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.
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。
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).
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。
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.
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。
The topologic structure and learning algorithm of the rough neural network are given, and the approximation theorem of the rough neural network is presented.
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。
Based on the learning characteristic of neural network and the function approximation ability of the wavelet, a new self tuning control algorithm is presented.
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。
Experiment shows neural network classifier that is optimized by algorithm could not only have fast learning speed but also ensure accuracy of classification.
实验结果表明：算法优化后的神经网络分类器不但学习速度快，还能保证分类精度。
Based on expatiated the basic structure model and some general improved algorithms of BP neural network, this paper brings forward a new self-organization learning algorithm.
介绍了BP网络的基本结构模型与常见改进算法，在此基础上提出了一种新型的结构自组织BP网络算法。
A new neural network controller is proposed based on the PID controller structure. Its basic structures and learning algorithm are analysed.
根据PID控制结构提出了一种新型神经网络控制器，对其基本结构和学习算法等进行了分析。
The compensation fuzzy neural network (CFNN) with fast learning algorithm and compensation fuzzy inference is introduced in this paper.
本文介绍了一种具有快速学习算法、能够执行补偿模糊推理的补偿模糊神经网络。
Compared with RBF Network, it is concluded that learning algorithm is master key to improve properties of Artificial Neural Network.
与工具箱的RBF网络相比较，说明网络的学习算法是改善网络性能的关键。
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.
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。
It established the model's output mathematic function and learning algorithm. Computer simulations showed the equivalence of fuzzy chaos neural network model and the original chaotic system.
确定了模型的输出函数，并推导了模型的学习算法，仿真结果表明永磁同步电机的模糊混沌神经网络模型与原系统是等价的。
PN model, LAC neural network and its learning algorithm are all put forward first time in this thesis.
PN神经元模型、lac神经网络及学习算法，都是本文首次提出的。
The parameter learning algorithm of dynamic recurrent neural network based on system identification is analyzed. D.
分析了动态递归神经网络系统辨识的参数学习算法。
This paper presents a model for identifying induction motor speed using the recurrent neural network, which is trained by a real time recurrent learning algorithm.
本文利用递归神经网络来建立异步电机转速辩识模型，其网络学习采用实时递归学习算法。
The model was a Feedforward Fuzzy neural network possessing five layers, and Gradient Descent was adopted as learning algorithm.
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。
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.
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。
A new type of adaptive PID controller using diagonal recurrent neural network (DRNN) is presented. An on-line learning algorithm based on PID parameter self-tuning method is given.
提出了一种基于对角回归神经网络的PID控制器结构，给出了PID参数在线自整定的学习控制算法。
In this paper, a novel fast learning algorithm for multilayered feedforward neural network is introduced.
本文提出一种前馈神经网络的快速学习算法。
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.
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。
We first discuss the structure and principle of the CMAC neural network. Using competitive learning, we develop a new adaptive quantization algorithm.
首先阐述了CMAC神经网络的原理、结构和学习算法，提出了一种新的采用竞争学习原理的非等距自适应量化算法。
Study modelling thought, network configuration, majorize GNNM(1,1) mode method and learning algorithm of GNNM(1,1) mode combined grey system theory and neural network.
研究了灰色系统理论与神经网络组合的灰色神经网络GNNM（1，1）模型的建模思想、网络结构及其优化GNNM（1，1）模型的方法和学习算法；
Analyzing the integral splitting PID algorithm, and melting the wide-used PID controller and the automatic learning neural network, got a PID control algorithm based on the BP network.
分析了积分分离pid控制算法，在此基础上，将应用最广泛的PID控制器与具有自学习功能的神经网络相结合，得到了基于BP神经网络的PID控制算法。
BP algorithm is the most popular training algorithm for feed forward neural network learning. But falling into local minimum and slow convergence are its drawbacks.
BP算法是前馈神经网络训练中应用最多的算法，但其具有收敛慢和陷入局部极值的严重缺点。
BP algorithm is the most popular training algorithm for feed forward neural network learning. But falling into local minimum and slow convergence are its drawbacks.
BP算法是前馈神经网络训练中应用最多的算法，但其具有收敛慢和陷入局部极值的严重缺点。
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