基于Hopfield实现ATM网络路由选择的双重路由算法

研究神经网络用于ＡＴＭ网络路由调度问题,通过引入等效带宽的概念,以降低呼损率和提高网络利用率为优化目标,提出双重路由算法。根据不同的网络状态,采用不同基于Ｈｏｐｆｉｅｌｄ的路由方法。提出的算法不但逻辑控制及硬件实现较为简单,而且仿真结果表明,在各种呼叫分布及网络拓扑情况下,均有效降低呼员率和提高网络和利用率。很好优化 了系统性能。     

解决陷入局部极小的一种新方法

本文提出一种能有效。解决Ｈｏｐｆｉｅｌｄ网在能量变。过程之中陷入局部极小问题的方法，这种方法通过有选择性地改变权值矩阵Ｗ的对角元以及有某种特定顺序的串行工作方式来使得网络跳出局部极小值（点）向能量最小点逼近，模拟结果显示；这种方法不仅速度比较快（与模拟退火法ＳｉｍｕｌａｔｅｄＡｎｎｅａｌｉｎｇＭｅｔｈｏｄ比较），而且在每次实验中达到全局最小点的成功率也非常高．在所进行的众多的实验中，迄今为止，尚未出现不成功的情况．     

利用Hopfield网络求Hamilton圈的算法

本文通过给一个图和补图构成的完全图的各边赋权的方法，使一个图的Ｈａｍｉｌｔｏｎ圈问题归结为旅行商问题（ＴＳＰ），并给出判定Ｈａｍｉｌｔｏｎ图的准则．为了用Ｈｏｐｆｉｅｌｄ网络解ＴＳＰ时容易获得最优解，简化了方程组，提出了新的初始值设置等方法．实例计算的结果表明，本文的算法是有效的，能够迅速地求得Ｈａｍｉｌｔｏｎ圈．     

Liver stereoscopic visualisation by using Hopfield neural nets

Three dimensional visualisation techniques have been used as a powerful tool in surgical and therapeutic applications. Due to large medical data, huge computations are necessary on 3D visualisation, especially for a real-time system. Many existing methods are sequential, which are too slow to be practical in real applications. In our previous work, we showed boundary detection and feature points extraction by using Hopfield networks. In this paper, a new feature points matching method for 3D surfaces using a Hopfield neural network is proposed. Taking advantage of parallel and energy convergence capabilities in the Hopfield networks, this method is faster and more stable for feature points matching. Stereoscopic visualisation is the display result of our system. With stereoscopic visualisation, the 3D liver used in the experiment can leap out of the screen in true 3D stereoscopic depth. This increases a doctor's ability to analyse complex graphics.     

On the performance of the LP-guided Hopfield network-genetic algorithm

This paper proposes a linear programming (LP)-guided Hopfield-genetic algorithm for a class of combinatorial optimization problems which admit a 0–1 integer linear programming. The algorithm modifies the updating order of the binary Hopfield network in order to obtain better performance of the complete hybrid approach. We theoretically analyze several different updating orders proposed. We also include in the paper a novel proposal to guide the Hopfield network using the crossover and mutation operators of the genetic algorithm. Experimental evidences that show the good performance of the proposed approach in two different combinatorial optimization problems are also included in the paper.     

基于CHNN聚类算法的款式部件生成模型

对智能化服装款式设计系统中的款式部件的自动获取功能进行了研究.采用基于连续Hopfield神经网络(CHNN)的聚类算法提出了一个款式部件的风格生成模型.提取表现部件造型特征的特征要素构造一个空间点集,利用CHNN网络对该点集进行聚类,分析部件类别与款式设计风格之间的关系,建立基于款式风格设计的部件搭配规则.并将该模型应用于款式的衣片部件上,实现了衣片部件的聚类.实验结果表明,该模型设计合理,分类清晰,具有可扩展性.     

On Non-Uniform Rational B-Splines Surface Neural Networks

This article presents a simulation study for validation of an adaptation methodology for learning weights of a Hopfield neural network configured as a static optimizer. The quadratic Liapunov function associated with the Hopfield network dynamics is leveraged to map the set of constraints associated with a static optimization problem. This approach leads to a set of constraint-specific penalty or weighting coefficients whose values need to be defined. The methodology leverages a learning-based approach to define values of constraint weighting coefficients through adaptation. These values are in turn used to compute values of network weights, effectively eliminating the guesswork in defining weight values for a given static optimization problem, which has been a long-standing challenge in artificial neural networks. The simulation study is performed using the Traveling Salesman problem from the domain of combinatorial optimization. Simulation results indicate that the adaptation procedure is able to guide the Hopfield network towards solutions of the problem starting with random values for weights and constraint weighting coefficients. At the conclusion of the adaptation phase, the Hopfield network acquires weight values which readily position the network to search for local minimum solutions. The demonstrated successful application of the adaptation procedure eliminates the need to guess or predetermine the values for weights of the Hopfield network.     

Complexity of reachability problems for finite discrete dynamical systems

Sequential Dynamical Systems (SDSs) are a special type of finite discrete dynamical systems that can be used to model simulation systems. We focus on the computational complexity of testing several phase space properties of SDSs. Our main result is a sharp delineation between classes of SDSs whose behavior is easy to predict and those whose behavior is hard to predict. Specifically, we show the following.

1.

Several state reachability problems for SDSs are PSPACE-complete, even when restricted to SDSs whose underlying graphs are of bounded bandwidth (and hence of bounded pathwidth and treewidth), and the function associated with each node is symmetric. Moreover, this result holds even when the underlying graph is d-regular for some constant d and all the nodes compute the same symmetric Boolean function. An immediate corollary of this result is a PSPACE-hard lower bound on the complexity of reachability problems for regular generalized 1D-Cellular Automata and undirected systolic networks with Boolean totalistic local transition functions.

2.

In contrast, the above reachability problems are solvable in polynomial time for SDSs when the Boolean function associated with each node is symmetric and monotone.

The PSPACE-completeness results follow as corollaries of simulation results which show for several classes of SDSs, how one class of SDSs can be efficiently simulated by another (more restricted) class of SDSs. We also prove several structural properties concerning the phase space of an SDS. SDSs are closely related to Cellular Automata (CA), concurrent transition systems, discrete Hopfield networks and systolic networks. This observation in conjunction with our lower bounds for SDSs, yields new PSPACE-hard lower bounds on the complexity of state reachability problems for these models, extending some of the earlier results in [K. Culik II, J. Karhumäki, On totalistic systolic networks, Inform. Process. Lett. 26 (5) (1988) 231-236; P. Floréen, E. Goles, G. Weisbuch, Transient length in sequential iterations of threshold functions, Discrete Appl. Math. 6 (1983) 95-98; P. Floréen, P. Orponen, Complexity issues in discrete Hopfield networks, Research Report No. A-1994-4, Department of Computer Science, University of Helsinki, 1994. Also appears in: I. Parberry (Ed.), Comp. and Learning Complexity of Neural Networks: Advanced Topics, 1999; D. Harel, O. Kupferman, M.Y. Vardi, On the complexity of verifying concurrent transition systems, Inform. and Comput. 173 (2002) 143-161; S.K. Shukla, H.B. Hunt III, D.J. Rosenkrantz, R.E. Stearns, On the complexity of relational problems for finite state processes, in: International Colloquium on Automata Programming and Languages, ICALP, 1996, pp. 466-477; A. Rabinovich, Complexity of equivalence problems for concurrent systems of finite agents, Inform. and Comput. 127 (2) (1997) 164-185].     

用改进的竞争Hopfieid神经网络求解多边形近似问题

多边形近似是提取曲线特征点和简化曲线描述的一种重要方法。提出一种改进的Hopfield神经网络多边形近似算法,该算法利用选择拐点策略减少了搜索空间,重新定义了神经网络的能量函数,使其更能反映优化目标;引入合并拆分搜索策略,有效帮助神经网络脱离局部最小值。实验结果表明,提出的改进算法是有效的,比其它算法如关键点检测法、竞争Hopfield神经网络、混沌Hopfield神经网络、遗传算法等具有更优的性能。