共查询到19条相似文献,搜索用时 93 毫秒
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为了克服神经网络财务危机预警方法收敛慢、不收敛和网络结构难以确定等缺陷,提出了基于蚁群算法的改进神经网络财务危机预警方法。将神经网络模型的结构和参数进行编码,利用蚁群算法确定若干个神经网络模型的结构和参数,然后通过评价函数得到神经网络的最佳结构,最后通过BP算法训练该神经网络,得到神经网络财务危机预警模型。验证结果表明,该模型结构简单、预警精度高。 相似文献
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为了克服神经网络依赖初始化结果,泛化能力不强的缺点,提出一种基于受限玻尔兹曼机(RBM)的神经网络模型.利用无监督学习方法优化神经网络的初始权值和阈值,将RBM与神经网络融合起来,模型与时间序列神经网络做实验对比,结果表明,基于受限的玻尔兹曼机的神经网络模型优于神经网络预测模型,模型可以提高预测的精准度,具有一定的应用意义. 相似文献
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唐万梅 《数学的实践与认识》2006,36(2):46-50
判别分析是判别样品所属类型的一种统计方法.利用M ATLAB提供的神经网络工具箱为基础,设计了一个三层BP神经网络判别模型,提出了一种进行判别分析的新方法,实例表明,利用BP神经网络建立的判别模型是进行判别分析的有效方法.是对研究分类问题的方法的扩充. 相似文献
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针对目前神经网络在实际应用中,网络结构难以确定以及网络极易陷入局部解问题,用遗传算法优化神经网络的连接权和网络结构,并在遗传进化过程中采取保留最佳个体的方法,建立基于遗传算法的神经网络模型,实例研究结果表明该方法可以取得较高的预测精度. 相似文献
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距离空间中插值神经网络的误差估计 总被引:2,自引:0,他引:2
研究距离空间中的神经网络插值与逼近问题.首先引进一类广义的激活函数,用比较简洁的方法讨论距离空间中插值神经网络的存在性,然后给出插值神经网络逼近连续函数的误差估计. 相似文献
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构造一种新型神经Mealy机,神经Mealy机具有一定的学习能力,它主要通过学习来获得(von Newman)计算机结构,可以较好地避免普通计算机那样损毁一条电路就带来灾难性后果的情况.其本质是将递归神经网络通过BP优化算法,对Mealy机进行模拟得到,并通过实验对该网络的学习性能进行研究分析.基于形式文法和自动机的等价性,用神经网络来实现文法推导.先采用神经网络对样本集进行学习,这些样本可由一个经典Mealy机生成,然后从训练完的神经网络提取出自动机. 相似文献
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A neural network model for solving an assortment problem found in the iron and steel industry is discussed in this paper. The problem arises in the yard where steel plate is cut into rectangular pieces. The neural network model can be categorized as a Hopfield model, but the model is expanded to handle inequality constraints. The idea of a penalty function is used. A large penalty is applied to the network if a constraint is not satisfied. The weights are updated based on the penalty values. A special term is added to the energy function of the network to guarantee the convergence of the neural network which has this feature. The performance of the neural network was evaluated by comparison with an existing expert system. The results showed that the neural network has the potential to identify in a short time near-optimal solutions to the assortment problem. The neural network is used as the core of a system for dealing with the assortment problem. In building the neural networks system for practical use, there were many implementation issues. Some of them are presented here, and the fundamental ideas are explained. The performance of the neural network system is compared to that of the expert system and evaluated from the practical viewpoint. The results show that the neural network system is useful in handling the assortment problem. 相似文献
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《Mathematical and Computer Modelling》1999,29(3):9-18
An artificial neural network is proposed in this paper for solving the linear complementarity problem. The new neural network is based on a reformulation of the linear complementarity problem into the unconstrained minimization problem. Our new neural network can be easily implemented on a circuit. On the theoretical aspect, we analyze the existence of the equilibrium points for our neural network. In addition, we prove that if the equilibrium point exists for the neural network, then any such equilibrium point is both asymptotically and bounded (Lagrange) stable for any initial state. Furthermore, linear programming and certain quadratical programming problems (not necessarily convex) can be also solved by the neural network. Simulation results on several problems including a nonconvex one are also reported. 相似文献
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In this paper, a class of bi-level variational inequalities for describing some practical equilibrium problems, which especially
arise from engineering, management and economics, is presented, and a neural network approach for solving the bi-level variational
inequalities is proposed. The energy function and neural dynamics of the proposed neural network are defined in this paper,
and then the existence of the solution and the asymptotic stability of the neural network are shown. The simulation algorithm
is presented and the performance of the proposed neural network approach is demonstrated by some numerical examples. 相似文献
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程毛林 《数学的实践与认识》2006,36(1):44-48
神经网络用于样本分类是一个新的研究课题,本文利用自组织特征映射神经网络,对生态城市进行分类.计算实例表明,用自组织特征映射神经网络用于分类是准确和可靠的. 相似文献
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《Communications in Nonlinear Science & Numerical Simulation》2014,19(4):789-798
In this paper, the optimization techniques for solving pseudoconvex optimization problems are investigated. A simplified recurrent neural network is proposed according to the optimization problem. We prove that the optimal solution of the optimization problem is just the equilibrium point of the neural network, and vice versa if the equilibrium point satisfies the linear constraints. The proposed neural network is proven to be globally stable in the sense of Lyapunov and convergent to an exact optimal solution of the optimization problem. A numerical simulation is given to illustrate the global convergence of the neural network. Applications in business and chemistry are given to demonstrate the effectiveness of the neural network. 相似文献
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Vladimir E. Bondarenko 《Complexity》2005,11(2):39-52
Information processing and two types of memory in an analog neural network model with time delay that produces chaos similar to the human and animal EEGs are considered. There are two levels of information processing in this neural network: the level of individual neurons and the level of the neural network. Similar to the state of brain, the state of chaotic neural network is defined. It is characterized by two types of memories (memory I and memory II) and correlation structure between the neurons. In normal (unperturbed) state, the neural network generates chaotic patterns of averaged neuronal activities (memory I) and patterns of oscillation amplitudes (memory II). In the presence of external stimulation, the activity patterns change, showing changes in both types of memory. As in experiments on stimulation of the brain, the neural network model shows synchronization of neuronal activities due to stimulus measured by Pearson's correlation coefficient. An increase in neural network asymmetry (increase of the neural network excitability) leads to the phenomenon similar to the epilepsy. Modeling of brain injury, Parkinson's disease, and dementia is performed by removing and weakening interneuron connections. In all cases, the chaotic neural network shows a decrease of the degree of chaos and changes in both types of memory similar to those observed in experiments with healthy human subjects and patients with Parkinson's disease and dementia. © 2005 Wiley Periodicals, Inc. Complexity 11:39–52, 2005 相似文献
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通过基于数据挖掘理论的粗糙集和神经网络的研究,用属性约简算法约简并提取了影响房地产价格的主要指标因素,对降维后的数据进行网络学习和训练,最后用训练好的的网络检验测试样本.方法使学习训练的速度和识别率提高了,为房地产价格预测提供了一种更为有效和实用的新途径. 相似文献
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建立未确知RBF神经网络.特点是:综合了未确知系统与神经网络的优点,充分利用已知样本所提供的先验信息,给出了期望输出隶属度的计算方法,网络输出合理且具有良好的可解释性.将未确知RBF神经网络应用于故障诊断领域,取得了很好的效果. 相似文献