基于Hopfield神经网络的作业车间生产调度方法

该文提出了基于Hopfield神经网络的作业车间生产调度的新方法.文中给出了作业车 间生产调度问题(JSP)的约束条件及其换位矩阵表示,提出了新的包括所有约束条件的计算能 量函数表达式,得到相应的作业车间调度问题的Hopfield神经网络结构与权值解析表达式,并 提出相应的Hopfield神经网络作业车间调度方法.为了避免Hopfield神经网络容易收敛到局部 极小,从而产生非法调度解的缺点,将模拟退火算法应用于Hopfield神经网络求解,使Hopfield 神经网络收敛到计算能量函数的最小值0,从而保证神经网络输出是一个可行调度方案.该文 改进了已有文献中提出的作业调度问题的Hopfield神经网络方法,与已有算法相比,能够保证 神经网络稳态输出为可行的作业车间调度方案.     

一种Hopfield神经网络能量函数改进下的作业车间调度*

由于作业车间调度问题的目标函数目前还无法用换位矩阵的元素以数学公式的形式表示,因此无法保证求出全局最优解。文中首先对换位矩阵表示方法进行了改进,给出新的带有目标函数的能量函数表达式,然后提出改进的Hopfield神经网络作业车间调度方法,并将模拟退火应用于Hopfield神经网络求解,避免了陷入局部极值。仿真结果表明,该方法具有全局搜索能力,并能够保证神经网络的稳态输出为全局最优或近似全局最优。     

使用模糊竞争Hopfield网络进行图像分割

针对传统自组织竞争学习方法的不足,将模糊竞争学习引入竞争Hopfield网络中,由此设计了一个用于图像分割的模糊竞争Hopfield网络,通过将图像空间映射到灰度特征空间,实现灰度特征集的模糊聚类,进而实现图像分割.实验结果表明:对于二值分割,与Ostu方法相比,此算法在分割效果和对噪声的自适应能力方面具有明显的优点.对于多类分割,此算法比目前的FCM(fuzzy C mean)算法的处理速度要快.     

Development of neurofuzzy architecture for solving the N-Queens problem

Neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural networks for solving the N-Queens problem. More specifically, a modified Hopfield network is developed and its internal parameters are explicitly computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points, which represent a solution of the considered problem. The network is shown to be completely stable and globally convergent to the solutions of the N-Queens problem. A fuzzy logic controller is also incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.     

一种新的传感器网络混合广播调度方法

由于传感器网络所使用无线信道的共享性和相互干扰, 节点间数据广播会产生资源冲突, 广播调度要解决的即是为每个节点分配到一个无冲突传输时隙, 其目标是找到最优时分复用(TDMA: time division multiple access)调度解, 使得帧长度最短而信道利用率最大. 提出基于神经网络的两阶段混合广播调度算法. 在阶段一, 使用改进的顶点着色算法来获得调度所需最短时隙数目; 在阶段二, 使用模糊Hopfield网络将节点模糊聚类为M类, 同类 节点可以在同一时隙被调度, 不同类节点必须在不同时     

Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement

The paper presents a fuzzy neural network system for edge detection and enhancement. The system can both: (a) obtain edges and (b) enhance edges by recovering missing edges and eliminate false edges caused by noise. The research is comprised of three stages, namely, adaptive fuzzification which is employed to fuzzify the input patterns, edge detection by a three-layer feedforward fuzzy neural network, and edge enhancement by a modified Hopfield neural network. The typical sample patterns are first fuzzified. Then they are used to train the proposed fuzzy neural network. After that, the trained network is able to determine the edge elements with eight orientations. Pixels having high edge membership are traced for further processing. Based on constraint satisfaction and the competitive mechanism, interconnections among neurons are determined in the Hopfield neural network. A criterion is provided to find the final stable result that contains the enhanced edge measurement. The proposed neural networks are simulated on a SUN Sparc station. One hundred and twenty-three training samples are well chosen to cover all the edge and non-edge cases and the performance of the system will not be improved by adding more training samples. Test images are degraded by random noise up to 30% of the original images. Compared with standard edge detection operators and enhancement techniques, the proposed system based on the neuro-fuzzy synergism obtains very good results.     

Mutation Hopfield neural network and its applications

In this paper, a new operator is proposed to optimize the traditional Hopfield neural network (HNN). The key idea is to incorporate the global search capability of the Estimation of Distribution Algorithms (EDAs) into the HNN, which typically has a powerful local search capability and fast operation. On account of this property of the EDA, our proposed algorithm also exhibits a powerful global search capability. In addition, the possible infeasible solutions generated during the re-sampling period of the EDA are eliminated by the HNN. Therefore, the merits of both these methods are combined in a unified framework. The proposed model is tested on a numerical example, the max-cut problem. The new and optimized model yielded a better performance than certain traditional intelligent optimization methods, such as HNN, genetic algorithm (GA). The proposed mutation Hopfield neural network (MHNN) is also used to solve a practical problem, aircraft landing scheduling (ALS). Compared with first-come-first-served sequence, MHNN sequence reduces both total landing time and total delay.     

基于神经网络模型的有约束的FMS资源调度

本文介绍了用神经网络求解ＦＭＳ中有约束的资源调度问题的方法，有约束的资源调度问题首和无被分解成一系列多维背包模型并且为背包模型建立了一个等价的Ｈｏｐｆｉｅｌｄ神经网络，然后通过扩展Ｈｏｐｆｉｅｌｄ网络，给出了一种求解有约束的资源调度问题的方法。这咱方法可以避免通常神经网络所具有的不稳定性和容易陷入局部极小点的缺陷。     

用神经网络求解划分问题

划分问题是一类常见的ＮＰ完备的优化问题,本文利用推广的Ｈｏｐｆｉｅｌｄ神经网络模型解决了划分问题,并取得了较好的效果,为这个总理２的解决提供了一条新的途径。同时也为解决其它优化总理２提供了有益的启示。     

模糊PID和Hopfield神经网络在矿井通风系统中的应用

针对传统的PID控制或者单一的模糊控制无法准确控制矿井通风系统风量的问题,提出了一种采用模糊PID调节器和Hopfield神经网络调节器对矿井通风机的转速、风门、风量进行控制的方法。该方法利用模糊控制器对PID参数进行实时修正,并结合Hopfield神经网络的联想记忆功能和反馈调节特性,实现矿井通风机风量的快速、稳定输出。仿真与实验结果表明,模糊PID调节器和Hopfield神经网络调节器可以准确控制矿井通风机的转速和风量,实现通风系统的稳定输出。     

基于神经网络的图象序列特征点匹配

利用神经网络优化技术解决图象序列的特征点匹配问题，将特征点匹配归结为一个带约束的优化问题，并用2D Hopfield网络实现，在Hopfield网络的能量函数的设计中，综合考虑了特征点的预测结果、特征点的遮挡等情况，从而克服了现有的多数方法所存在的误匹配现象，对于特征点的跟踪，头3帧图象的正确匹配是十分关键的。本文提出了一种3D Hopfield网络用以解决头3帧图象的特征点匹配，并提出了一个运动平滑性的代价函数用以构造3D Hopfield网络的能量函数，实际图象序列的实验结果证明了本方法的有效性。     

基于神经网络量子算法的多用户检测器

利用遗传量子算法和Hopfield神经网络，提出了一种融合两种算法优点的神经网络量子算法，并将其应用到CDMA通信系统的多用户检测问题中。所提算法把神经网络嵌入到遗传量子算法的每一代中，可进一步提高量子种群的适应度函数值。通过混合神经网络到GQA中，还可加快GQA的收敛速度进而减少算法的计算复杂度。另外，GQA所提供的良好初值改善了HNN的性能，嵌入的HNN也提高了GQA的性能。仿真结果证明了该方法的抗多址干扰能力和抗远近效应能力都优于传统检测器和一些应用智能算法的多用户检测器。     

Hopfield神经网络在机组组合问题中的应用

提出利用多层Hopfield神经网络求解机组组合优化问题。通过构造合适的能量函数使得单层Hopfield神经网络可以解决某一时刻的机组出力问题,与之相对应的多层神经网络可以解决任意时间段的机组出力问题。多层Hopfield神经网络的层数由所需求解问题的时间段确定。给出单层及多层神经网络的能量函数及求解算法,能量函数考虑到机组升降功率和出力上下限的约束。通过对已有文献的算例进行计算比对,所得结果和遗传算法基本一致,但Hopfield神经网络通过解微分方程组来确定最优解,计算时间相对较少。     

A Symmetric Probabilistic Encryption Scheme Based On CHNN Without Data Expansion

In 1999, Guo et al. proposed a new probabilistic symmetric probabilistic encryption scheme based on chaotic attractors of neural networks. The scheme is based on chaotic properties of the Overstoraged Hopfield Neural Network (OHNN). The approach bridges the relationship between neural network and cryptography. However, there are some problems in their scheme: (1) exhaustive search is needed to find all the attractors; (2) the data expansion in the paper is wrongly derived; (3) problem exists on creating the synaptic weight matrix. In this letter, we propose a symmetric probabilistic encryption scheme based on Clipped Hopfield Neural Network (CHNN), which solves the above mentioned problems. Furthermore, it keeps the length of the ciphertext equals to that of the plaintext.     

Max-Product型模糊Hopfield网络稳定性及其聚类方法研究

本文将模糊逻辑和神经网络相结合,提出了Ｍａｘ－Ｐｒｏｄｕｃｔ型Ｈｏｐｆｉｅｌｄ人工神经网络,给出了它的网络结构和形式化描述,证明了ＦｕｚｚｙＨＮ的稳定性,最后通过理论和数值实验对基于Ｍａｘ－Ｐｒｏｄｕｃｔ型Ｈｏｐｆｉｅｌｄ网络的动态聚类过程和有关性质进行了研究。     

一种基于神经网络的生产调度方法

提出解决具有开、完工期限制的约束Job-shop生产调度问题的一种神经网络方法. 该方法通过约束神经网络,描述各种加工约束条件,并对不满足约束的开工时间进行相应调 节,得到可行调度方案;然后由梯度搜索算法优化可行调度方案,直至得到最终优化可行调度 解.理论分析、仿真实验表明了方法的有效性.     

An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem

In this paper, an approach to solving the classical Traveling Salesman Problem (TSP) using a recurrent network of linear threshold (LT) neurons is proposed. It maps the classical TSP onto a single-layered recurrent neural network by embedding the constraints of the problem directly into the dynamics of the network. The proposed method differs from the classical Hopfield network in the update of state dynamics as well as the use of network activation function. Furthermore, parameter settings for the proposed network are obtained using a genetic algorithm, which ensure a stable convergence of the network for different problems. Simulation results illustrate that the proposed network performs better than the classical Hopfield network for optimization.     

基于量子神经网络的MIMO信号检测技术

MIMO信号的最优检测在常规条件下是一NP难解问题。利用量子并行计算和量子纠缠等特性,量子计算与人工神经网络结合的量子神经网络能有效的解决这一问题。本文采用Hopfield神经网络实现MIMO信号检测,利用基于检测序列最大后验概率最佳接收似然函数与Hopfield神经网络的能量函数对应关系,构造一种量子神经网络的MIMO检测器。计算仿真结果表明:本文所提出的检测器在误码率方面有良好的性能。     

A New Relaxation Procedure in the Hopfield Network for Solving Optimization Problems

When solving an optimization problem with a Hopfield network, a solution is obtained after the network is relaxed to an equilibrium state. The relaxation process is an important step in achieving a solution. In this paper, a new procedure for the relaxation process is proposed. In the new procedure, the amplified signal received by a neuron from other neurons is treated as the target value for its activation (output) value. The activation of a neuron is updated directly based on the difference between its current activation and the received target value, without using the updating of the input value as an intermediate step. A relaxation rate is applied to control the updating scale for a smooth relaxation process. The new procedure is evaluated and compared with the original procedure in the Hopfield network through simulations based on 200 randomly generated instances of the 10-city traveling salesman problem. The new procedure reduces the error rate by 34.6% and increases the percentage of valid tours by 194.6% as compared with the original procedure.     

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.