多准则图象重建的神经网络模型及实现

本文提出一种采用Ｈｏｐｆｉｅｌｅ神经网络（Ｈｏｐｆｉｅｌｄ Ｎｅｉｒａｌ Ｎｅｔｗｏｒｋ简称ＨＮＮ）优化的图象重建算法。将图象重建问题转化为ＨＮＮ优化问题,取重建图象的峰值函数最小以及原始投影与再投影之间的误差平方和最小作为图象重建的优化目标,作为能量函数构造连续型ＨＮＮ模型,由ＨＮＮ能量函数极小化可得到重建问题的优化解。这种方法具有简单、计算量小、收敛快、便于并行计算等特点。对照ＡＲＴ算法,用计算机模拟产生的无噪声投影数据检验新算法,验证了新算法的优越性。     

反馈电压偏置型Hopfield 网络电域盲检测相干光通信信号

为解决无线分集相干光接收机的自适应盲检测问题,提出了一种新的离散时间连续状态的网络输出反馈偏置型的复Hopfield 神经网络用以解决多值QAM 信号的盲检测问题。反馈电压偏置的引入即不脱离传统Hopfield 模型,又能有效满足多值信号检测时所需的搜索空间变大的特殊要求。全文完成多值信号盲检测的优化问题构造和能量函数的映射,给出能量函数的证明、分析和它的约束条件,给出适用该问题的激活函数的基本特征,正确盲检测信号的权矩阵的配置方法。最后,通过详细的仿真结果展示和与其他算法性能对比进一步验证算法的有效性和优越性并指出算法所存在的问题和下一步的研究方向。     

用Hopfield网络实现边缘模糊图象的二值化处理

本文基于Hopfield网络提出了一个实现边缘模糊图象二值化处理的新方法.首先将图象二值化处理问题转化成优化问题,然后构造相应的Hopfield网络参数并用Hopfield网络实现这个优化问题的解.实验说明,该方法具有较高的精度,同时对较小图象,甚至一维信号亦具有好的效果.     

Sequential and parallel image restoration: neural networkimplementations

Sequential and parallel image restoration algorithms and their implementations on neural networks are proposed. For images degraded by linear blur and contaminated by additive white Gaussian noise, maximum a posteriori (MAP) estimation and regularization theory lead to the same high dimension convex optimization problem. The commonly adopted strategy (in using neural networks for image restoration) is to map the objective function of the optimization problem into the energy of a predefined network, taking advantage of its energy minimization properties. Departing from this approach, we propose neural implementations of iterative minimization algorithms which are first proved to converge. The developed schemes are based on modified Hopfield (1985) networks of graded elements, with both sequential and parallel updating schedules. An algorithm supported on a fully standard Hopfield network (binary elements and zero autoconnections) is also considered. Robustness with respect to finite numerical precision is studied, and examples with real images are presented.     

用人工神经网络进行FBLP阵处理

本文给出了一种Ｈｏｐｆｉｅｌｄ神经网络，它的状态空间不再为双值空间（如《０．１》等），而为一实数空间。此网络可用地复数域中的最优估计，文中将此网络用于ＦＢＬＰ阵处理，从而为ＢＬＰ阵处理提供了一条计算有效的途径，模拟结果表明此网络是可行的。     

能量极小化的一种启发式遗传算法

Chakradhar et.al(1988,1990)将组合电路表示为Hopfield神经网络,将测试生成问题转化为一个组合优化问题。本文在传统遗传算法的基础上,结合电路的拓扑信息,提出了一种用于组合电路神经网络模型能量极小化的启发式遗传算法。     

Static and dynamic channel assignment using neural networks

We examine the problem of assigning calls in a cellular mobile network to channels in the frequency domain. Such assignments must be made so that interference between calls is minimized, while demands for channels are satisfied. A new nonlinear integer programming representation of the static channel assignment (SCA) problem is formulated. We then propose two different neural networks for solving this problem. The first is an improved Hopfield (1982) neural network which resolves the issues of infeasibility and poor solution quality which have plagued the reputation of the Hopfield network. The second approach is a new self-organizing neural network which is able to solve the SCA problem and many other practical optimization problems due to its generalizing ability. A variety of test problems are used to compare the performance of the neural techniques against more traditional heuristic approaches. Finally, extensions to the dynamic channel assignment problem are considered     

A compact dynamic channel assignment scheme based on Hopfield networks for cellular radio systems

In this paper, a new channel assignment strategy named compact dynamic channel assignment (CDCA) is proposed. The CDCA differs from other strategies by consistently keeping the system in the utmost optimal state, and thus the scheme allows to determine a call succeeding or failing by local information instead of that of the whole network. It employs Hopfield neural networks for optimization which avoids the complicated assessment of channel compactness and guarantees optimum solutions for every assignment. A scheme based on Hopfield neural network is considered before; however, unlike others, in this algorithm an energy function is derived in such a way that for a neuron, the more a channel is currently being allocated in other cells, the more excitation the neuron will acquire, so as to guarantee each cluster using channels as few as possible. Performance measures in terms of the blocking probability, convergence rate and convergence time are obtained to assess the viability of the proposed scheme. Results presented show that the approach significantly reduces stringent requirements of searching space and convergence time. The algorithm is simple and straightforward, hence the efficient algorithm makes the real‐time implementation of channel assignment based on neural network feasibility. Copyright © 2008 John Wiley & Sons, Ltd.     

A mathematical framework for solving dynamic optimization problemswith adaptive networks

The paper develops a mathematical framework for solving dynamic optimization problems with adaptive networks (AN's) based on Hopfield networks. The dynamic optimization problem (DOP) includes a dynamic traveling salesman problem (TSP), in which the distance between any pair of cities in the conventional TSP is extended into a time variable. Compared to previous deterministic networks, such as the Hopfield network, the adaptive network has the most distinguished feature: it can change its states, continually reacting to inputs from the outside environment. From the scientific viewpoint, our framework demonstrates mathematically rigorously that the adaptive network produces as final states locally minimum solutions to the DOP. From the engineering viewpoint, it provides a mathematical basis for developing engineering devices, such as very large scale integration (VLSI), that can solve real world DOP's efficiently     

Channel Assignment for Cellular Networks Based on a Local Modified Hopfield Neural Network

In this paper, we propose a modified discrete Hopfield neural networks algorithm for the channel assignment problem. In opposition to previous work, we tried to apply the optimization locally on a per cell basis in order to reduce the CPU processing time and decrease the designed system complexity while obtaining a near-optimum solution. In addition, the research is extended to study the algorithm performance in a more realistic cellular system where the number of requested channels is continuously changing with time. In this paper, the channel assignment problem is formulated as an energy function which is at its minimum when all the defined compatibility constraints are satisfied and the assigned channel number (ACN) is equal to the requested channel number (RCN) in each cell.     

遗传算法在多用户检测中的应用研究

本文针对CDMA系统中多用户检测的组合优化问题,提出一种结合遗传算法和Hopfield神经网络的检测方法。该方法首先由遗传算法给神经网络提供一个初始解,神经网络在此基础上再进行局部寻优。研究表明：这种方法具有平方的计算复杂度,优于Hopfield神经网络检测方法、以及单独采用遗传算法的检测方法,对远近问题不敏感,具有良好的误码率性能和抗多址干扰性能。     

A Hough transform system based on neural networks

Neural-like analog circuitry is suggested for image-to-parameter-space mapping, and a modified Hopfield optimization network is proposed for the parameter space peak detection. Solution time under 50 μs is obtainable with general-purpose operational amplifiers. Example system applications include autonomous navigation, tracking multiple targets, curve following, mensuration, and image recognition     

A Context-Sensitive Technique for Unsupervised Change Detection Based on Hopfield-Type Neural Networks

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. This technique is based on a modified Hopfield neural network architecture designed to model spatial correlation between neighboring pixels of the difference image produced by comparing images acquired on the same area at different times. Each spatial position in the considered scene is represented by a neuron in the Hopfield network that is connected only to its neighboring units. These connections model the spatial correlation between neighboring pixels and are associated with a context-sensitive energy function that represents the overall status of the network. Change detection maps are obtained by iteratively updating the output status of the neurons until a minimum of the energy function is reached and the network assumes a stable state. A simple heuristic thresholding procedure is presented and adopted for initializing the network. The proposed change detection technique is unsupervised and distribution free. Experimental results carried out on two multispectral and multitemporal remote sensing images confirm the effectiveness of the proposed technique     

连续型神经网络解决信元调度问题的参数分析

本文利用连续型Hopfield神经网络实现信元调度问题,对采用的新的能量函数进行仿真模拟,通过对网络模型的参数特性进行研究分析,寻找最佳的取值范围,为Hopfield神经网络今后的研究和实际应用提供帮助。     

Network lifetime maximization with cross-layer design in wireless sensor networks

This paper investigates a cross-layer design approach for minimizing energy consumption and maximizing network lifetime (NL) of a multiple-source and single-sink (MSSS) WSN with energy constraints. The optimization problem for MSSS WSN can be formulated as a mixed integer convex optimization problem with the adoption of time division multiple access (TDMA) in medium access control (MAC) layer, and it becomes a convex problem by relaxing the integer constraint on time slots. Impacts of data rate, link access and routing are jointly taken into account in the optimization problem formulation. Both linear and planar network topologies are considered for NL maximization (NLM). With linear MSSS and planar single-source and single-sink (SSSS) topologies, we successfully use Karush-Kuhn-Tucker (KKT) optimality conditions to derive analytical expressions of the optimal NL when all nodes are exhausted simultaneously. The problem for planar MSSS topology is more complicated, and a decomposition and combination (D&C) approach is proposed to compute suboptimal solutions. An analytical expression of the suboptimal NL is derived for a small scale planar network. To deal with larger scale planar network, an iterative algorithm is proposed for the D&C approach. Numerical results show that the upper-bounds of the network lifetime obtained by our proposed optimization models are tight. Important insights into the NL and benefits of cross-layer design for WSN NLM are obtained.     

APPLYING OONN TO SOLVE MAP-COLORING PROBLEM

For solving the map-coloring problems,this paper presents an energy function,amore effective dynamic equation and a more simple convergence condition.For the first time westudy the map-coloring problems in the way of connecting discrete Hopfield neural network withthe orthogonal optimization,and as a practical example,a color map of China is given.     

The application of competitive Hopfield neural network to medical image segmentation

In this paper, a parallel and unsupervised approach using the competitive Hopfield neural network (CHNN) is proposed for medical image segmentation. It is a kind of Hopfield network which incorporates the winner-takes-all (WTA) learning mechanism. The image segmentation is conceptually formulated as a problem of pixel clustering based upon the global information of the gray level distribution. Thus, the energy function for minimization is defined as the mean of the squared distance measures of the gray levels within each class. The proposed network avoids the onerous procedure of determining values for the weighting factors in the energy function. In addition, its training scheme enables the network to learn rapidly and effectively. For an image of n gray levels and c interesting objects, the proposed CHNN would consist of n by c neurons and be independent of the image size. In both simulation studies and practical medical image segmentation, the CHNN method shows promising results in comparison with two well-known methods: the hard and the fuzzy c-means (FCM) methods.     

Analysis of Joint Multiband Sensing‐Time M‐QAM Signal Detection in Cognitive Radios

We analyze a wideband spectrum in a cognitive radio (CR) network by employing the optimal adaptive multiband sensing‐time joint detection framework. This framework detects a wideband M‐ary quadrature amplitude modulation (M‐QAM) primary signal over multiple nonoverlapping narrowband Gaussian channels, using the energy detection technique so as to maximize the throughput in CR networks while limiting interference with the primary network. The signal detection problem is formulated as an optimization problem to maximize the aggregate achievable secondary throughput capacity by jointly optimizing the sensing duration and individual detection thresholds under the overall interference imposed on the primary network. It is shown that the detection problems can be solved as convex optimization problems if certain practical constraints are applied. Simulation results show that the framework under consideration achieves much better performance for M‐QAM than for binary phase‐shift keying or any real modulation scheme.     

基于免疫算法的Hopfield神经网络多用户检测器

提出的基于免疫算法的Hopfield神经网络多用户检测器，将扰乱的Hopfield神经网络多用户检测器的输出作为免疫算法的初始种群，利用了免疫算法的全局收敛的特点，从而克服了Hopfield易收敛到局部能量最小点的缺点。理论分析和仿真结果表明：该检测器具有良好的抗多址干扰和抗远近效应的能力。     

An optimal power-dispatching control system for the electrochemical process of zinc based on backpropagation and Hopfield neural networks

This paper describes an optimization problem to minimize the cost of power consumption for the electrochemical process of zinc (EPZ) depending on varying prices of electrical power. A series of conditional experiments was conducted to obtain enough data, which reflect the complex relationships among the factors influencing power consumption. Two backpropagation neural networks are used to build a process model that describes these relationships. An equivalent Hopfield neural network is constructed to solve this nonlinear optimization problem with technological constraints, a penalty function is introduced into the network energy function to meet the equality constraints, and inequality constraints are removed by altering the sigmoid function. An optimal power-dispatching control system (OPDCS) has been developed to provide an optimal power-dispatching scheme and keep the EPZ running economically. Since the OPDCS was put into service in a smeltery, the cost of power consumption has decreased significantly, and it also contributes to balancing the power grid load.