Personalization Recommendation System Based on Web Log& Cache Data Mining

A Web Personalized Recommendation Method Based on Uncertain Consequent Association Rules

一种基于后项不定长关联规则的Web个性化推荐方法

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This is defined as the technique of web Personalized recommendation.

这就是所谓的Web个性化推荐技术，是Web技术研究和应用的热点之一。

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Adaptive study rate of BP neural network is improved in this BP neural network and optimization of astringency based on momentum is realized, which solves problems of slow astringency and local minimum.

在BP算法的自适应学习率，在动量法优化网络收敛性等方面，进行了深入研究，并改进了基本BP算法中的收敛速度慢和易陷入局部最小点等问题。

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Aiming at the defect of gradient-descending arithmetic, such as low-speed of constringency, easily falling into local minimum, an improved approach based on affixation momentum and self-adaptive learning rate is proposed.

针对传统BP算法即梯度下降法，收敛速度慢、容易陷入局部极小值等缺点，提出了基于附加动量法和自适应学习速率的改进方法。

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Artificial Neural Network ( ANN) can approximate any complex nonlinear system, has good pattern recognition, adaptive learning and memory ability, such as associative memory.

人工神经网络（ANN）可以逼近任意复杂的非线性系统，具有较好的模式识别、自适应学习和记忆联想等能力。

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To guarantee the fast convergence of the proposed neural network model, the optimal adaptive learning rates are derived in the sense of discrete-type Lyapunov stability.

在离散型Lyapunov稳定性的意义下，导出了权值自适应学习速率的取值范围，保证控制系统的快速收敛。

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Especially back propagation algorithm based on self adapting learning rate with momentum gradient reduction can avoid efficiently the slow convergence and the problem of 'local infinitesimal value' of BP network.

其中自适应学习速率动量梯度下降反向传播算法，可以有效避免BP网络收敛速度慢和存在所谓局部最小值问题。

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Direct routine bp algorithm learning inefficiency, rapidity of convergence slow wait vice, adopt lot study, append momenta term, adaptive learning coefficient wait measure held over improve on it.

针对常规BP算法学习效率低，收敛速度慢等缺陷，采用批量学习、附加动量项、自适应学习系数等措施加以改进。

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The standard artificial neural networks have inherent disadvantages. To overcome these disadvantages, an evolved Back Propagation ( BP) algorithm is developed by combination of additional inertia and self-adaptation of learning velocity.

为了克服标准误差反传（Back Propagation，BP）算法的缺陷，提出了一种附加动量项和自适应学习速率相结合的改进的BP算法。

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Design of IRT-based adaptive learning management system

基于IRT的自适应学习管理系统设计

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A reverse transmission calculation of self adapting learning rate with momentum gradient reduction is described in the paper against the problems of slow convergence and easy trapping into smallest spot.

针对BP网络学习收敛速度慢和易陷入局部最小点的不足，提出了一种自适应学习速率动量梯度下降反向传播算法对BP神经网络进行训练。

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The application of fuzzy adaptive learning control ( FALCON) in milling-classification operation system

模糊自适应学习控制（FALCON）在磨矿分级系统中的应用

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By the use of the additional momentum method and adaptive learning rate of BP network, a neural network model was established to calculate the speed of tidal bore.

运用BP网络附加动量法和自适应学习速率法，建立神经网络模型，模拟计算涌潮波速。

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The improved BP algorithms based on adaptive parameters adjustment and error contracting gradually are presented, which are applied successfully to fault diagnosis of steam-turbine generator unit.

提出了自适应学习率及动量因子的BP神经网络算法和误差逼近度渐近收缩学习的BP神经网络算法，并将其应用于汽轮发电机组振动故障诊断与识别。

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At last, a kind of BP algorithm with adaptive learning rate is evaluated and compared with standard BP algorithm by XOR problem.

最后结合XOR问题把一种自适应学习率BP算法和标准BP算法进行了比较和评价。

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To improve the networks' classification effect and train speed, the additive momentum and self-adaptive – study-rate adjustment method are adopted further to improve traditional BP algorithm.

为了提高网络的分类效果以及训练速度，采用了附加动量法和自适应学习速率调整法对BP算法进行了改进。

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Artificial neural network is a nonlinear system which simulates biological neural systems. It has powerful parallel processing, distributed storage, adaptive learning, a high degree of fault-tolerant capability.

人工神经网络（Artificial neural network，ANN）是模拟生物神经系统的非线性系统，具有强大的并行处理、分布式存储、自适应学习、高度容错的能力。

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Because of the existing problems of general BP algorithm, operation speed of BP Network is improved by using adaptive learning rate and additional item.

针对普通BP算法存在的问题，采用自适应学习率及附加动量项的方法进行改进，以提高BP网络的运算速度。

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Realize Adaptive Learning in E-learning System

实现E-learning平台中的学生自适应学习

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The neural network has the ability of self adaptive learning and remembering, and the three-layer BP network has wide application.

人工神经网络具有自适应学习和记忆能力，尤其是3层BP网络模型。

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