交通流灰色RBF网络非线性组合预测方法

针对智能交通系统的开发,提出一种基于灰色GM(1,1)模型和RBF网络非线性组合的短时交通流预测方法.该方法采用三层结构的RBF网络将两种单一预测方法(灰色GM(1,1)模型和RBF网络)进行了非线性组合.利用实测数据对组合方法进行了仿真实验,结果表明:非线性组合模型的预测准确性高于单独的RBF网络预测的准确性;组合模型发挥了两种单一方法各自的优势,是短时交通流预测的有效方法.     

Switching Control of Nonlinear Systems Based on the Quasi-ARX Model and the SVR Algorithm北大核心CSCD

该文基于改进的含有外部输入项的准线性自回归(准ARX)径向基函数(RBF)网络模型和支持向量回归(SVR)算法,提出了一种非线性切换控制方法.改进的准ARX模型非线性部分采用RBF网络.控制系统设计过程分为三个部分:首先,利用聚类方法确定模型的非线性参数;然后,采用线性SVR算法来解决控制系统的鲁棒性问题;接下来,基于控制误差给出切换判定函数,确定切换律给出控制序列.最后通过数值仿真验证了该方法的有效性.     

钢管的订购和运输解答模型

首先通过最短路算法简化了供需距离网络 ,去掉了铁路、公路等边的性质 ,使供需距离网络简化为一个供需运输价格表 .在此基础上构造了三个模型 :线性费用的网络流模型、改进的线性费用的网络流模型和具有非线性费用的网络流模型 .通过改进传统的最小费用最大流算法 ,解决了本题的非线性费用网络流模型 ,并给出了算法的正确性证明与复杂度分析     

多商品流电子商务供应链网络模型

针对多商品流三层供应链网络模型,将电子商务整合到多商品流供应链网络中,导出了每层网络代理商或决策者的最优性条件,给出了问题的变分不等式形式,得到了系统达到均衡的条件,给出了具体算例并进行了求解.     

多目标供应链网络平衡模型

针对生产商、零售商和消费群三层决策者问题建立了一个多目标供应链网络均衡模型.给出此模型的均衡条件,并讨论了(弱)均衡解和标量化解之间的关系.最后,给出具体的例子说明此模型.     

非线性二维主成分分析方法

提出一种矩阵数据的非线性压缩方法 非线性二维主成分分析方法.该方法在二维主成分分析的基础上,通过引入激活函数对投影后数据进行变换,从而使算法压缩性能得以提升;同时,该方法可以从网络模型角度获得直观解释,它通过在特定位置引入形变子层以改变压缩方向,最终实现对矩阵数据两个维度的同时非线性压缩;最后,设计了该模型的"形变反向...     

非线性互补函数及其在不确定规划中的应用

本文在非线性互补问题中引入不确定变量,提出了不确定非线性互补问题.给出了三种有限制的不确定非线性互补函数,证明了它们之间的等价性,并将三种有限制的非线性互补函数转化为各自的期望残差最小化模型,对模型水平集的有界性和误差界进行了研究.提供了非线性互补函数在不确定规划中的应用.最后,给出了数值算例,验证了本文方法的可行性.     

非线性时间序列的投影寻踪学习网络逼近

本文研究非线性自回归模型投影寻踪学习网络逼近的收敛性,证明了在L^k(k为正整数）空间上,投影寻踪学习网络可以以任意精度逼近非线性自回归模型,给出基于投影寻踪学习网络的非线性时间序列模型建模和预报的计算方法和应用实例,对太阳黑子数据,山猫数据及西安数据进行了拟合和预报,将其结果与改进BP网和门限自回归模型相应的结果进行比较,结果表明基于投影寻踪学习网络的非线性时间序列的建模预报方法是一类行之有效的方法。     

政府奖励与惩罚机制下闭环供应链网络均衡的互补模型

为研究政府经济政策对闭环供应链的影响,以非线性互补理论为基本工具,分别得到了在政府奖励机制与惩罚机制下,制造商负责回收的闭环供应链网络的各层均衡及整体均衡条件、经济解释及对应的非线性互补模型.最后通过数值算例验证了模型的正确性与有效性,其分析结果表明当政府预期的最低回收率较低时,惩罚机制优于奖励机制;当政府预期的最低回收率较高时,奖励机制优于惩罚机制.政府部门为了达到预期的最低回收率目标,可以适当调整奖励因子与惩罚因子.     

Richards模型参数估计及其模型应用

在非线性模型中,Richards模型是一个含有四参数的增长曲线模型,该模型对数据的拟合有较强的适应性,应用较为广泛.但其参数的估计较为复杂,给出简便易行的三种方法,实例应用表明拟合效果很好.     

Online affine model identification of nonlinear processes using a new adaptive neuro-fuzzy approach

This paper presents a new online identification algorithm to drive an adaptive affine dynamic model for nonlinear and time-varying processes. The new algorithm is devised on the basis of an adaptive neuro-fuzzy modeling approach. Two adaptive neuro-fuzzy models are sequentially identified on the basis of the most recent input-output process data to realize an online affine-type model. A series of simulation test studies has been conducted to demonstrate the efficient capabilities of the proposed algorithm to automatically identify an online affine-type model for two highly nonlinear and time-varying continuous stirred tank reactor (CSTR) benchmark problems having inherent non-affine dynamic model representations. Adequacy assessments of the identified models have been explored using different evaluation measures, including comparison with an adaptive neuro-fuzzy inference system (ANFIS) as the pioneering and the most popular adaptive neuro-fuzzy system with powerful modeling features.     

Combined separable signals based neuro-fuzzy Hammerstein–Wiener model

Hammerstein–Wiener model can describe a large number of complicated industrial processes. In this paper, a novel identification method for neuro-fuzzy based Hammerstein–Wiener model is presented. A neuro-fuzzy system with correlation analysis based non-iterative parameter updating algorithm is proposed to model the static nonlinearity of Hammerstein–Winer processes. As a result, the proposed method not only avoid the inevitable restrictions on static nonlinear function encountered by using the polynomial approach, but also overcomes the problems of initialization and convergence of the model parameters, which are usually resorted to trial and error procedure in the existing iterative algorithms used for the identification of Hammerstein–Winer model. In addition, combined separable signals are adopted to identify the Hammerstein–Wiener process, resulting in the identification problem of the linear model separated from that of nonlinear parts. Moreover, one part of the input signals is extended to more general signals, such as binary signals, Gaussian signals or other modulated signals. Examples are used to illustrate the effectiveness of the proposed method.     

Parameter identification of TSK neuro-fuzzy models

In the framework of the TSK neuro-fuzzy model a combination of the two well-known identification methods are employed for parameter estimation of the neuro-fuzzy inference system, namely the series–parallel and the parallel configurations. The presented paper proposes two new possible configurations for identifying the parameters of the TSK neuro-fuzzy model using the combinations of these two existing configurations. One of the proposed configurations constitutes the series–parallel configuration to the premise part and the parallel configuration to the consequent part of the neuro-fuzzy model, termed as PS-P configuration. The second one is composed of the series–parallel configuration to the consequent part and the parallel configuration to the premise part of the neuro-fuzzy model, termed as CS-P configuration. The presented work mainly deals with a comparative study of the proposed configurations and the existing configurations in the context of parameter identification of the TSK neuro-fuzzy model on three different benchmark examples. Moreover, it investigates upper bound of the learning rates, using the Lyapunov stability theorem, to assure the stability and the convergence of the model learning process. Implementation of the modified mountain clustering (MMC) and the cluster validity function yields initial models. To restrict the upper bound during the learning process it also presents a two-phase adaptive learning rate.     

Identification of nonlinear system using extreme learning machine based Hammerstein model

In this paper, a new method for nonlinear system identification via extreme learning machine neural network based Hammerstein model (ELM-Hammerstein) is proposed. The ELM-Hammerstein model consists of static ELM neural network followed by a linear dynamic subsystem. The identification of nonlinear system is achieved by determining the structure of ELM-Hammerstein model and estimating its parameters. Lipschitz quotient criterion is adopted to determine the structure of ELM-Hammerstein model from input–output data. A generalized ELM algorithm is proposed to estimate the parameters of ELM-Hammerstein model, where the parameters of linear dynamic part and the output weights of ELM neural network are estimated simultaneously. The proposed method can obtain more accurate identification results with less computation complexity. Three simulation examples demonstrate its effectiveness.     

一种改进的基于指数平滑神经网络模型的时间序列预测方法

基于指数平滑模型与误差反传神经网络法提出了一个改进的时间序列预测方法.将神经网络模型移植入指数加权滑动平均模型中,充分考虑了时间序列的部分线性性和非线性性对预测结果的影响,是传统的混合模型的一个更合理的改进.最后通过对上证指数时间序列的实证分析,以预测均方误差为检验标准,对五种常用的时间序列预测模型进行了预测精度的比较,而且经验证所提出的改进的时间序列预测模型相对来说具有更小的预测均方误差.     

A locally linear neuro-fuzzy model for supplier selection in cosmetics industry

Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques.     

Forecasting business cycle with chaotic time series based on neural network with weighted fuzzy membership functions

This study presents a forecasting model of cyclical fluctuations of the economy based on the time delay coordinate embedding method. The model uses a neuro-fuzzy network called neural network with weighted fuzzy membership functions (NEWFM). The preprocessed time series of the leading composite index using the time delay coordinate embedding method are used as input data to the NEWFM to forecast the business cycle. A comparative study is conducted using other methods based on wavelet transform and Principal Component Analysis for the performance comparison. The forecasting results are tested using a linear regression analysis to compare the approximation of the input data against the target class, gross domestic product (GDP). The chaos based model captures nonlinear dynamics and interactions within the system, which other two models ignore. The test results demonstrated that chaos based method significantly improved the prediction capability, thereby demonstrating superior performance to the other methods.     

A mathematical model for the effect of malicious object on computer network immune system

In this paper, a nonlinear mathematical model is proposed and analyzed to study the effect of malicious object on the immune response of the computer network. Criteria for local stability, instability and global stability are obtained. It is shown that the immune response of the system decreases as the concentration of malicious objects increases, and certain criteria’s are obtained under which it settles down at its equilibrium level. This paper shows that the malicious objects have a grave effect on cyber defense mechanism. The paper has two parts – (i) in the first part a mathematical model is proposed in which dynamics of pathogen, immune response and relative characteristic of the damaged node in the network is investigated, (ii) in second part the effect of malicious object on the immune response of the network has been examined. Finally how and where to use this modeling is discussed.     

Neuro-fuzzy technique for navigation of multiple mobile robots

In this paper, navigation techniques for several mobile robots are investigated in a totally unknown environment. In the beginning, Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of sensors for measuring the distances of obstacles around it and an image sensor for detecting the bearing of the target. It is found that the FLC having Gaussian membership function is best suitable for navigation of multiple mobile robots. Then a hybrid neuro-fuzzy technique has been designed for the same problem. The neuro-fuzzy technique being used here comprises a neural network, which is acting as a pre processor for a fuzzy controller. The neural network considered for neuro-fuzzy technique is a multi-layer perceptron, with two hidden layers. These techniques have been demonstrated in simulation mode, which depicts that the robots are able to avoid obstacles and reach the targets efficiently. Amongst the techniques developed neuro-fuzzy technique is found to be most efficient for mobile robots navigation. Experimental verifications have been done with the simulation results to prove the authenticity of the developed neuro-fuzzy technique.