径向搜索方法在电路容差优化设计中的应用

文本采用 R.Spence 的径向搜索方法进行电路的成品率估测及标称点中心设计,并对原方法中的成品率估测公式进行了重要修正,在此基础上通过引入新的概念对公式进行了证明,从而为这种设计方法提供了充足的理论依据.另外,在计算机软件实现过程中,对原迭代方法进行了必要的补充和修改,以保证其精确收敛.最后就某些具体的电路和模型进行了实际设计,并就其设计结果及本方法的特点进行了讨论.     

多层结构神经网络的等误差范围逼近与收缩学习方法及其应用

本文提出了一种用于前馈型多层神经网络学习的等误差范围逼近与收缩学习方法,这种方法仅仅要求网络的实际输出落在理想模式输出的一个事先给定的误差范围之内,从而可以大大提高网络的学习速度,且运算量小,而且通过适当选择等误差范围,它还可以提高网络在模式识别中的推广性能.如果网络用于模式联想等方面时,通过误差范围的逐步收缩,这种方法还可以以很小的额外代价提高网络学习的逼近精度;另外,它还可以避免传统方法中经常出现的训练模式反转等局域极小状态和过学习现象的出现.最后,文中给出了以这种方法训练的网络用于脑电波癫痫信号识别中的实验结果及其分析.     

EQUAL INTERVAL RANGE APPROXIMATION AND EXPANDING LEARNING RULE FOR MULTI-LAYER PERCEPTRONS AND APPLICATIONS

In this paper,we propose an equal interval range approximation and expandinglearning rule for multi-layer perceptrons applied in pattern recognitions.Compared with tra-ditional BP algorithm,this learning rule requires the output activations interval between themaximum target output node and other nodes to exceed a given equal interval range for eachtraining input pattern,thus it can train networks faster in much lower calculation cost andmay avoid the occurrences ot reversed target output and overlearning,hence it can improve thenetwork's generalization abilities in pattern recognitions.Through gradually expanding of theinterval range,this learning rule can also enable the network to learn its targets more accuratelyin less additional training iterations.Finally,we apply this algorithm in network training inEEG detection,and the experimental results have shown the above advantages of the proposedalgorithm.