基于混沌神经网络的移动通信信道分配方法研究

该文应用混沌神经网络求解信道分配问题,给出了信道分配的能量函数表达式和混沌神经网络模型,研究了判别混沌神经网络混沌特性的Lyapunov指数法,讨论了网络模型参数对网络混沌特性的影响,提出了基于混沌神经网络的信道分配算法.仿真结果表明,混沌神经网络具有复杂的瞬态混沌特性,它比Hopfield网络具有更强的搜索全局最优解的能力,和更快的收敛速度.     

A multistage self-organizing algorithm combined transiently chaotic neural network for cellular channel assignment

In this paper, a new multistage self-organizing channel assignment algorithm with a transiently chaotic neural network (MSSO-TCNN) is proposed as an optimization algorithm. The algorithm is used for assigning channels in cellular mobile networks to cells in the frequency domain. The MSSO-TCNN consists of a progressively initial channel assignment stage and the TCNN assignment stage. According to the difficulty measure of each cell, the first stage is executed to assign channels cell by cell inspired by the mechanism of bristle. If the optimum assignment solution is not obtained in the first stage, the TCNN stage is then applied to continue the channel assignment until the optimum assignment is made or a maximum number of iterations is reached. A salient feature of the TCNN model is that chaotic neurodynamics are temporarily generated for searching and self-organizing in order to escape local minima. Therefore, the neural network gradually approaches, through transient chaos, a dynamical structure similar to conventional models such as the Hopfield neural network and converges to a stable equilibrium point. A variety of testing problems are used to compare the performance of the MSSO-TCNN against existing heuristic approaches. Simulation results show that the MSSO-TCNN improves performance substantially through solving well-known benchmark problems within comparable numbers of iterations to most existing algorithms.     

Hopfield神经网络在B-ISDN路由选择中的应用

宽带综合业务数字网（Ｂ－ＩＳＤＮ）采用异步转移模式（ＡＴＭ）作为其传输技术,引入了虎通道（ＶＰ）和虚通路（ＶＣ）概念。本文针对Ｂ－ＩＳＤＮ中的ＶＰ路由问题,提出了一种基于Ｈｏｐｆｉｅｌｄ神经网络的ＶＰ路由选择算法,给出了神经网络能量函数的表示方法及神经元的状态方程。计算机模拟结果表明,本算法能根据网络的物理结构和业务需求情况,快速、有效地实现ＶＰ路由选择,提高网络的生存性。     

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。     

Cellular radio channel assignment using a modified Hopfield network

The channel-assignment problem is important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal channel-assignment problem has become increasingly important. A new channel-assignment algorithm using a modified Hopfield (1985, 1986) neural network is proposed. The channel-assignment problem is formulated as an energy-minimization problem that is implemented by a modified discrete Hopfield network. Also, a new technique to escape the local minima is introduced. In this algorithm, an energy function is derived, and the appropriate interconnection weights between the neurons are specified. The interconnection weights between the neurons are designed in such a way that each neuron receives inhibitory support if the constraint conditions are violated and receives excitatory support if the constraint conditions are satisfied. To escape the local minima, if the number of assigned channels are less than the required channel numbers (RCNs), one or more channels are assigned in addition to already assigned channels such that the total number of assigned channels is the same as the required number of channels in the cell even though the energy is increased. Various initialization techniques, which use the specific characteristics of frequency-assignment problems in cellular radio networks, such as cosite constraint (CSC), adjacent channel constraint (ACC), and cochannel constraint (CCC), and updating methods are investigated. In the previously proposed neural-network approach, some frequencies are fixed to accelerate the convergence time. In our algorithms, no frequency is fixed before the frequency-assignment procedure. This new algorithm, together with the proposed initialization and updating techniques and without fixing frequencies in any cells, has better performance results than the results reported previously utilizing fixed frequencies in certain cells     

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     

基于免疫克隆量子算法的多用户检测器

为了解决CDMA系统最佳多用户检测的高计算复杂度问题,基于免疫克隆选择理论和新的遗传量子算法,该文提出了免疫克隆量子算法。该算法把根据神经网络制作的疫苗接种到克隆量子算法的每一代中,通过接种疫苗到CQA中,可以加快CQA的收敛速度减少计算复杂度。另外,CQA所提供的好的初值可以改善疫苗的性能,接种的疫苗还改善了CQA的性能,文中给出了在免疫克隆量子算法中使用随机神经网络制作疫苗的统一理论框架结构。仿真结果证明了该方法不仅能够快速收敛到全局最优解,并且无论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以前智能计算算法的多用户检测器。     

Image restoration using a modified Hopfield network

A modified Hopfield neural network model for regularized image restoration is presented. The proposed network allows negative autoconnections for each neuron. A set of algorithms using the proposed neural network model is presented, with various updating modes: sequential updates; n-simultaneous updates; and partially asynchronous updates. The sequential algorithm is shown to converge to a local minimum of the energy function after a finite number of iterations. Since an algorithm which updates all n neurons simultaneously is not guaranteed to converge, a modified algorithm is presented, which is called a greedy algorithm. Although the greedy algorithm is not guaranteed to converge to a local minimum, the l (1) norm of the residual at a fixed point is bounded. A partially asynchronous algorithm is presented, which allows a neuron to have a bounded time delay to communicate with other neurons. Such an algorithm can eliminate the synchronization overhead of synchronous algorithms.     

Channel assignment in cellular radio using genetic algorithms

The channel assignment problem has become increasingly important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal assignment problem of channels has become increasingly important. Recently Genetic Algorithms (GAs) have been proposed as new computational tools for solving optimization problems. GAs are more attractive than other optimization techniques, such as neural networks or simulated annealing, since GAs are generally good at finding an acceptably good global optimal solution to a problem very quickly. In this paper, a new channel assignment algorithm using GAs is proposed. The channel assignment problem is formulated as an energy minimization problem that is implemented by GAs. Appropriate GAs operators such as reproduction, crossover and mutation are developed and tested. In this algorithm, the cell frequency is not fixed before the assignment procedures as in the previously reported channel assignment algorithm using neural networks. The average generation numbers and the convergence rates of GAs are shown as a simulation result. When the number of cells in one cluster are increased, the generation numbers are increased and the convergence rates are decreased. On the other hand, with the increased minimal frequency interval, the generation numbers are decreased and the convergence rates are increased. The comparison of the various crossover and mutation techniques in a simulation shows that the combination of two points crossover and selective mutation technique provides better results. All three constraints are also considered for the channel assignments: the co-channel constraint, the adjacent channel constraint and the co-site channel constraint. The goal of this paper is the assignment of the channel frequencies which satisfied these constraints with the lower bound number of channels.     

Classification of multispectral images based on a fuzzy-possibilistic neural network

In this paper, a new Hopfield-model net based on fuzzy possibilistic reasoning is proposed for the classification of multispectral images. The main purpose is to modify the Hopfield network embedded with fuzzy possibilistic C-means (FPCM) method to construct a classification system named fuzzy-possibilistic Hopfield net (FPHN). The classification system is a paradigm for the implementation of fuzzy logic systems in neural network architecture. Instead of one state in a neuron for the conventional Hopfield nets, each neuron occupies 2 states called membership state and typicality state in the proposed FPHN. The proposed network not only solves the noise sensitivity fault of Fuzzy C-means (FCM) but also overcomes the simultaneous clustering problem of possibilistic C-means (PCM) strategy. In addition to the same characteristics as the FPCM algorithm, the simple features of this network are clear potential in optimal problem. The experimental results show that the proposed FPHN can obtain better solutions in the classification of multispectral images.     

A Neural Network-Based Blind Multiuser Receiver for DS-CDMA Communication Systems

The Universal Mobile Telecommunications System (UMTS) which is based on Wideband-Code Division Multiple Access (W-CDMA) techniques is one of the most important broadband wireless communication systems. Adaptive Blind Multiuser Detection was widely considered for mobile receivers. The main drawback of this approach is that it achieves the optimum solution after a certain number of bit times. This paper deals with a new neural network approach in order to reduce the convergence time in different application environments. In particular, a modified Kennedy-Chua neural network, based on the Hopfield model is proposed. The neural network stability was investigated by means of a suitable analytical approach, while the performance of the proposed receiver scheme was derived by means of computer simulations. The numerical results shown in this paper highlight a fast convergence behavior of the proposed scheme, in particular under multipath-fading conditions. This revised version was published online in July 2006 with corrections to the Cover Date.     

Neural network-based dynamic channel assignment for cellular mobilecommunication systems

Conventional dynamic channel assignment schemes are both time-consuming and algorithmically complex. An alternative approach, based on cascaded multilayered feedforward neural networks, is proposed and examined on two cellular systems with different configurations. Simulation results showed that the blocking performance of our multistage neural network approach can match that of an example conventional scheme with less complexity and higher computational efficiency. The example scheme considered here is the ordered channel search, which can achieve a reasonably high spectral efficiency as compared to that of an ideal dynamic channel allocation algorithm. We conclude that our neural network approach is well-suited to the dynamic channel allocation problem of future cellular or microcellular systems with decentralized control     

一种新的基于混沌神经网络的动态路由选择算法

针对通信网的路由选择问题,提出了一种动态路由选择的混沌神经网络实现方法。所提出的此方法具有许多优良特性,即暂态混沌特性和平稳收敛特性,能有效地避免传统Hopfield神经网络极易陷入局部极值的缺陷。它通过短暂的倒分叉过程,能很快进入稳定收敛状态。实验证明了本算法能实时、有效地实现通信网的路由选择,并且当通信网中的业务量发生变化时,算法能自动调整最短路径和负载平衡之间的关系。     

一种新的复值递归神经网络训练方法及其应用

一种新的基于数字滤波器理论的全互连复值递归神经网络训练方法被提出.每个递归神经元均具有复数ⅡR滤波器结构.通过优化ⅡR滤波器的系数来更新神经网络的权值,而优化过程则采用逐层优化(LBLO)技术和递归最小平方(RLS)方法.该算法的性能通过将其应用于复信道均衡来加以说明.计算机仿真结果表明,该算法具有较快的收敛速度.这为快速训练复值递归神经网络提供了一条新的途径.     

Impact of decentralization on the performance of a Hopfield neuralnetwork-based DCA scheme

The impact of decentralizing a Hopfield neural network-based dynamic channel allocation (HNN-DCA) scheme is evaluated. In this scheme, the channel assignment is performed autonomously by every cell, based on information gathered from a set of neighboring cells rather than relying upon a central resource manager and availability of complete system information. Centralized schemes are technically undesirable or computationally prohibitive, or both, since large optimization areas impose increased signaling load and channel allocation time. Results show that a distributed HNN-DCA exhibits the advantages of distributed schemes-scalability and distributed signaling load and reduced allocation time, without significant loss in performance     

Shortest path routing algorithm using Hopfield neural network

A near-optimal routing algorithm employing a modified Hopfield neural network (HNN) is presented. Since it uses every piece of information that is available at the peripheral neurons, in addition to the highly correlated information that is available at the local neuron, faster convergence and better route optimality is achieved than with existing algorithms that employ the HNN. Furthermore, all the results are relatively independent of network topology for almost all source-destination pairs     

Computerized tumor boundary detection using a Hopfield neuralnetwork

The authors present a new approach for detection of brain tumor boundaries in medical images using a Hopfield neural network. The boundary detection problem is formulated as an optimization process that seeks the boundary points to minimize an energy functional based on an active contour model. A modified Hopfield network is constructed to solve the optimization problem. Taking advantage of the collective computational ability and energy convergence capability of the Hopfield network, the authors' method produces the results comparable to those of standard “snakes”-based algorithms, but it requires less computing time. With the parallel processing potential of the Hopfield network, the proposed boundary detection can be implemented for real time processing. Experiments on different magnetic resonance imaging (MRI) data sets show the effectiveness of the authors' approach     

广义Hopfield网络求解TSP

本文提出了用广义Ｈｏｐｆｉｅｌｄ网络求解ＴＳＰ的改进算法，较之用Ｈｏｐｆｉｅｌｄ网络求解ＴＳＰ的传统算法，新算法改进之处主要有两点，一、引入了辅助单元（本文称之为快单元）从而可以更加灵活构造能量函数。二、采用新的单元输入输出函数，并调整单元的自反馈和阈值，从而实现能量补偿，抵消能量误差，模拟结果表明，新算法优于传统的Ｈｏｐｆｉｅｌｄ网络算法。     

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。     

用神经网络求解Job－Shop类型调度问题

本文在Ｈｏｐｆｉｅｌｄ神经网络的基础上针对ＦＭＳ中Ｊｏｂ－ｓｈｏｐ类型调度问题，提出了线性Ｈｏｐｆｉｅｌｄ神经网络的表示方法，给出了网络的能量函数表示方法及神经元状态方程。理论上，证明了算法的收敛性及能量函数中系数与迭代步长的关系，软件模拟计算结果表明，所提出的方法是有效的且计算结果是满意的。