Application of VPRS model with enhanced threshold parameter selection mechanism to automatic stock market forecasting and portfolio selection

This study proposes a technique based upon Fuzzy C-Means (FCM) classification theory and related fuzzy theories for choosing an appropriate value of the Variable Precision Rough Set (VPRS) threshold parameter (β) when applied to the classification of continuous information systems. The VPRS model is then combined with a moving Average Autoregressive Exogenous (ARX) prediction model and Grey Systems theory to create an automatic stock market forecasting and portfolio selection mechanism. In the proposed mechanism, financial data are collected automatically every quarter and are input to an ARX prediction model to forecast the future trends of the collected data over the next quarter or half-year period. The forecast data are then reduced using a GM(1, N) model, classified using a FCM clustering algorithm, and then supplied to a VPRS classification module which selects appropriate investment stocks in accordance with a pre-determined set of decision-making rules. Finally, a grey relational analysis technique is employed to weight the selected stocks in such a way as to maximize the rate of return of the stock portfolio. The validity of the proposed approach is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio results obtained using the proposed hybrid model are compared with those obtained using a Rough Set (RS) selection model. The effects of the number of attributes of the RS lower approximation set and VPRS β-lower approximation set on the classification are systematically examined and compared. Overall, the results show that the proposed stock forecasting and stock selection mechanism not only yields a greater number of selected stocks in the β-lower approximation set than in the RS approximation set, but also yields a greater rate of return.     

Closed-loop identification using direct approach and high order ARX/GOBF-ARX models

Model accuracy plays a key role in the performance of advanced, model predictive control algorithms. Model fidelity is usually affected by routine operating condition changes, which necessitate reidentification. From several theoretical and practical considerations, it is recommended that such re-identification be performed under closed-loop conditions. The direct approach for closed-loop identification, owing to its simplicity, is better suited for MPC. In order to yield unbiased and consistent parameter estimates, however, this approach requires the noise model to be sufficiently parameterized. Towards this objective, high order ARX models are the most suitable candidates from the viewpoint of ease of parameter estimation. For multivariable systems, however, the identification of high order ARX models would require longer experiments to be performed. This being undesirable from a practical viewpoint, there is a need for a parsimonious parameterization that would retain the benefits of high order ARX models. In this work, we propose to use generalized orthonormal basis filters (GOBFs) to achieve this parsimonous parameterization. Further, we propose an approach to obtain reduced order models by emphasizing important frequencies so as to suitably shape the bias. We also show that the choice of the GOBF parameterization has another important merit, viz. their ability to perform well even with minimal perturbation data or short experiment times. The efficacy of the proposed approach is demonstrated via simulations on the benchmark Shell Control Problem and a laboratory quadruple tank setup.     

变焦遗传算法优化变增益模糊ARX模型研究

针对传统ARX模型对非线性系统模型辨识精度比较低的问题,进行了模糊模型和ARX模型相关优化算法的调查,介绍了遗传算法优化模糊模型的现状,提出采用改进变焦遗传算法优化变增益模糊ARX模型参数的方法以提高模型的辨识精度。改进的变焦遗传算法能在不同前代种群情况下更新不同数量基因,以提高搜索速度;用不同的概率选择交叉位置,可避免早熟现象,并能在较短时间内达到最优或次优解。变增益模糊ARX模型可根据非线性系统的变化改变其增益,使模型的辨识精度提高。利用改进变焦遗传算法的优点,对变增益模糊ARX模型的参数进行优化,并通过两入两出多时滞离散非线性系统进行试验仿真。试验结果证明了改进变焦遗传算法优化变增益模糊ARX模型参数的方法能提高模型辨识精度,表明了提出的优化方法的有效性,为变焦遗传算法与模糊模型的结合提供了一种途径。     

解决MFC与ARX在图形变换中冲突的方法

本文针对ＡｕｔｏＣＡＤ中常用图形变换方法与ＭＦＣ的冲突,提出利用ＡＲＸ的ＡｃＧｅ库的图形“先变换后显示”,不仅能解决上述冲突,还加快了绘图速度,增加了编程与显示的灵活性。     

ARX在岔管过渡段曲面CAD中的应用

ＡＲＸ是基于ＡｕｔｏＣＡＤ进行ＣＡＤ软件开发的新一代开发工具。本文讨论了利用ＡＲＸ在ＡｕｔｏＣＡＤ ＲＭ上进行岔管过渡段曲面ＣＡＤ开发的方法。     

用Visual J++实现ARX应用程序的动态自动加载

利用AutoCAD提供的COM接口,在Visual J 6.0环境下开发了一个Java应用程序,实现了AutoCAD的动态加载以及ARX应用程序和相应的菜单、工具条的动态自动加载.     

Closed-loop identification of systems with uncertain time delays using ARX-OBF structure

Closed-loop identification of systems with known time delays can be effectively carried out with simple model structures like Autoregressive with Exogenous Input (ARX) and Autoregressive Moving Average with Exogenous Input (ARMAX). However, when the system contains large uncertain time delay, such structures may lead to inaccurate models with significant bias if the time delay estimate used in the identification is less accurate. On the other hand, conventional orthonormal basis filter (OBF) model structures are very effective in capturing the dynamics of systems with uncertain time delays. However, they are not effective for closed-loop identification. In this paper, an ARX-OBF model structure which is obtained by modifying the ARX structure is shown to be effective in handling closed-loop identification of systems with uncertain time delays. In addition, the paper shows that this advantage of ARX-OBF models over simple ARX model is considerable in multi-step ahead predictions.     

用ARX进行变径岔管曲面CAD开发

讨论了利用ＡＲＸ在ＡｕｔｏＣＡＤＲ１４上进行变径二岔 管和三岔管曲面ＣＡＤ二次的方法,文中讨论了的方法可用于其它类似的基于ＡｕｔｏＣＡＤ的ＣＡＤ系统的二次开发。     

ARX在建筑装饰CAD中的应用

ＡＲＸ（Ａｕｔｏ ＣＡＤ Ｒｕｎｔｉｍｅ ｅＸｔｅｎｓｉｏｎ）是新一代的面向对象的ＡｕｔｏＣＡＤ开发环境。其支持面向对象技术的特性改变了传统的ＣＡＤ软件开发模式,以建筑装饰领域中的楼梯为例,剖析了ＡＲＸ的面向对象分析、设计和开发思想,并深入探讨了开发过程中其它一些关键技术问题,如对象名字管理策略、虚拟用户坐标系（ＵＣＳ）、图像用户界面（ＧＵＩ）、公用库等,展示了ＡＲＸ在建筑装饰ＣＡＤ领域中的广阔应