在数学学习上寻求帮助 getting help with math; getting helwith math
数学 mathematics 他数学方面训练不够。 he was lacking in mathematical training.; 数学归纳法 [数学] complete induction; mathematical induction; 数学家 mathematician; 数学教学 mathematical education; 数学近似 mathematical approach; 数学投影 mathematical projection; 数学作业 arithmetic operation
新课程理念下的数学学习评价 the evaluation of mathematics study under the new curriculum philosophy
学习 study; learn; emulate 集中学习 massed learning; 学习某人的榜样 learn from sb.'s example; follow sb.'s example; 学习文化 learn to read and write; 学习雷锋 learning from lei feng; 学习英雄人物活动 activities to learn from heroes; 从生活中学习 learn through living; 学习成绩 academic record; school record; records of studies; 学习动机 academic motivation; motivation of learning; 学习方法 learning method; 学习环境 academic environment; 学习进度表 academic progress check; 学习目的 aim of learning; 学习能力 mentality; learning ability; learning capacity; 学习年限 period of schooling; 学习期限 period of schooling; 学习体会 study notes; 学习委员 comrade in charge of studies; 学习障碍 learning disorder
A parameter learning method for stochastic fuzzy 随机模糊神经网络的参数学习算法
In the research on fuzzy neural networks , the problem of parameters learning is very important 在模糊神经网络的研究中,参数学习问题具有很大的重要性。
But as a good optimization method , the researches on using tabu search as the learning algorithms for fuzzy neural networks are very few 然而,目前使用禁忌搜索算法作为模糊神经网络参数学习算法的研究却不多见。
Generally the problem of learning the parameters of fuzzy neural networks may change to the problem of function optimization 一般来说,对模糊神经网络的参数学习问题可以转化为对其目标函数的优化问题,即寻找一组合适的参数向量使其目标函数值最优。
From the results of the research given in this paper , we can see that tabu search has the high convergent ratio , and good convergent precision in learning the parameters of the neuro - fuzzy system 通过本文的研究可以看出,将禁忌搜索算法用在模糊神经系统参数学习中具有很好的性能,该算法具有收敛概率高,收敛精度好等优点。
On the basis of theoretic convergence analyses of a single - coefficient learning algorithm , a transposition rule is proposed , which is applied to the single - coefficient learning algorithm to gain quick convergence speed in the phase of coefficient learning 最后在单参数学习算法收敛性的分析基础上,提出一种变调整规则的单参数学习算法,加快参数学习速度。
The lyapunov function is used to analyze the convergence of the general learning rule , and it is proved in theory that the general learning rule has the inherent factor which adjusts the coefficient values to gain the minimum error 通过理论推导,用李雅普诺夫函数分析和验证通用参数学习规则的学习收敛性,揭示参数学习算法朝最小误差方向调整参数的内在因素。
We make some further study on some problems , such as the learing of structure and parameters of bayesian network , network estimate and so on , on the basis of which a kind of learning method of bayesian network based on the attribute relativity analyse is achieved 研究了贝叶斯网络的拓扑结构学习、参数学习和网络评估等问题,在此基础上设计实现了一种基于属性相关性分析的贝叶斯网络学习算法。
In this paper , we study the trading model based on the project " the network market " , which was implemented by the chongqing electronic commerce inc and us . aiming at the shortage of trading model in " the network market " , we employ game theory and multi - criteria decision theory , introduce the matchmaking schema based on price and quantity into electronic commerce application , bring forward a market matchmaking trading model including five phases : market matchmaking , reinforce learning , biliteral negotiation , contract signing , contract executing . the main work and conclusion as follows : considering the behaviour of multi - buyers with multi - sellers , we realise a matchmaking model based on the price and quantity through double auction mechanism under discriminatory and non - discriminatory price situation , analyse the incentive compatibility and competitive equilibrium of the mechanism 主要研究成果如下:针对多个买家与多个卖家的交易行为,提出一种撮合交易模型?采用价格和数量作为撮合要素,以双重拍卖机制为撮合手段,重点研究均价和差价形式下的实现机制,并对其激励相容性和市场均衡进行理论分析?为增强市场效率,论文提出了一种针对双重拍卖的学习机制,它以三参数学习模型为基础进行改进,借助交易历史信息,实现交易代理的自我学习
Based on the analysis of the methods for optimizing the fuzzy neural networks before , this paper has finished following works : 1 ) we proposed a learning algorithm based on tabu search for fuzzy neural networks based on the model of anfis proposed by jyh - shing roger jang . then used the system for one variable function ' s approximation . 2 ) based on the first research , we improved the tabu search algorithm for the purpose of approximating complex functions . 3 ) analysis the capabilities of tabu search , and discuss the approximation ability and generalization ability of the fuzzy neural networks system according to the compute results 本文在对以前的模糊神经网络参数学习算法进行分析的基础上,做了以下几个方面的工作: 1 )根据禁忌搜索算法的特点,在jyh - shingrogerjang提出的anfis模型的基础上,将禁忌搜索算法应用于模糊神经网络线性和非线性参数的学习上,并将该模型用于单变量函数的逼近; 2 )在第一阶段的基础上,对算法进行了改进,使改进后的算法能够适用于复杂的ii函数逼近问题; 3 )根据计算机仿真的结果,对禁忌搜索算法的性能进行了分析,并对该模糊神经系统的函数逼近能力和泛化能力进行了讨论。