基于遗传算法的模糊控制器参数优化

针对非线性量化因子模糊控制器的参数对系统性能影响和参数间的相互制约,提出了一种基于遗传算法的参数整定与优化方法,并进行了仿真研究。仿真结果表明通过该方法寻优的系统具有更好的响应速度和控制精度；当对象结构或参数发生变化时,非线性量化因子模糊控制器可以重新整定参数,以保持良好的控制效果,具有很强的鲁棒性。     

Performance of Garch Models in Forecasting the Volatility of Exchange Rate for Developing Countries

This paper empirically investigates the performance of GARCH model in forecasting the volatility of exchange rate of some developing countries. We apply linear GARCH model and non-linear GARCH model. We fit these two models to some developing countries exchange rate index from January, 1998 to February, 2005. The return series of the developing countries＇ foreign exchange rate are leptokurtic, significantly skew, deviating from normality and volatile clustering as well. We find within-sample and out-of-sample evidence that conditional estimates of non-linear GARCH model outperform the conditional estimations of linear GARCH models. In our comparisons in most of the developing countries, the non-linear GARCH model produce better results than the linear GARCH model tor forecasting the volatility of exchange rate.     

Using Genetic Algorithms to Estimate and Validate Bioeconomic Models: The Case of the Ibero-atlantic Sardine Fishery

Synopsis The Neo-classical approach to fisheries management is based on designing and applying bioeconomic models. Traditionally, the basic bioeconomic models have used pre-established non-linear functional forms (logistic, Cobb–Douglas) in order to try to reflect the dynamics of the renewable resources under study. This assumption might cause misspecification problems and, in consequence, a loss of predictive ability. In this work we intend to verify if there is a bias motivated by employing the said non-linear parametric perspective. For this purpose, we employ a novel non-linear and non-parametric prediction method, called Genetic Algorithms, and we compare its results with those obtained from the traditional methods.     

基于非线性科学的管理信息系统构建

管理信息系统已深入到各行各业，但开发高质量的MIS远落后于计算机发展，由于系统设计缺陷及满足需求增长的不适应，给用户带来不便与损失，并缩短了系统使用寿命。为此，提出了基于非线性科学的管理信息系统构建思路：对系统信息进行统计自相似性分析，能对未来信息进行动态预测；对功能部分与整体进行自相似性分析，从而部分能有效地过渡到整体。     

A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices

The popular sentiment-based investor index S^BW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, Huang et al. (2015) developed a new investor sentiment index, S^PLS, which can predict monthly stock returns based on a linear framework. However, the linear model may lead to misspecification and lack of robustness. We provide statistical evidence that the relationship between stock returns, S^BW and S^PLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither S^BW nor S^PLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.     

中国农村经济发展积累过程形态分析

了解中国农村经济发展的经济结构对认识中国农村经济有着特殊的意义。本文旨在通过全国30个省、自治区和直辖市经济发展的比较研究,运用值域分析、方差分析和回归分析方法、探索出全国经济积累过程的发展形态,总结出地区伴随着经济发展过程而出现的特有经济结构现象,寻找问题症结的所在和解决问题的突破点,为国家经济提供建设性参考建议。     

促进节能减排的非线性四轮驱动分析——以浙江省为例

文章通过对浙江省能源资源、经济社会发展等方面统计数据的系统分析,提出了依靠能源结构、产业结构的双重优化、调整,节能减排技术、管理水平的运用、提升,以提高全社会的能源利用效率、减少污染物排放,进而实现浙江经济-能源-环境协调发展的思路,从而促使“线性的、数量驱动”的经济社会节能减排向“非线性的、全方位质量驱动”的方向发展。     

粒子滤波算法(PF)在疲劳驾驶检测系统中的应用研究

目前,实现定位跟踪的算法有很多,如卡尔曼滤波算法、扩展卡尔曼滤波算法、粒子滤波算法等。由于我们实际生活中的系统基本上都是非线性的,因此本文研究的是专门用于非线性非高斯系统跟踪的粒子滤波算法(PF)的基本原理及其具体应用。