A novel FOCAL technique based on BP-ANN

In this paper, the feed-forward back-propagation artificial neural network (BP-ANN) algorithm is introduced in the traditional Focus Calibration using Alignment procedure (FOCAL) technique, and a novel FOCAL technique based on BP-ANN is proposed. The effects of the parameters, such as the number of neurons on the hidden-layer and the number of training epochs, on the measurement accuracy are analyzed in detail. It is proved that the novel FOCAL technique based on BP-ANN is more reliable and it is a better choice for measurement of the image quality parameters. (c) 2005 Elsevier GmbH. All rights reserved.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

1736～1999年西安与汉中地区年冬季平均气温序列重建

基于现代气象资料所建立的西安、汉中地区冬季降雪天数与年冬季平均气温之间的相关关系,根据清代档案所记载的西安与汉中地区冬季降雪日数,恢复了西安与汉中地区1736～1910年的年冬季平均气温,重建了1736～1999年西安与汉中地区年冬季平均气温序列。序列分析表明,这两个地区年冬季平均气温变化趋势一致:18世纪和20世纪为暖期,19世纪为冷期,且20世纪的增暖趋势明显。与利用树轮重建的镇安初春(3～4月)温度序列对比分析表明:西安、汉中地区冬季平均气温与镇安初春温度的低频变化趋势基本一致,但镇安较西安与汉中有

1736年以来长江中下游梅雨变化

利用清代雨雪分寸资料,复原了1736~1911年长江中下游地区的梅雨入出日期、雨期长度,重建了1736年以来长江中下游地区梅雨雨量变化序列,分析了梅雨的变化特征;并根据梅雨期长度与东亚夏季风指数之间的关系,分析了1736年以来东亚夏季风的强弱及与之对应的雨带位置阶段性变化特征.结果表明:长江中下游地区入、出梅日期及雨期长度均存在明显的年-年代际变化,雨期长度除具有2,7~8,20~30及40a的年际与年代际周期外,还具有百年波动的信号.1736年以来,雨期长短、中国东部季风雨带位置移动与东亚夏季风强弱变化