共查询到20条相似文献,搜索用时 62 毫秒
1.
Quantum Neural Network (QNN) is a burgeoning new field built upon the combination of classical neural networks and quantum computations, which has many problems needed to solve. Where the learning of the network weight vector is an issue must be settled to develop QNN. Upon the analysis of the Grover’s quantum algorithm, a model of QNN with weight vector and a training method for it are proposed in this paper. It can be shown that this model and method work in quantum mechanism. Results on the data set show that this network model can deal with some classical problem such as XOR problem and the proposed weight updating algorithm based on the Grover always can learn training examples in a certain percentage, despiting it has not been proven to excel classical learning algorithm in performance. It yet has some advantages over classical counterpart. 相似文献
2.
该文提出一种新的改进激励函数的量子神经网络模型。首先为了提高学习速率,在网络权值训练过程中引入了动量项。然后为了有效实现相邻类之间具有覆盖和不确定边界的分类问题,新网络采用区分度更大的双曲正切函数的叠加作为其隐层激励函数。最后将该算法用于字符识别,将双曲正切激励函数的量子神经网络应用于数字、字母和汉字样本的多次实验,并且与原多层激励函数量子神经网络和BP网络的实验效果进行比较,发现改进后量子神经网络不仅具有较高的识别率,而且在样本训练次数上相对原多层激励函数量子神经网络有明显减少。仿真结果证明该方法的优越性。 相似文献
3.
4.
前馈神经网络的新学习算法研究及其应用 总被引:18,自引:0,他引:18
为了提高多层前馈神经网络的权的学习效率。通过引入变尺度法,提出一种新的学习算法。理论上新算法不仅具有变尺度优化方法的一切优点,而且也能起到Kick—Out学习算法中动量项及修正项的相同作用,同时又克服了动量系数及修正项系数难以适当选择的困难。仿真试验证明了新学习算法用于非线性动态系统建模时的有效性。 相似文献
5.
6.
A learning algorithm based on the modified Simplex method is proposed for training multilayer neural networks. This algorithm is tested for neural modelling of experimental results obtained during cross-flow filtration tests. The Simplex method is compared to standard back-propagation. Simpler to implement, Simplex has allowed us to achieve better results over four different databases with lower calculation times. The Simplex algorithm is therefore of interest compared to the classical learning techniques for simple neural structures. 相似文献
7.
8.
人工神经网络集成技术是神经计算技术的一个研究热点,在许多领域中已经有了成熟的应用.神经网络集成是用有限个神经网络对同一个问题进行学习,集成在某输入示例下的输出由构成集成的各神经网络在该示例下的输出共同决定.负相关学习法是一种神经网络集成的训练方法,它鼓励集成中的不同个体网络学习训练集的不同部分,以使整个集成能更好地学习整个训练数据.改进的负相关学习法是在误差函数中使用一个带冲量的BP算法,给合了原始负相关学习法和带冲量的BP算法的优点,使改进的算法成为泛化能力强、学习速度快的批量学习算法. 相似文献
9.
10.
基于粒子群优化算法的BP网络学习研究 总被引:26,自引:3,他引:26
文章提出了基于粒子群优化的BP网络学习算法。在该算法中,用粒子群优化算法替代了传统BP算法中的梯度下降法,使得改进后的算法具有不易陷入局部极小、泛化性能好等特点。并将该算法应用在了高速公路动态称重系统的设计中,实验证明:这种算法能够明显减少迭代次数、提高收敛精度,其泛化性能也优于传统BP算法。 相似文献
11.
介绍集成神经网络的基本概念及其算法理论,提出基于遗传算法的集成神经网络入侵检测方法,并以KDDCUP99作为数据源给出应用该方法进行入侵检测的性能.通过与单个神经网络的比较,说明基于遗传算法的集成神经网络检测方法能克服单个分类算法的缺陷,提高入侵检测系统的检测率. 相似文献
12.
13.
提出一种量子神经网络模型及算法.首先借鉴受控非门的含义提出一种受控量子旋转门,基于该门的物理意义,提出一种量子神经元模型,该模型包含对输入量子比特相位的旋转角度和对旋转角度的控制量两种设计参数;然后基于上述量子神经元提出一种量子神经网络模型,基于梯度下降法详细设计了该模型的学习算法:最后通过模式识别和时间序列预测两个仿... 相似文献
14.
Cheng-Jian Lin Yong-Cheng Liu Chi-Yung Lee 《Journal of Intelligent and Robotic Systems》2008,52(2):285-312
This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang
(TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions
as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable
parameters of the WNFN model. The proposed WNFN-S learning scheme is based on dynamic symbiotic evolution (DSE). The proposed
DSE uses the sequential-search-based dynamic evolutionary (SSDE) method. In some real-world applications, exact training data
may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolutionary learning, called WNFN-R,
is proposed. Computer simulations have been conducted to illustrate the performance and applicability of the proposed WNFN-S
and WNFN-R learning algorithms. 相似文献
15.
依据RBF神经元模型的几何解释,提出一种新的构造型神经网络分类算法.首先从样本数据本身入手,通过引入一个密度估计函数来对样本数据进行聚类分析;然后在特征空间里构造超球面,以逼近样本点分布的几何轮廓,从而将神经网络训练问题转化为点集"包含"问题.该算法有效克服了传统神经网络训练时间长、学习复杂的缺陷,同时也考虑了神经网络规模的优化问题.实验证明了该算法的有效性. 相似文献
16.
针对BP神经网络中采用的梯度下降法局部搜索能力强、全局搜索能力差和遗传神经网络中采用的遗传算法全局搜索能力强、局部搜索能力差的特点,提出了一种集梯度下降法和遗传算法优点为一体的混合智能学习法(Hybrid Intelligence learning algorithm),简称HI算法,并将其应用到优化多层前馈型神经网络连接权问题。对该算法进行了设计和实现,从理论和实际两方面证明混合智能学习法神经网络与BP神经网络和基于遗传算法的神经网络相比有更好的运算性能、更快的收敛速度和更高的精度。 相似文献
17.
There is no method to determine the optimal topology for multi-layer neural networks for a given problem. Usually the designer selects a topology for the network and then trains it. Since determination of the optimal topology of neural networks belongs to class of NP-hard problems, most of the existing algorithms for determination of the topology are approximate. These algorithms could be classified into four main groups: pruning algorithms, constructive algorithms, hybrid algorithms and evolutionary algorithms. These algorithms can produce near optimal solutions. Most of these algorithms use hill-climbing method and may be stuck at local minima. In this article, we first introduce a learning automaton and study its behaviour and then present an algorithm based on the proposed learning automaton, called survival algorithm, for determination of the number of hidden units of three layers neural networks. The survival algorithm uses learning automata as a global search method to increase the probability of obtaining the optimal topology. The algorithm considers the problem of optimization of the topology of neural networks as object partitioning rather than searching or parameter optimization as in existing algorithms. In survival algorithm, the training begins with a large network, and then by adding and deleting hidden units, a near optimal topology will be obtained. The algorithm has been tested on a number of problems and shown through simulations that networks generated are near optimal. 相似文献
18.
针对油藏测井解释中的水淹层识别问题,提出一种量子神经网络模型。该模型用量子旋转门更新量子比特的相位,用受控旋转门实现网络的非线性映射功能。网络可调参数为量子旋转门的旋转角度和受控非门的控制参数。基于梯度下降法设计了学习算法。仿真结果表明,该模型的预测能力优于普通BP网络、模糊神经网络和过程神经网络等其他方法。 相似文献
19.
20.
鞅在学习样本选择中的应用 总被引:1,自引:0,他引:1
样本训练集的选取对网络分类精度及泛化能力有很大影响,同样对回归分析中的两难问题“偏差-方差”影响很大。经典的简单抽样理论在现实中很难做到,数据之间关系受到噪音以及领域知识的限制而显得很复杂,尤其是离群点的影响不能忽视。故而有限样本集中学习,如何获得最优结果不仅与算法有关,且与样本集的选取有关。文章首先从学习的数学理论出发阐明样本训练集的选取方法必要性,进而提出样本选择的鞅性要求与样本训练集中的离群点定义,最后提出在无监督学习中,混合密度分布有限样本集且样本类别数不知情形下的聚类与离群点判别算法,试验结果表明该算法的可行性与有效性。 相似文献