1) multil-value nerve network
多值神经网络
1.
An automatic identifying system of yawp environment is established by appling multil-value nerve network system and Dempster-shafer theory.
通过声振法采集水泥混凝土路面声频信号,利用MATLAB软件的数据分析和处理功能进行音频信号的频谱及声谱分析,定义频率变量和频率峰态值等状态参数,应用多值神经网络和Dempster-Shafer理论建立了一套噪声环境下的自动识别系统,实现水泥混凝土路面地基脱空的判定。
2) multi-weights neural network
多权值神经网络
1.
In this paper, a novelspeech emotion recognition, that is speech emotion recognition based onmulti-weights neural network (MWNN.
针对这个问题，本文提出了一种新的语音情感识别方法——基于多权值神经网络的语音情感识别。
3) multi weighted value function neural network
多权值函数神经网络
1.
Takes the safe application of the intelligent mobile phone as its carrier,integrates the advantages of PCA and multi weighted value function neural network for face identification,makes use of nonlinear multi weighted value function neural network to refine multi components,and multi weighted value function neural network face identification.
该文以高端智能手机的安全应用为载体,结合主元分析法和多权值函数神经网络在人脸识别中的优势,利用非线性多权值函数神经网络实现多主元提取,以及多权值函数神经网络识别。
2.
This paper takes the safe application of the intelligent mobile phone as its carrier,integrates the advantages of PCA and multi weighted value function neural network for face identification,makes use of nonlinear multi weighted value function neural network to refine multi components and achieve face identification.
以高端智能手机的安全应用为载体,结合主元分析(PCA,Principle Component Analysis)法和多权值函数神经网络在人脸识别中的优势,利用非线性多权值函数神经网络实现多主元提取,以及多权值函数神经网络识别。
3.
This paper takes the safe application of the intelligent mobile phone as its carrier,integrates the advantages of PCA and multi weighted value function neural network for face identification,makes use of nonlinear multi weighted value function neural network to refine multi components,and multi weighted value function neural network face identification.
以高端智能手机的安全应用为载体,结合主元分析(PCA Principle Component Analysis)法和多权值函数神经网络在人脸识别中的优势,利用非线性多权值函数神经网络实现多主元提取,以及多权值函数神经网络识别。
4) mutivalued recurrent network
多值反馈神经网络
6) multiple neural network
多神经网络
1.
Since in practical industrial applications, soft sensors based on static models often lead to low accuracy and poor robustness, a multiple neural network (MNN) model is presented to develop dynamic soft sensors.
本文提出了一种用多神经网络建立动态软测量模型的通用方法 。
2.
The approach to build software sensor model by multiple neural networks (MNN) based on Kmeans clustering method is presented.
采用多神经网络结构建立软测量模型。
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