具有输入时滞的不确定时滞系统的积分控制

针对具有输入时滞的不确定时滞系统,利用Lyapunov稳定性定理和一个特殊的引理,设计了带记忆的积分控制器,使得系统在有限时间内渐近稳定,并给出时滞依赖的稳定性判据.数值算例表明了该设计方法的有效性.     

一类Lur' e时滞系统新的时滞依赖绝对稳定判据

考虑了一类Lur'e时变时滞系统的绝对稳定问题,通过构造适当的Lyapunov函数和引入一些松弛矩阵,以LMI形式得到了新的时滞依赖绝对稳定判据.最后,给出了2个数值例子,结果验证了方法的有效性和较弱的保守性.     

具有多胞型摄动的时滞系统鲁棒稳定性

通过定义一个参数依赖的Lyapunov泛函,结合利用逆凸组合技术和一个矩阵不等式松散方法,得到了保守性小且计算复杂度低的鲁棒稳定性判据.算例表明新的判据显著地改进了已有的结果.     

具有一般死亡率的SIRS模型研究

研究了一类具有一般死亡率的SIRS模型,得到了疾病绝灭的阈值R0.用Hurwitz判据证明了平衡点的局部稳定性,用广义Bendixson-Dulac定理排除了周期解的存在性.从而得到当R0≤1时无病平衡点全局渐近稳定;当R0＞1时无病平衡点不稳定,地方病平衡点是全局渐近稳定.     

一类时滞和阶段结构食物链系统的局部分析

对一类3种群的捕食被捕食模型,运用上下解方法、正性引理和线性化方法,研究了具有时滞和阶段结构弱耦合半线性抛物方程组的存在性和渐近性态.获得了解的整体存在惟一性,并给出了非平凡平衡解局部渐近稳定性易验证的充分条件.     

一类非线性时滞系统的鲁棒自适应控制

考虑了一类含有时滞非线性系统鲁棒自适应控制问题.运用backstepping方法,巧妙构造了状态反馈控制器和自适应控制律.通过选取适当的Lyapunov函数,解决了系统中的时滞项.基于Lyapunov稳定性理论,所设计的控制器实现了闭环系统的渐近稳定.最后,给出了数值例子,并对系统进行了仿真,结果验证了方法的有效性.     

具有功能反应的三维捕食系统的周期解

研究了一类具有Holling Ⅲ类功能反应且周期系统的三维非自治捕食系统,利用微分不等式给出了系统永久持续生存的充分条件;同时,通过构造Lyapunov函数,建立了系统正周期解存在惟一及全局渐近稳定的充分判据.     

时滞泛函微分方程的一个不稳定性定理

给出了时滞泛函微分方程的零解不稳定性的新判据，取消了李雅普诺夫泛函y的传统限制：dV／dt≥0，推广了J．K.Hale的结果。     

具有时滞的植物传染病模型的稳定性和Hopf分支

研究一类依赖昆虫媒介传播的具有时滞的植物传染病模型.若基本再生数R0≤1,模型仅存在唯一的无病平衡点,利用Routh-Hurwitz判据讨论无病平衡点的局部渐近稳定性,构造Liapunov泛函方法讨论无病平衡点的全局渐近稳定性.若基本再生数R01,无病平衡点不稳定,模型还存在唯一的正平衡点,进一步讨论正平衡点处的局部稳定性和Hopf分支的存在性,并用中心流形定理和正规型理论确定了分支周期解的稳定性和分支方向.最后通过数值模拟验证了理论分析的正确性.     

时滞扩散和功能性反应捕食系统的全局稳定性

研究了一类具有时滞扩散和HollingⅢ类功能反应的非自治捕食系统.利用微分方程定性与稳定性理论,得到了系统一致持久的充分条件.通过构建Liapunov函数,运用Barbalart引理,得到对应周期系统存在惟一全局渐近稳定正周期解的充分条件.     

分布时滞的脉冲神经网络的稳定性分析

讨论了具有无穷分布时滞的脉冲神经网络的全局指数稳定性.利用Lyapunov-Krasovskii泛函和线性矩阵不等式,得到了神经网络的全局指数稳定的充分条件.同时,推广了已有文献的结果,降低了系统的保守性.通过实例说明了所得结论的实用性.     

Hopfield连续神经网络局部稳定性分析

利用微分几何的方法,将非线性Hopfield神经网络在平衡点附近精确线性化,使之变换成线性系统。通过分析神经网络的n阶李导数,给出并证明了Hopfield神经网络的局部稳定性定理。定理表明,当神经网络满足精确线性化条件时,系统的可控矩阵的非奇异性充分决定了神经网络吸引子的局部稳定范围。     

具有阶段结构与随机扰动的酗酒模型

研究一类具有阶段结构与随机扰动的酗酒模型,分析饮酒平衡点附近的随机扰动.通过建立Lyapunov函数及应用伊藤公式,证明饮酒平衡点附近的随机全局渐近稳定性.当确定性模型基本再生数R_01,随机模型的解是平均持续的,说明饮酒行为持续存在.另外,饮酒的传播率,自然死亡率及复发率对饮酒平衡点附近的随机全局渐近稳定性起着决定性作用.     

Intelligent Estimation of the Canola Oil Stability Using Artificial Neural Networks

In the present study, a multi-layer perceptron neural network and radial basis function (RBF) network were used to estimate the oxidative stability of canola oil during storage. Artificial neural networks (ANNs) were used to model oxidative stability of canola oil during storage, and comparison was also made with the results obtained from a regression analysis. The oxidative stability of canola oils was considered as dependent variable, and independent variables were selected as time (in week), variety, C14:0, C16:0, C18:0, C20:0, C18:1, C18:2, C18:3, and C22:1 fatty acid content. The results were compared with experimental data and it was found that the estimated oxidative stability by RBF neural network is more accurate than multi-layer perceptron network and regression model. It was also found that the oxidative stability of canola oil decreased with increase in storage time and C18:3 fatty acid content.     

一类具有群体感应机理的传染病模型渐近分析

考虑病菌的群体感应机理,建立了一类具有一般作用率反应病菌与免疫系统竞争的传染病数学模型. 分析了模型平衡点的存在性及渐近稳定性. 特别地,利用中心流形定理研究退化平衡点的稳定性. 最后,数值模拟验证所得理论结果,为传染病的控制和预防提供了理论基础和数值依据.     

前向神经网络:一个新的非参数回归方法

从统计建模的观点,前向神经网络可以看作是一个新的非参数回归方法.通过模拟例子和实际例子对前向神经网络和局部多项式光滑方法的有限样本行为进行了对比,结果表明前向神经网络稍微优于局部多项式光滑方法.此外,对前向神经网络的优点和存在的问题进行了深入讨论.     

A new approach for prediction of sewing performance of fabrics in apparel manufacturing using artificial neural networks

Abstract

This paper investigates the use of extended normalized radial basis function (ENRBF) neural networks to predict the sewing performance of fabrics in apparel manufacturing. In order to evaluate the performance of the ENRBF neural networks that could be emulated as human decision in the prediction of sewing performance of fabrics more effectively, it could be compared with the traditional back-propagation (BP) neural networks in terms of prediction errors. There are 109 data sets cover fabric properties measured by using a computerized measuring system, and the sewing performance of each fabric's specimen assessed by the domain experts. Of these 109 input—output data pairs, 94 were used to train the proposed ENRBF and BP neural networks for the prediction of the unknown sewing performance of a given fabric, and 15 were used to test the proposed ENRBF and BP neural networks, respectively. After 10,000 iterations of training of the ENRBF and BP neural networks, both of them converged to the minimum error level. A comparison was made between actual fabric performances during sewing, the experts' advices, and the results of predicting fabric performances during sewing for both networks. It was found that the ENRBF and BP neural networks indicate similar error levels, but the prediction made by the ENRBF neural network is better than the prediction made by the BP neural network in some areas. Both the systems provided better advice than the experts in some areas, when compared to actual sewing performance.     

一类多时滞病毒动力系统稳定性分析

在基本病毒动力学系统的基础上,建立了一个具有3个时滞的动力系统.通过稳定性分析,讨论了无病平衡点以及正平衡点的局部稳定性态,并得到不同时滞对平衡点局部稳定性的影响.其结果推广了基本动力学系统,且可以用来解释免疫状态的复杂性.     

Predicting Protein Functionality with Artificial Neural Networks: Foaming and Emulsifying Properties

Using physicochemical properties of 11 food-related proteins, artificial neural networks (ANN) were developed for predicting foam capacity and stability and the emulsion activity index. The prediction accuracy of ANN was compared to that of principal component regression (PCR) models. ANN had better prediction ability than PCR, especially after cross-validation. For foam stability, PCR did not generate a significant model. ANN and PCR models indicated that fluorescence probe hydrophobicity (measured using cispsrinaric acid) and other properties, such as viscosity, surface tension and net charge were important in predicting foam capacity and stability.     

Performance of Error Back Propagation vis-á-vis Radial Basis Function Neural Network: Part I: Prediction of Properties for Design Engineering of Woven Suiting Fabrics

Abstract

In this paper, an artificial neural network approach is used to predict the low-stress mechanical, dimensional, and tensile properties of woven suiting fabrics. Radial basis function neural network prediction is studied along with error back propagation based neural networks. Radial basis function neural networks are found to have better predictability and are faster to train and easier to design than back propagation neural networks. The prediction of fabric properties by the neural networks due to changes in fabric constructional parameters is in good agreement with generally accepted trends for fibre, yarn and fabric structure property relationships.