C++程序设计教学中的技能培养

本文阐述了知识与技能的关系,以及C++程序设计教学中知识和技能的双重性要求。以选择语句为例,演示了"先知识后技能"的教学过程。实践证明,这样的教学安排行之有效。     

膨胀弯管在管道柔性设计中的定性分析

为防止管道因热胀冷缩、管道支撑或端点附加位移造成的应力问题,对管线中设置的膨胀弯管进行定性分析.通过2个参数的变化对膨胀弯管进行编号得到多种设计方案,用ANSYS建立管道的有限元模型,并分析各方案中膨胀管道的变形、应力和弯矩等;根据分析结果对膨胀弯管设计半径、设置位置和形状进行定性分析.该定性分析结果可为膨胀弯管概念设计和管道柔性设计提供参考.     

Incorporating statistical information into expert classification systems to reduce classification costs

Interactive expert systems seek relevant information from a user in order to answer a query or to solve a problem that the user has posed. A fundamental design issue for such a system is therefore itsinformation-seeking strategy, which determines the order in which it asks questions or performs experiments to gain the information that it needs to respond to the user. This paper examines the problem of optimal knowledge acquisition through questioning in contexts where it is expensive or time-consuming to obtain the answers to questions. An abstract model of an expert classification system — considered as a set of logical classification rules supplemented by some statistical knowledge about attribute frequencies — is developed and applied to analyze the complexity and to present constructive algorithms for doing probabilistic question-based classification. New heuristics are presented that generalize previous results for optimal identification keys and questionnaires. For an important class of discrete discriminant analysis problems, these heuristics find optimal or near-optimal questioning strategies in a small fraction of the time required by an exact solution algorithm.     

图形控制器在中小型控制系统中的应用

从选型、设计、调试3个方面比较了中小型控制系统中采用的触摸屏加PLC方案和图形控制器方案的优缺点;以某工厂供粉控制系统为例,给出了一种采用图形控制器来代替触摸屏加PLC的控制方案。实际应用说明了该控制方案的可行性及优势。     

短波chirp探测的遗传退火频率分配算法研究

针对短波chirp探测系统的通信网络频率分配问题,建立了合理的干扰模型;利用系统所提供的测量参数,结合基本遗传算法,同时进行模拟退火操作以克服基本遗传算法易陷入局部最优解的不足,并最终趋于全局最优化;仿真结果表明,该算法在寻优性、收敛速度和稳定性等方面都要优于基本遗传算法。     

Extremal optimization vs. learning automata: Strategies for spin selection in portfolio selection problems

Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

Semantic analysis in XTRA,an English — Chinese machine translation system

This paper concerns the resolution of lexical ambiguity in a machine translation environment. We describe the integration of principles of selection restrictions. Preference Semantics, and intelligent relaxation of constraints in handling lexical ambiguity. The approach differs from many previous MT systems in that it is more powerful than brute force systems, while more realistic than systems that assume a large degree of coded encyclopedia information for full understanding.     

一种面向SOA业务恢复的服务选择算法

针对SOA业务恢复的服务选择问题,本文提出了一种QoS近似全局最优的服务选择算法GALSA:首先利用GA算法将全局QoS约束分解为最优的局部QoS约束,然后利用LSA算法选取出满足局部QoS约束的最优服务。算法不仅能作用于服务重构的服务选择过程中,而且在单个服务节点失效时亦能选取出满足全局QoS约束的服务进行替换。理论分析与实验结果说明了算法的可行性和有效性。     

An improved algorithm for finding the median distributively

Given two processes, each having a total-ordered set ofn elements, we present a distributed algorithm for finding median of these 2n elements using no more than logn +O(logn) messages, but if the elements are distinct, only logn +O(1) messages will be required. The communication complexity of our algorithm is better than the previously known result which takes 2 logn messages.