该方案将自由口编程完全采用智能仪表的特定通信协议，既简化网络结构，又节约布线。
The free-port program with special communication protocol of intelligent instrument is used in the scheme, not only network structure is simplified but also wire laying is saved.
功率控制技术是容错拓扑控制中的重要手段，可以简化网络结构，获得可靠连通和高效的底层拓扑。
Transmit power control technology contributes as a fundamental part in fault tolerant topology control, through which reliably-connected and highly efficient topology can be constructed.
结果表明，与传统的基于误差平方和准则的学习算法相比，采用模糊学习算法可以大大简化网络结构，有效提高模拟精度和效率。
The modeling results showed that the introduction of fuzzy learning method can not only simplify the structure of neural network, but also effectively improve the modeling accuracy and efficiency.
然而，要想做到“保持原有网络结构和简化变更设置，同时加强可用性”，又不是一件轻易的事。
However, want to accomplish "to maintain original network structure and simplify modificatory setting, strengthen usability" at the same time, not be an easy thing.
利用改进后的方案实现语音识别、网络结构简化、运算速度加快、训练时间缩短。
By making use of the scheme after improving, it can simplify network structure, accelerate operation, and shorten the training time.
尤其是改进的神经网络分段预测模型具有网络结构简化、收敛速度快，泛化能力强的特点，取得很好的拟合精度和预测效果。
The improving neural network subsection prediction model can take advantage of simple network structure, fast convergence rate and strong generalization capability, and get a good modeling effect.
本论文对某公司销售进行了实例预测研究，证明本文所建立的模型和研究方法是实用而有效的，不仅简化了网络结构，而且提高了预测精度。
The thesis did a experiment on someone comply sales forecasting, proved that the model and research method is effective. It simply the network structure, enhanced the precision.
研究适用于隐马尔可夫模型(HMM)结合多层感知器(mlp)的小词汇量混合语音识别系统的一种简化神经网络结构。
It is applicable to any small vocabulary hybrid speech recognition system that combines hidden Markov model (HMM) with multi-layer perceptron (MLP).
然后应用多权重剪枝策略（MW-OBS）进一步简化、确定网络结构。
The multi-weights-optimal brain surgeon (MW-OBS) algorithm, was included to further prune unimportant weights or nodes.
该模型利用粗糙集理论约简气象影响因素，提取关键因素作为网络的输入，简化了网络结构。
This model was applied to. reduce factors of weather, withdraw essential ingredient as the input of the network, and simplify network structure.
并采用主成分分析法简化神经网络结构，将网络的输入节点数从60维降低到3维。
The PCA reduced the network's input nodes from 60 to 3 to simplify the neural network's structure.
并采用主成分分析法简化神经网络结构，将网络的输入节点数从60维降低到3维。
The PCA reduced the network's input nodes from 60 to 3 to simplify the neural network's structure.
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