The ant colony optimization algorithm has found slow convergence and easy to stagnation.
发现基本蚁群优化算法存在慢收敛且易停滞等问题。
However, the standard Particle Swarm Optimization is easy to fall into local optimum, and slow convergence.
然而，标准粒子群算法存在容易陷入局部最优，后期收敛过慢等问题。
Such as: existing partial minimum, the slow convergence, selecting the number of hidden nodes difficult and so on.
如：训练时易陷入局部极小、收敛速度慢、隐结点个数难以确定等。
Traditional neural network algorithms are easy to fall into the local minimum, slow convergence when in fault diagnosis.
传统的神经网络算法应用于故障诊断时，具有易陷入局部极小值，收敛速度较慢等缺点。
Reinforcement learning is an important machine learning method. However, slow convergence has been one of main problem in practice.
强化学习是一种重要的机器学习方法，然而在实际应用中，收敛速度缓慢是其主要不足之一。
Self-configuring neural network is used because BP neural network has both slow convergence in learning course and poor fault tolerant ability.
鉴于BP网络存在着学习过程收敛速度慢、网络容错能力差的缺点，本文提出一种自构神经网络算法。
The algorithm has slow convergence when the estimated value is far from the target. This paper presents a mixed dichotomy to solve the problem.
针对迭代过程中估计值偏离目标值时收敛较慢的情况，提出混合二分算法。
To overcome the slow convergence of the BP algorithm, recursive prediction error algorithm is proposed, which can train both the weight and the bias.
本文介绍了动态对角递归网络，并针对BP算法收敛慢的缺点，将递推预报误差学习算法应用到神经网络权值和域值的训练。
Then some defects such as slow convergence rate and getting into local minimum in BP algorithm are pointed out, and the root of the defects is presented.
分析了BP算法的基本原理，指出了BP算法具有收敛速度慢、易陷入局部极小点等缺陷以及这些缺陷产生的根源。
Then some defects such as slow convergence rate and getting into local minimum in BP algorithm are pointed out, and the root of the defects is presented.
针对前向神经网络BP算法由于初始权值选择不当而陷入局部极小点这一缺陷，提出新的全局优化训练算法。
Aiming at the slow convergence problem of clonal selection algorithm, this paper proposes an adaptive parallel immune algorithm with orthomutation (APIA).
针对克隆选择算法收敛速度较慢的问题，对算法策略进行研究，提出了一种基于定向突变的自适应并行免疫算法（APIA）。
An improved BP neural network is proposed for the purpose of overcoming the slow convergence and existence of local minimum in conventional BP neural network.
先对传统的BP人工神经网络进行了分析，针对其收敛速度慢，存在局部极小值的缺点提出了一种改进后的BP人工神将网络。
Q-learning is a typical Reinforcement Learning (RL) method with a slow convergence speed especially as the scales of the state space and action space increase.
学习是一种典型的强化学习，其学习效率较低，尤其是当状态空间和决策空间较大时。
To avoid the problem of slow convergence this paper presents a fast blind acquisition equalization algorithm to improve the convergence of the conventional CMA.
为了克服传统恒模算法收敛速度慢的缺点，该文提出了一种用于捕获阶段的快速盲均衡算法。
The thesis describes some solutions for the slow convergence (count to infinite) problem in Vector-Distance algorithm in TCP/IP and points some shortages of them.
论述了TCP/IP网络中距离向量（V D）算法中的慢收敛问题，以及一些解决方法的不足。
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。
The particle swarm optimization(PSO) algorithm, is used to train neural network to solve the drawbacks of BP algorithms which is local minimum and slow convergence.
针对多层前馈网络的误差反传算法存在的收敛速度慢，且易陷入局部极小的缺点，提出了采用微粒群算法（PSO）训练多层前馈网络权值的方法。
People put forward radial basis function networks considering the conventional BP algorithm problems of slow convergence speed and easily getting into local dinky value.
对于传统BP算法存在的收敛速度慢和易陷入局部极小值问题，人们提出了径向基函数网络。
Aiming at the slow convergence rate of BP neural network, append a correlative node on hidden layer, improve the adaptive ability and rate of studying of neural network.
针对BP算法收敛速度慢的特点，在隐含层上加入了关联节点，改善了网络的学习速率和适应能力。
In order to overcome the slow convergence rate of traditional CMA (Constant modulus algorithm), a Momentum algorithm based Constant modulus algorithm (MCMA) is proposed.
针对传统常数模算法收敛速度慢的缺点，提出了一种基于动量算法的常数模算法。
An improved neural network based on L-M algorithm has been applied to fault diagnosis expert system against to the slow convergence rate of conventional BP neural network.
针对传统BP神经网络训练中收敛速度较慢的缺点，提出一种基于L - M算法的神经网络应用于机械设备故障诊断的专家系统。
Because of the high algorithm complexity, the slow convergence and the signal or symbol obscure problem of the blind channel estimation, it is not suitable in practical use.
由于盲信道估计算法复杂度高，收敛时间长且存在信号或符号的不确定问题，不适于在实际中应用，因此本文主要研究的是基于导频的信道估计。
During the alternate iteration, an acceleration method called as vector extrapolation was applied to the alternate iteration steps owing to slow convergence of the algorithm.
在交替迭代过程中，考虑到算法收敛较慢，一种称为向量外推的加速方法被采用。
The BP neural network has the ability to solve many practical problems because of its strong mapping. However, it has slow convergence rate and is prone to fall into local extremum.
BP神经网络具有很强的映射能力，可以解决许多实际问题，但同时还存在着收敛速度慢，易陷于局部极小的缺点。
It is confirmed that PSO could overcome intrinsic shortcomings of BP neural network, including low learning efficiency, slow convergence rate, being easy to fall into local minima, etc.
经验证(PSO)优化算法可以有效地克服BP神经网络存在的学习效率低，收敛速度慢以及容易陷入局部极小点等固有缺点。
In application of neural networks based short-term load forecasting model, the main problems are over many training samples, thus resulting long training time and slow convergence speed.
在神经网络负荷预测实际应用中，突出的问题是训练样本大、训练时间长、收敛速度慢。
To solve the problem of slow convergence in the modified constant modulus algorithm (MCMA), a variable step and dual mode blind equalization algorithm is proposed, based on the MCMA algorithm.
为解决修正常系数模板算法（MCMA）收敛速度缓慢的问题，在MCMA算法的基础上，给出了一种变步长双模式MCMA算法。
To solve the problem of slow convergence in the modified constant modulus algorithm (MCMA), a variable step and dual mode blind equalization algorithm is proposed, based on the MCMA algorithm.
常数模算法是一种最为常用的盲均衡算法，普遍应用于恒包络信号和非恒包络信号的均衡，但存在收敛速度慢和剩余误差大的缺点。
To solve the problem of slow convergence in the modified constant modulus algorithm (MCMA), a variable step and dual mode blind equalization algorithm is proposed, based on the MCMA algorithm.
常数模算法是一种最为常用的盲均衡算法，普遍应用于恒包络信号和非恒包络信号的均衡，但存在收敛速度慢和剩余误差大的缺点。
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