高阶神经网络 meaning in English
higher order neural network
Examples
- Research on higher - order neural networks
高阶神经网络模型特性研究 - In this algorithm , author used two technology - the higher - order neural network of single _ layer and hierarchy
在这一算法中，作者运用了单层的高阶神经网络和分层方法。 - The detailed works are as follows : the finding patterns problems in the time - series data sequence are described , and a new trend logic expression method is introduced , and its algorithm and experiment result of algorithm are given ; time - scries data are disposed , and using the arctg . slope of line as the sample of pattern recognition , so ignoring the aberrance of pattern in the classified . in addition , a new time - series pattern finding algorithm based on higher - order neural network is put forward
同时给出了本文的具体的工作，主要是：对在时序数据序列中发现模式问题进行了描述，并介绍了一种新的趋势逻辑表示方法，给出了其算法及算法的实验结果；对时序数据进行处理，提出了利用线段的斜率反正切值作为模式识别的样本，从而在分类时忽略模式的畸变；另外，还提出了一个新的基于高阶神经网络的时序模式发现算法。 - The paper studies the existence and global exponential convergence of alomost periodic solutions for high - order neural networks involving variable delay by applying the theory of fixed point and differential inequality technique , some new criteria on the existence and global exponential convergence of almost periodic solutions are obtained
摘要利用不动点理论和微分不等式分析等技巧，研究了变时滞高阶神经网络概周期解存在性与全局指数收敛性，并且给出了一些新的判别准则。 - Comparing with the other time - series pattern finding algorithm , this one has a few of innovations . firstly , improving time - series pattern finding algorithm from the classified accuracy . secondly , ignoring some aberrance of time - series pattern by the high - order neural network
同其他的时序模式发现算法相比较，具有几点创新： 1 )从分类精度的角度来改善时序模式发现算法； 2 )通过高阶神经网络，直接利用网络的特性忽略了时序模式的某些畸变； 3 )利用了分层方法，进一步改善分类精度。