针对文本中事件专题挖掘，提出事件模型学习算法。
For text-mining method of event topic, the method of event model was also proposed.
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.
理论分析说明这种模糊规则后件参数学习算法是收敛的、所建模糊模型能够以要求的精度逼近已知的实验数据。
The learning algorithm and the characteristics of the fuzzy rules model which can approximate the experiment data are shown to converge to any arbitrary accuracy by the theoretical analysis.
提出了一种传感器动态模型辩识新方法，给出了相应的辩识过程及学习算法。
This paper presents a new method for dynamic model identification of sensors, and gives the corresponding design processes and learning algorithm.
它支持有向或无向的模型，离散或连续的变量，各种推论及学习算法。
It supports directed and undirected models, discrete and continuous variables, various inference and learning algorithms.
提出基于TDNN神经网络的线性噪声消除器，讨论了它的模型与学习算法及其通用逼近性。
Linear noise canceller based on TDNN was proposed, and its model, learning algorithm and universal approximation were discussed.
本文在李雅普·诺夫稳定性意义下，提出了一种辨识模糊关系模型的学习算法。
A learning algorithm based on Lyapunov stability is derived to fuzzy relational model identification in this paper.
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.
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。
PN model, LAC neural network and its learning algorithm are all put forward first time in this thesis.
以径向基函数神经网络作为软测量模型，在软测量建模中引入正则化学习算法。
Using RBF (Radial Basis Function) network as the soft-sensing model, its natural to introduce regularization 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。
The model was a Feedforward Fuzzy neural network possessing five layers, and Gradient Descent was adopted as learning algorithm.
通过计算机实验，讨论样本、学习算法和网络结构等对神经网络预测模型性能的影响及其改进措施。
Through computer simulation, samples, BP algorithms and the influence of network structure neurula on model performance have been discussed as well as the improving measures.
本文首先介绍了神经网络中应用最为成熟广泛的BP网络的模型及其学习算法，并简单对比介绍了RBF网络。
The model and learning algorithms of BP( Error Back Propagation)network, which is widely applied, is recommended, and RBF( Radial B asis Function)is simply recommended contrastively.
本文根据经济微观仿真模型ASPEN的定价机理，提出了一种自动学习算法。
In this paper, a self-learning algorithm is put forward based on pricing principle of ASPEN, a microsimulation model of the economy.
为了提高性别检测的精度，提出了一种支持向量机(SVM)与主动外观模型(aam)相结合的迭代学习算法。
In order to increase accuracy in gender classification, an iterative learning approach combining support vector machine (SVM) and active appearance model (AAM) was proposed.
提出了一种基于神经元状态融合的组合导航系统信息融合模型，给出了神经元融合权重在线自适应学习算法。
An information fusion model of integrated navigation system based on neurons is proposed, and also an on line adaptive training algorithm of the weights of neuron is given.
本课程涵盖了语法、语意及对话处理模型，著重在机器学习或是以语料库为基础的方法及演算法。
It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms.
本文有针对性地对人工神经网络的各种网络模型及学习算法进行了研究，提出一了种基于人工神经网络的电厂主汽温控制系统。
This thesis researched the models and the learning algorithm of neural network, raised a main steam control system of power station based on neural network.
两个典型算例的实验结果表明，该模型及其学习算法是可行和有效的。
Finally, experimental results of two typical benchmarks demonstrate that the new model and its learning algorithm are feasible and efficient.
通过研究神经元学习算法，提高神经元的学习效率，并且提出一种新的神经元模型。
The efficiency of artificial neuron is promoted and a new model of artificial is created by studying the learning algorithm.
算法的系统动态模型具有对速度的学习能力，这样可以减少粒子的维数和所需要的粒子数。
System dynamic model of algorithm can learn the velocity of target, so the dimension of particle is reduced and the required particle is very few.
因此构造自学习算法模型，综合考虑在各种网络业务环境下如何实现CPU流控是算法的关键。
And the essential is how to construct this automatism learning model and build out the CPUFC arithmetic to adapt to various network services.
提出了CUSI神经元模型的一般形式，给出其学习算法。通过实例将CUSI神经元模型应用到地质数据的分析上，取得了比线性回归更好的效果。
The general form of CUSI neuron model and its learning algorithm are given, and apply it to geological data analysis and get the better effect than linear regression.
研究了基于所建模型的联想式模式学习算法。该算法模型体现了结构自组织、神经回路反响谐振等生理学特征。
Based on the established model, some associative pattern learning algorithms were put forward, which had the physiological features of structure's self-organizing and neural circuits' reverberating.
为了对在线学习文档进行分类，本文根据自适应谐振理论给出了一个半监督学习模糊art模型(SLFART)及其算法。
For learning document classification on line, the paper gives the semi-supervised learning fuzzy ART model (SLFART) based on adaptive resonance theory and the models 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。
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.
还给出了任务类知识需求模板和用户模型的自学习算法。
The self-learning algorithms of the task class knowledge need model and user model are put forward.
为简化模型的计算复杂度，开发了一种基于正交基函数展开的学习算法。
To simplify the calculation, a learning algorithm based on the expansion of the orthogonal basis functions is developed.
本文提出了一种异联想记忆模型的优化学习算法。
This paper presents an optimized learning strategy for a bidirectional associative memory.
本文提出了一种异联想记忆模型的优化学习算法。
This paper presents an optimized learning strategy for a bidirectional associative memory.
应用推荐