提出了一种基于免疫识别原理的径向基函数神经网络学习算法。
Radial Basis Function (RBF) neural network learning algorithm based on immune recognition principle is proposed.
提出了一种基于免疫识别原理的径向基函数神经网络学习算法。
A Radial Basis Function (RBF) neural network learning algorithm based on immune recognition principle is proposed.
提出了一种基于免疫识别原理的径向基函数神经网络学习算法。
A Radial Basis Function (RBF) neural network learning algorithm based on immune recognition principle is studied.
本文提出了基于神经网络学习算法的一种新的胃电信号自适应时频分析方法。
A novel adaptive time frequency analyzing methodology based on neural network is presented in this paper.
本文根据自组织特征映射神经网络学习算法，提出了其权值的CMOS实现电路。
According to a learning algorithm of self organizing neural network for mapping character, a CMOS implementation of its synaptic weight by circuit is presented in this paper.
如何找到一种更加行之有效的RBF神经网络学习算法具有重要的理论意义和应用价值。
It has important theoretical significance and application value how to find an effective learning algorithm of RBF neural networks.
提出一个基宽度可调的RBF神经网络学习算法，并将它应用于个股走势模式的分类问题。
This paper presents a learning algorithm for a RBF neural network with adjustable radial basis width and discusses its application in the classification problem of share tendency patterns.
该方法用幂级数多项式拟合传感器的非线性模型，多项式的系数可由神经网络学习算法得到。
The response of the sensor is expressed in terms of its output by a power series. The coefficients of the power series can be learned and determined by a simple neural algorithm.
提出了基于改进的BP神经网络学习算法和自适应残差补偿算法的炼铜转炉吹炼终点组合预报模型。
It is the first time that a converting furnace endpoint prediction model based on an improved BP neural network and error compensation of linear regression.
阐述了神经网络学习算法，设计了高速公路可变速度标志神经网络控制器，并对控制器进行了仿真研究。
The neural network algorithm is formulated and the controller is designed. Simulation research is carried out by taking full advantage of a computer.
介绍了一种新的非参数回归RBF神经网络学习算法，该算法将R BF神经网络与回归树结合起来使用。
A new nonparametric regression learning algorithm for RBF neural network is presented. It is a novel method involving a combination between regression trees and RBF networks.
针对BP(反向传播)神经网络学习易陷入局部极小的缺陷，提出了一种改进BP神经网络学习算法——RMBP算法。
A modified BP algorithm of neural network, random adjustment of parameters (RMBP) algorithm, is proposed to overcome the defect of easy going into local minimum of BP neural network.
本文运用卡尔曼滤波原理，提出了一种新的神经网络学习算法。该算法的学习速度是由带时间参数的里卡蒂微分方程来确定的。
In this paper, making use of Kalman filtering, we derive a new back-propagation algorithm whose learning rate is computed by Riccati difference equation.
创建监管学习程序需要使用许多算法，最常见的包括神经网络、SupportVectorMachines (SVMs)和Naive Bayes分类程序。
Many algorithms are used to create supervised learners, the most common being neural networks, Support Vector Machines (SVMs), and Naive Bayes classifiers.
有意思的是，神经网络是一种对学习型评估的进化，算法经过训练后其行为就像是一个人类专家。
It is interesting to note that Neural Networks is an evolution of learning-oriented estimation, in which the method algorithm is trained to behave like a human expert.
以人工神经网络的前馈型网络为基础结构，基于反向传播算法进行学习和训练来拟和证券价格指数的运动趋势。
In this model, back propagation algorithm based on forward networks was conducted to learn information of historical data and to train the network weights.
由于这些变量具有非线性时间序列数据，用人工神经网络(ANN)将使用反向传播算法作为学习算法。
Since these variables are characterized as nonlinearities time series data, Artificial Neural networks (ANN) will be employed using back propagation algorithm as learning algorithm.
提出一种准对角递归神经网络(QDRNN)结构及学习算法。
A structure and training algorithm for quasi-diagonal recurrent neural network (QDRNN) is presented.
本文从神经网络模型的结构出发，对学习算法提出了一系列改进和优化措施，以加快网络的学习速度，并增加模型的稳定性。
With the study of neural network model, this paper advances some of improvement and optimization techniques that can accelerate the learning speed of network and increase the stability of model.
提出了一个基于模糊集理论的新的神经网络结构及其学习算法。
This paper presents a novel neural network architecture based on fuzzy set theory, FIBP.
因此，作者将非线性规划的平行切线算法用于神经网络的权值学习，提出了一种具有快速学习算法的神经网络。
This paper applies parallel tangents of nonlinear programming during the weights training of neural network and puts forward a neural network in view of fast learning algorithm.
依据小波的非线性逼近能力和神经网络的自学习特性，提出了一种基于小波神经网络模型的自校正控制算法。
Based on the learning characteristic of neural network and the function approximation ability of the wavelet, a new self tuning control algorithm is presented.
通过利用合理的学习算法进行训练，神经网络对事物和环境具有很强的自学习、自适应和自组织能力。
Carries on the training through the use reasonable study algorithm, the neural network has to the thing and the environment very strong from the study, auto-adapted and from the organization ability.
该系统具有神经网络的结构和学习算法，称模糊神经网络FNN。
This system, possesses the structure of neural net and learning algorithm, addressed as fuzzy neural net FNN.
本文提出了一种利用微机机群来实现并行处理，在并行编程环境P VM中实现BP神经网络的并行学习算法。
In this paper, we proposed a parallel BP neural network learning algorithm with the support of PC cluster under the circumstance of PVM (parallel Virtual Machine).
文中阐述了这种方法的原理、神经网络的结构及学习算法。
The principle of the approach, the structure of neural networks'and hew learning algorithm are interpreted.
给出了粗糙神经网络的拓朴结构和学习算法以及粗糙神经网络的逼近定理。
The topologic structure and learning algorithm of the rough neural network are given, and the approximation theorem of the rough neural network is presented.
该模型采用五层前向模糊神经网络，学习算法为梯度下降法。
The model was a Feedforward Fuzzy neural network possessing five layers, and Gradient Descent was adopted as learning algorithm.
本文提出了一种新的前向神经网络快速分层学习算法。
A new rapid layer-wise learning algorithm for feed-forward neural networks is proposed in this paper.
本文提出了一种新的前向神经网络快速分层学习算法。
A new rapid layer-wise learning algorithm for feed-forward neural networks is proposed in this paper.
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