SLF混沌神经网络及其应用

针对混沌神经网络的单调激励函数,引入Legendre函数和Sigmoid函数组合作为非单调激励函数,构造了一种新的暂态混沌神经元模型（SLF模型）,并给出了此混沌神经元的倒分岔图和最大Lyapunov指数时间演化图,利用该模型构建了一种暂态混沌神经网络,通过对非线性函数优化和TSP问题的求解验证了该模型的有效性。     

Shannond小波混沌神经网络及其TSP（城市旅行商）问题的求解

混沌神经网络已经被证明是解决组合优化问题的有效工具．针对混沌神经网络的单调的激励函数。通过引入Shannond小波和Sigmoid函数加和组成的非单调激励函数,提出了一种新型的暂态混沌神经元模型．给出了该混沌神经元的倒分岔图和最大Lyapunov指数时间演化图,分析了其动力学特性．基于该模型,构造了一种暂态混沌神经网络,并将其应用于函数优化和组合优化问题．通过经典的10城市TSP验证了该暂态混沌神经网络的有效性．     

Shannon小波混沌神经网络及其TSP(城市旅行商)问题的求解

混沌神经网络已经被证明是解决组合优化问题的有效工具.针对混沌神经网络的单调的激励函数,通过引入Shannon小波和Sigmoid函数加和组成的非单调激励函数,提出了一种新型的暂态混沌神经元模型.给出了该混沌神经元的倒分岔图和最大Lyapunov指数时间演化图,分析了其动力学特性.基于该模型,构造了一种暂态混沌神经网络,并将其应用于函数优化和组合优化问题.通过经典的10城市TSP验证了该暂态混沌神经网络的有效性.     

求解任务指派问题的差异演化算法磁

建立了任务指派问题的数学模型,采用差异演化算法对其进行求解,给出了差异演化算法求解该问题的具体方案,对不同的任务指派问题算例进行了仿真实验。结果表明,算法可以有效、快速地找到任务指派问题的最优解。     

基于暂态混沌神经网络的多车调度混合优化算法

探讨车辆调度问题的解决方法．提出一种用于求解带容量约束的多车调度问题（CVRP）的混合优化算法．该算法分为路线划分、构造初始解和改进解3个阶段：第1阶段用模糊C均值聚类算法将所有客户按车容量要求装车；第2阶段用暂态混沌神经网络方法对每条路线排序；第3阶段用禁忌搜索法改进得到的解．最后采用标准问题进行仿真计算，通过与其他算法的比较，说明该算法是求解CVRP问题可行且高效的方法．     

自适应扰动暂态混沌神经网络MPSK盲检测

为提高MPSK信号盲检测算法的性能,针对CHNN_APHM算法易陷入局部最优的缺点,本文引入暂态混沌神经网络模型,使用新的模拟退火策略,加入扰动因子和混沌,提出带扰动的幅值相位型离散幅值多电平暂态混沌神经网络算法。算法使用暂态混沌神经网络提高抗噪性能,并在起始时刻使用混沌初始化获得原始信号,选取与发送信号相关性高的微小扰动因子使算法跳出局部最优解。实验仿真结果证明,带扰动的幅值相位型离散幅值多电平暂态混沌神经网络MPSK信号盲检测算法需要较少的起点个数,能在更小的信噪比和更短的数据长度下收敛,有效提高了抗干扰性能。     

基于暂态混沌神经网络的组播路由算法

讨论了高速包交换计算机网络中具有端到端时延的组播路由问题。首先给出了这类问题的网络模型及其数学描述，然后提出了基于暂态混沌神经网络的组播路由算法。实验结果表明，该算法能够快速有效地实现组播路由优化，并且计算性能及解的质量优于基于Hopfield神经网络的路由算法。     

用神经网络求解机器人动态调度中的近似指派问题

本文利用HOPFIELD神经网络，对机器人动态调度中的近似指派问题提出了合理的神经网络表示方法，给出了网络的能量函数表示法及神经元状态方程，从而得出了机器人动态调度中近似指派问题的快速求解策略，满足了动态调度的实时性要求。本文从理论上论证了所提算法的收敛性。软件仿真结果表明，本文提出的近似指派问题网络求解方法是有效的，计算结果是满意的。     

用神经网络求解机器人动态调度中的近似指派问题

本文利用HOPFIELD神经网络，对机器人动态调度中的近似指派问题提出了合理的神经网络表示方法，给出了网络的能量函数表示法及神经元状态方程，从而得出了机器人动态调度中近似指派问题的快速求解策略，满足了动态调度的实时性要求．本文从理论上论证了所提算法的收敛性．软件仿真结果表明，本文提出的近似指派问题网络求解方法是有效的，计算结果是满意的．     

改进粒子群优化算法求解任务指派问题

任务指派问题是典型NP难题，引入粒子群优化算法对其进行求解。建立了任务指派问题的数学模型，给出了粒子群优化算法求解任务指派问题的具体方案。为提高其优化求解效果，引入变异机制及局部更新机制对粒子群优化算法进行改进。实例及数字仿真验证了改进粒子群优化算法的有效性。     

Primal and dual assignment networks.

This paper presents two recurrent neural networks for solving the assignment problem. Simplifying the architecture of a recurrent neural network based on the primal assignment problem, the first recurrent neural network, called the primal assignment network, has less complex connectivity than its predecessor. The second recurrent neural network, called the dual assignment network, based on the dual assignment problem, is even simpler in architecture than the primal assignment network. The primal and dual assignment networks are guaranteed to make optimal assignment. The applications of the primal and dual assignment networks for sorting and shortest-path routing are discussed. The performance and operating characteristics of the dual assignment network are demonstrated by means of illustrative examples.     

Analysis and design of primal-dual assignment networks

The assignment problem is an archetypical combinatorial optimization problem having widespread applications. This paper presents two recurrent neural networks, a continuous-time one and a discrete-time one, for solving the assignment problem. Because the proposed recurrent neural networks solve the primal and dual assignment problems simultaneously, they are called primal-dual assignment networks. The primal-dual assignment networks are guaranteed to make optimal assignment regardless of initial conditions. Unlike the primal or dual assignment network, there is no time-varying design parameter in the primal-dual assignment networks. Therefore, they are more suitable for hardware implementation. The performance and operating characteristics of the primal-dual assignment networks are demonstrated by means of illustrative examples.     

Competition Based Neural Networks for Assignment Problems

Competition based neural networks have been used to solve the generalized assignment problem and the quadratic assignment problem.Both problems are very difficult and are ε approximation complete.The neural network approach has yielded highly competitive performance and good performance for the quadratic assignment problem.These neural networks are guaranteed to produce feasible solutions.     

A hill-jump algorithm of Hopfield neural network for shortest path problem in communication network

In this paper, we present a hill-jump algorithm of the Hopfield neural network for the shortest path problem in communication networks, where the goal is to find the shortest path from a starting node to an ending node. The method is intended to provide a near-optimum parallel algorithm for solving the shortest path problem. To do this, first the method uses the Hopfield neural network to get a path. Because the neural network always falls into a local minimum, the found path is usually not a shortest path. To search the shortest path, the method then helps the neural network jump from local minima of energy function by using another neural network built from a part of energy function of the problem. The method is tested through simulating some randomly generated communication networks, with the simulation results showing that the solution found by the proposed method is superior to that of the best existing neural network based algorithm.     

弹性需求下公交网络系统票价结构的优化

对具有弹性需求的城市公交网络系统票价的合理设定问题进行了研究分析.考虑到公 交收费结构的变化会影响乘客的出行需求量和乘客对路径选择行为,将这一问题描述为一个两 级数学规划问题.上一级问题是寻求收益最大的优化问题,下一级问题是估计乘客在网络上的 流量分布的具有弹性需求的随机用户平衡分配模型.鉴于两级规划问题的非凸性,提出基于灵 敏度分析的启发式算法.最后,给出一个仿真算例说明提出的模型和算法的合理性.     

A meta-heuristic algorithm to solve quadratic assignment formulations of cell formation problems without presetting number of cells

The purpose of cellular manufacturing (CM) is to find part-families and machine cells which form self-sufficient units of production with a certain amount of autonomy that result in easier control (Kusiak, 1987, 1990). One of the most important steps in CM is to optimally identify cells from a given part-machine incidence matrix. Several formulations of various complexities are proposed in the literature to deal with this problem. One of the mostly known formulations for CM is the quadratic assignment formulation (Kusiak and Chow, 1988). The problem with the quadratic assignment based formulation is the difficulty of its solution due to its combinatorial nature. The formulation is also known as NP-hard (Kusiak and Chow, 1988). In this paper a novel simulated annealing based meta-heuristic algorithm is developed to solve quadratic assignment formulations of the manufacturing cell formation problems. In the paper a novel solution representation scheme is developed. Using the proposed solution representation scheme, feasible neighborhoods can be generated easily. Moreover, the proposed algorithm has the ability to self determine the optimal number of cell during the search process. A test problem is solved to present working of the proposed algorithm.     

Using Laplacian Eigenmap as Heuristic Information to Solve Nonlinear Constraints Defined on a Graph and Its Application in Distributed Range-Free Localization of Wireless Sensor Networks

In this paper, we are concerned with the problem of nonlinear inequalities defined on a graph. The feasible solution set to this problem is often infinity and Laplacian eigenmap is used as heuristic information to gain better performance in the solution. A continuous-time projected neural network, and the corresponding discrete-time projected neural network are both given to tackle this problem iteratively. The convergence of the neural networks are proven in theory. The effectiveness of the proposed neural networks are tested and compared with others via its applications in the range-free localization of wireless sensor networks. Simulations demonstrate the effectiveness of the proposed methods.     

On finding and detecting efficient assignments in the case of multiple inputs and outputs

An interesting variant of the assignment problem is the case where each partial assignment of an individual to a job involves multiple inputs and outputs. In this paper, three issues about this problem are discussed: finding an efficient assignment, verifying the efficiency of a solution and restoring the efficiency of an inefficient assignment. For the first issue, a current method, proposed by Chen and Lu, is compared with a proposed multiobjective formulation and for the second and third ones, a two-phase method is developed, which is based on the simplex method and the Dantzig–Wolfe decomposition algorithm.     

A Reliability-Based Stochastic Traffic Assignment Model for Network with Multiple User Classes under Uncertainty in Demand

This paper presents a novel reliability-based stochastic user equilibrium traffic assignment model in view of the day-to-day demand fluctuations for multi-class transportation networks. In the model, each class of travelers has a different safety margin for on-time arrival in response to the stochastic travel times raised from demand variations. Travelers' perception errors on travel time are also considered in the model. This model is formulated as an equivalent variational inequality problem, which is solved by the proposed heuristic solution algorithm. Numerical examples are presented to illustrate the applications of the proposed model and the efficiency of solution algorithm.