3-5周到货
作      者
出  版 社
出版时间
2013年04月23日
装      帧
平装
ISBN
9781475773095
复制
页      码
340
开      本
9.21 x 6.14 x 0.75
语      种
英文
综合评分
暂无评分
我 要 买
- +
库存 50 本
  • 图书详情
  • 目次
  • 买家须知
  • 书评(0)
  • 权威书评(0)
图书简介
The decade prior to publication has seen an explosive growth in com- tational speed and memory and a rapid enrichment in our understa- ing of arti?cial neural networks. These two factors have cooperated to at last provide systems engineers and statisticians with a working, prac- cal, and successful ability to routinely make accurate complex, nonlinear models of such ill-understood phenomena as physical, economic, social, and information-based time series and signals and of the patterns h- den in high-dimensional data. The models are based closely on the data itself and require only little prior understanding of the stochastic mec- nisms underlying these phenomena. Among these models, the feedforward neural networks, also called multilayer perceptrons, have lent themselves to the design of the widest range of successful forecasters, pattern clas- ?ers, controllers, and sensors. In a number of problems in optical character recognition and medical diagnostics, such systems provide state-of-the-art performance and such performance is also expected in speech recognition applications. The successful application of feedforward neural networks to time series forecasting has been multiply demonstrated and quite visibly so in the formation of market funds in which investment decisions are based largely on neural network–based forecasts of performance. The purpose of this monograph, accomplished by exposing the meth- ology driving these developments, is to enable you to engage in these - plications and, by being brought to several research frontiers, to advance the methodology itself.
本书暂无推荐
本书暂无推荐
看了又看
  • 上一个
  • 下一个