共查询到18条相似文献,搜索用时 78 毫秒
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本文提出了一种求解线性约束二次优化问题的神经模型 ,研究了该神经网络的稳定性和收敛性 ,给出了电路框图 ,并通过算例证明了该神经网络的可行性。 相似文献
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针对线性约束的非线性规划的求解问题,利用罚函数求解优化问题的思想将其转化为二次凸规划,基于神经网络的结构特性,定义所需的能量函数,从而使网络收敛于唯一稳定点最终实现线性约束的非线性规划的求解。实验仿真结果表明,该方法是有效和正确的,且能推广到含参的非线性规划和多目标规划中去。 相似文献
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提出了一种基于约束规划的选择性神经网络集成方法,在训练出个体网络之后,用约束规划方法选择出相对最佳的个体网络组成神经网络集成。理论分析和实验结果表明,该方法设计过程简单,能够以较小的运算代价提高神经网络集成的泛化能力。 相似文献
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本文首先对饱和模式线性神经网络模型(LSSM)进行分析,指出其实际上是一种沿内部和边界面依次搜索局部极小值点进行存储的联想记忆模型,进而证明了基于约束区域的神经网络[6]与LSSM具有相同的联想记忆特性,它由普通的常微分方程描述,更利于模拟和硬件实现,计算机模拟说明了结论的正确性. 相似文献
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本文提出一种求解约束二次Minimax问题的神经网络模型,给出了它的Lyapunov能量函数,运用LaSalle不变性原理证明了它的大范围渐近稳定性,作为应用考察了L1范数极小化问题。 相似文献
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提出一种基于约求满足的自适应神经网络方法求解车间作业调度问题。在该算法中,神经网络在运行过程中能够根据问题的约束类型、约束满足情况、启发式规则的选择来自适应调节神经元之间的连接权值,从而求得问题的可行解。仿真实验证明了算法的有效性。 相似文献
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基于动态规划的约束优化问题多参数规划求解方法及应用 总被引:1,自引:0,他引:1
结合动态规划和单步多参数二次规划, 提出一种新的约束优化控制问题多参数规划求解方法. 一方面能得到约束线性二次优化控制问题最优控制序列与状态之间的显式函数关系, 减少多参数规划问题求解的工作量; 另一方面能够同时求解得到状态反馈最优控制律. 应用本文提出的多参数二次规划求解方法, 建立无限时间约束优化问题状态反馈显式最优控制律. 针对电梯机械系统振动控制模型做了数值仿真计算. 相似文献
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《国际计算机数学杂志》2012,89(5):523-536
In this paper, we introduce a new algorithm for solving nonlinear programming (NLP) problems. It is an extension of Guo's algorithm [1] which possesses enhanced capabilities for solving NLP problems. These capabilities include: a) extending the variable subspace, b) adding a search process over subspaces and normalized constraints, c) using an adaptive penalty function, and d) adding the ability to deal with integer NLP problems, 0-1 NLP problems, and mixed-integer NLP problems which have equality constraints. These four enhancements increase the capabilities of the algorithm to solve nonlinear programming problems in a more robust and universal way. This paper will present results of numerical experiments which show that the new algorithm is not only more robust and universal than its competitors, but also its performance level is higher than any others in the literature. 相似文献
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A neural network for solving convex nonlinear programming problems is proposed in this paper. The distinguishing features
of the proposed network are that the primal and dual problems can be solved simultaneously, all necessary and sufficient optimality
conditions are incorporated, and no penalty parameter is involved. Based on Lyapunov, LaSalle and set stability theories,
we prove strictly an important theoretical result that, for an arbitrary initial point, the trajectory of the proposed network
does converge to the set of its equilibrium points, regardless of whether a convex nonlinear programming problem has unique
or infinitely many optimal solutions. Numerical simulation results also show that the proposed network is feasible and efficient.
In addition, a general method for transforming non-linear programming problems into unconstrained problems is also proposed.
ID="A1" Correspondence and offprint requests to: Dr Z Chen, Department of Electronic Engineering, Brunel University, Uxbridge, Middle-sex, UK 相似文献
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提出用遗传编程(GP)方法发现新的神经网络学习规则,并通过实验得到了几个新的学习规则;另外讨论了通过文法产生式的设计来改进对该问题的研究. 相似文献
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一种新的非线性规划神经网络模型 总被引:1,自引:0,他引:1
提出一种新型的求解非线性规划问题的神经网络模型.该模型由变量神经元、Lagrange乘子神经元和Kuhn-Tucker乘子神经元相互连接构成.通过将Kuhn-Tucker乘子神经元限制在单边饱和工作方式,使得在处理非线性规划问题中不等式约束时不需要引入松弛变量,避免了由于引入松弛变量而造成神经元数目的增加,有利于神经网络的硬件实现和提高神经网络的收敛速度.可以证明,在适当的条件下,文中提出的神经网络模型的状态轨迹收敛到与非线性规划问题的最优解相对应的平衡点. 相似文献
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A simplified neural network model is proposed to solve a class of linear matrix inequality problems. The stability and solvability
of the proposed neural network are analyzed and discussed theoretically. In comparison with the previous neural network models
(Lin and Huang, Neural Process Lett 11:153–169, 2000; Lin et al., IEEE Trans Neural Netw 11:1078–1092, 2000), the simplified
one is composed of two layers rather than three layers, and the neuron array in each layer is triangular rather than square.
The proposed approach can therefore reduce the complexity of the neural network architecture. In addition, the simplified
neural network can also be extended to solve multiple linear matrix inequalities with specific constraints, which enlarges
the application domain of the proposed approach. Finally, examples are given to illustrate the effectiveness and efficiency
of the simplified neural network. 相似文献
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环境监测是无线传感器网络的典型应用,事件边界检测是其中的重要内容。文中首先建立无线传感器网络数据的时空模型,提出基于线性神经网络的事件边界检测方法。该方法利用传感器数据流的时间相关性,基于线性神经网络预测与验证数据流,并确定异常数据集合。在此基础上,根据传感器节点之间的空间相关性进行事件边界检测,不仅可识别故障节点,而且能识别事件边界节点,从而准确估算事件发生的区域位置与大小。理论分析及实验表明,文中方法在获得较高的故障节点和事件边界节点的检测准确率的同时,保持较低的误判率。 相似文献