首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到10条相似文献,搜索用时 140 毫秒
1.
Fuzzy regression analysis using neural networks   总被引:4,自引:0,他引:4  
In this paper, we propose simple but powerful methods for fuzzy regression analysis using neural networks. Since neural networks have high capability as an approximator of nonlinear mappings, the proposed methods can be applied to more complex systems than the existing LP based methods. First we propose learning algorithms of neural networks for determining a nonlinear interval model from the given input-output patterns. A nonlinear interval model whose outputs approximately include all the given patterns can be determined by two neural networks. Next we show two methods for deriving nonlinear fuzzy models from the interval model determined by the proposed algorithms. Nonlinear fuzzy models whose h-level sets approximately include all the given patterns can be derived. Last we show an application of the proposed methods to a real problem.  相似文献   

2.
Complex nonlinear systems can be represented to a set of linear sub-models by using fuzzy sets and fuzzy reasoning via ordinary Takagi-Sugeno (TS) fuzzy models. In this paper, the exponential stability of TS fuzzy bidirectional associative memory (BAM) neural networks with impulsive effect and time-varying delays is investigated. The model of fuzzy impulsive BAM neural networks with time-varying delays established as a modified TS fuzzy model is new in which the consequent parts are composed of a set of impulsive BAM neural networks with time-varying delays. Further the exponential stability for fuzzy impulsive BAM neural networks is presented by utilizing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique without tuning any parameters. In addition, an example is provided to illustrate the applicability of the result using LMI control toolbox in MATLAB.  相似文献   

3.
This research deals with complementary neural networks (CMTNN) for the regression problem. Complementary neural networks consist of a pair of neural networks called truth neural network and falsity neural network, which are trained to predict truth and falsity outputs, respectively. In this paper, a novel adjusted averaging technique is proposed in order to enhance the result obtained from the basic CMTNN. We test our proposed technique based on the classical benchmark problems including housing, concrete compressive strength, and computer hardware data sets from the UCI machine learning repository. We also realize our technique to the porosity prediction problem based on well log data set obtained from practical field data in the oil and gas industry. We found that our proposed technique provides better performance when compared to the traditional CMTNN, backpropagation neural network, and support vector regression with linear, polynomial, and radial basis function kernels.  相似文献   

4.
陈娟  戴斌祥 《经济数学》2004,21(3):246-251
将径向基函数网络方法应用于工程工料消耗估算 ,讨论了网络结构的设计、学习算法等问题 ;建立了基于径向基函数网络的工程工料消耗估算模型 ,计算实例表明 ,借助该模型可实现工程工料消耗的快速估算 .  相似文献   

5.
IntroductionIn previous worksll,2], we discussed several issues associated with the standard version oflarge eddy simulation (LES) such as filtering and averaging. By the standard version, we meanthe traditional practice of first constructing a set of field equations of motions for turbulentmotion and then discretizing the equations to suit computational simulations.In this paper, we revisit the issue of large eddy simulation with a view towards improvingthe filtering procedure that is used i…  相似文献   

6.
In this paper, we study approximation by radial basis functions including Gaussian, multiquadric, and thin plate spline functions, and derive order of approximation under certain conditions. Moreover, neural networks are also constructed by wavelet recovery formula and wavelet frames.  相似文献   

7.
In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction–diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction–diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits.  相似文献   

8.
In this paper, we study exponential synchronization of delayed reaction-diffusion fuzzy cellular neural networks with general boundary conditions. By using Sobolev inequality techniques and constructing suitable Lyapunov functional, some sufficient conditions are given to ensure the exponential synchronization of the drive-response delayed fuzzy cellular neural networks with general boundary conditions. Finally, an example is given to verify the theoretical analysis.  相似文献   

9.
This paper explores, from a surface-fitting viewpoint, two algorithmswhich are often applied in the field intelligent control: fuzzyself-organizing controllers and neural networks. Both methodologiesadapt internal model parameters in response to the plant's input-outputmapping. However, while the convergence of single-layer neuralnetworks has been studied in great detail, very few theoremshave been proved about self-organizing fuzzy logic controllers.In this paper, it is shown that B-splines can provide a frameworkfor choosing the shape of the fuzzy sets. Then the operatorschosen to implement the underlying fuzzy logic are examined,showing how they can produce ‘smooth’ control surfaces.It is now possible to make a direct comparison between fuzzylogic controllers and feedforward neural networks, demonstratingthat, in a forward-chaining mode, storing the plant's behaviourin terms of weights or rule confidences is equivalent. Finally,three training rules for the self-organizing fuzzy controllerare derived.  相似文献   

10.
The fuzzy sets theory and the artificial neural networks are computational intelligence tools which are nowadays widely used in earthquake engineering. This paper develops a method and a computer program which use these computational intelligence tools in order to support the damage and safety evaluation of buildings after strong earthquakes. The model uses an artificial neural network with three layers and a Kohonen learning algorithm; it also uses fuzzy sets in order to manage subjective information such as linguistic qualification of the damage levels in buildings and a fuzzy rule base to support the decision making process. All these techniques are incorporated in the developed computer program. The input data is the subjective and incomplete information about the building state obtained by possibly non experienced evaluators in the field of the seismic performance of buildings. The proposed method is implemented in a tool especially useful in the emergency response phase, when it supports the decision making regarding the building habitability and reparability. In order to show its effectiveness, two examples are included for two different types of buildings.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号