A new neural network algorithm for planarization problems

To deal with the planarization problem widely used in many applications including routing very-large-scale integration （VLSI） circuits, this paper points out that only when its vertices are arranged in some specific order in a line can a planar graph be embedded on a line without any cross connections or cross edges. Energy function is proposed to meet the need of embedding a graph on a single line and route it correctly. A Hopfield network is designed according to the proposed energy function for such embedding and routing. The advantage of the proposed method is that it not only can detect if a graph is a planar one or not, but also can embed a planar graph or the maximal planar subgraph of a non-planar graph on a single line. In addition, simulated annealing is employed for helping the network to escape from local minima during the running of the Hopfield network. Experiments of the proposed method and its comparison with some existent conventional methods were performed and the results indicate that the proposed method is of great feasibility and effectiveness especially for the planarization problem of large graphs.     

Process parameters selection of fluid industry based on flexible neural trees

Fluid industry＇s production takes on high complexity, strong association, non-linear and indeterminacy. To find important parameters which influence production process, provide theoretical basis for process control of fluid industry＇s production and make the production process control more exact, we propose a flexible neural tree model in this paper. The model can be created based on predeflned information set. The structure and parameters of flexible neural tree are optimized by probabilistic incremental program evolution and simulation annealing respectively. We take production process of decomposed kiln---one of important cement＇s processes---as an example. The result indicates that the proposed model is greatly efficient.     

Parameter determination of support vector machine and feature selection using simulated annealing approach

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM.To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.     

A Review of the Application of Meta-Heuristic Algorithms to 2D Strip Packing Problems

This paper is a review of the approachesdeveloped to solve 2D packing problems withmeta-heuristic algorithms. As packing tasks arecombinatorial problems with very large searchspaces, the recent literature encourages theuse of meta-heuristic search methods, inparticular genetic algorithms. The objective ofthis paper is to present and categorise thesolution approaches in the literature for 2Dregular and irregular strip packing problems.The focus is hereby on the analysis of themethods involving genetic algorithms. Anoverview of the methods applying othermeta-heuristic algorithms including simulatedannealing, tabu search, and artificial neuralnetworks is also given.     

动态系统的常微分方程组建模—基于不同搜索技术的实验研究

以人口模型和化学反应模型为例,通过大量实验研究比较了分别采用基于两种传统的搜索方法即局部搜索算法和模拟退火算法、遗传算法（简称GA）四者相结合的14种不同算法建立动态系统的常微分方程组模型的实验结果,得到了有关各算法性能比较的一些新的结论。两个实例的实验结果表明：在14种算法中,GP＋GA＋LS－MU算法（即在采用GP的模型结构的优化过程中嵌入采用GA的模型参数的优化过程,并且在每一演化代对种群中的部分个体进行基于GP的标准变异算子产生邻域解的局域搜索过程）是目前解决常微分方程组建模问题的最好算法。     

可用于人脸识别的反馈型二元神经网络

提出和分析了一种新型的反馈型随机神经网络,并将其用于解决复杂的人脸识别问题.该模型采用随机型加权联接,神经元为简单的非线性处理单元.理论分析揭示该网络模型存在唯一的收敛平稳概率分布,当网络中神经元个数较多时,平稳概率分布逼近于Boltzmann-Gibbs分布,网络模型与马尔可夫随机场之间存在密切关系.在设计了一种新型模拟退火和渐进式Boltzmann学习算法后,系统被成功地应用于难度较大的静态和动态人像识别,实验结果证实了系统的可行性和高效率.     

Meta-heuristic algorithms for parameter estimation of semi-parametric linear regression models

Consider the semi-parametric linear regression model Y=β^′X+ε, where ε has an unknown distribution F₀. The semi-parametric MLE of β under this set-up is called the generalized semi-parametric MLE(GSMLE). Although the GSML estimation of the linear regression model is statistically appealing, it has never been attempted due to difficulties with obtaining the GSML estimates of β and F until recent work on linear regression for complete data and for right-censored data by Yu and Wong [2003a. Asymptotic properties of the generalized semi-parametric MLE in linear regression. Statistica Sinica 13, 311-326; 2003b. Semi-parametric MLE in simple linear regression analysis with interval-censored data. Commun. Statist.—Simulation Comput. 32, 147-164; 2003c. The semi-parametric MLE in linear regression with right censored data. J. Statist. Comput. Simul. 73, 833-848]. However, after obtaining all candidates, their algorithm simply does an exhaustive search to find the GSML estimators. In this paper, it is shown that Yu and Wong's algorithm leads to the so-called dimension disaster. Based on their idea, a simulated annealing algorithm for finding semi-parametric MLE is proposed along with techniques to reduce computations. Experimental results show that the new algorithm runs much faster for multiple linear regression models while keeping the nice features of Yu and Wong's original one.     

供应链集成化模型与优化

本文建立了一个包括供应商、制造商、销售商 在内的集成化模型,这一模型含有线性状态方程和神经网络非线性模型,提出了库存成本、 供应成本、生产水平等在内的供应链目标函数和约束条件．对于供应链集成化模型采用模拟 退火方法进行优化,并以辽化公司为背景进行了初步条件的仿真工作．     

SAFE: An Efficient Feature Extraction Technique

This paper proposes an efficient window-based semi-automatic feature extraction technique which uses simulated annealing for minimizing the energy of an active contour within a specified image region. The energy is computed based on a chamfer image, in which pixel values are a function of distance to image edges. A user places a number of control points close to the feature of interest. B-spline fitted to these points provides an initial approximation of the contour. A window containing both the initial contour and the feature of interest is considered. The contour with minimum energy inside the window provides the final delineation. Comparison of the performance of the proposed algorithm with traditional snake, a popular feature extraction technique based on energy minimization, demonstrates the superiority of the SAFE technique. Received 18 August 1999 / Revised 25 October 2000 / Accepted in revised form 8 December 2000     

Discrimination or favoritism? Sex bias in book reviews.

Studied 180 reviews of books written by male, female, or male and female authors to investigate sex bias. Half the books had been reviewed by males, half by females. Each review was rated separately for positive and negative elements. For positive elements, there was a significant interaction between author's and reviewer's sex: same-sex books were more favorably evaluated than other-sex books, while books of mixed authorship fell in between. There was no comparable interaction for negative elements. Reviewers of both sexes tended to find more negative elements in books by male than by female authors. It is concluded that unlike in studies employing hypothetical situations, in real-life settings each sex is biased in favor of itself. (PsycINFO Database Record (c) 2010 APA, all rights reserved)