共查询到19条相似文献,搜索用时 218 毫秒
1.
基于模糊分类的模糊神经网络辨识方法及应用 总被引:2,自引:6,他引:2
基于改进的T-S模型,提出一种自适应模糊神经网络模型(AFNN),给出了网络的连接结构和学习算法。基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数,并得出每个样本对每条规则的适用程度。利用卡尔曼滤波算法在线辨识删的后件参数。AFNN结构简洁,逼近能力强,能够显著提高辨识精度,并且在线辨识的模糊模型简单有效。将该AFNN用于非线性系统的模糊辨识和化工过程连续搅拌反应器(CSTR)的建模中,仿真结果验证了该方法的有效性,表明该网络能够实现复杂非线性系统的建模,而且建模精度高、收敛速度快。可当作复杂系统建模的一种有效手段。 相似文献
2.
针对非线性辨识问题,基于改进的T-S模型,提出一种自适应模糊神经网络模型(AFNN)。首先,基于模糊竞争学习算法确定系统的模糊空间和模糊规则数,并得出每个样本对每条规则的适用程度。其次,利用卡尔曼滤波算法在线辨识AFNN的后件参数。AFNN具有结构简洁,逼近能力强,能够显著提高辨识精度,并且辨识的模糊模型简单有效。最后,将该AFNN用于非线性系统的模糊辨识,仿真结果验证了该方法的有效性。 相似文献
3.
4.
《模式识别与人工智能》2005,18(5)
主要解决语音信号模型的系统辨识问题.针对过去的模糊聚类算法进行系统辨识时逼近性能不理想的问题,提出了一种新的模糊聚类神经网络(FCNN).该方法以模糊系统模型为基础,将每个状态看作一个模糊系统,用连续的若干序列作为系统的输入,利用改进的模糊聚类辨识算法构成一种新型的模糊聚类神经网络,对系统的输出进行预测.通过语音信号系统辨识的实验,验证了本网络的有效性. 相似文献
5.
6.
7.
赵宝江 《计算机工程与应用》2011,47(21):153-156
基于T-S模型,提出一种非线性系统的模型辨识方法。利用蚁群聚类算法来进行结构辨识,确定系统的模糊空间和模糊规则数。在聚类的基础上,利用遗传算法辨识模糊模型的后件加权参数,得到一个精确的模糊模型,从而实现参数辨识。仿真结果验证了该方法的有效性,表明该方法能够实现非线性系统的辨识,辨识精度高,可当作复杂系统建模的一种有效手段。 相似文献
8.
9.
10.
11.
12.
Hybrid Fuzzy Modelling for Model Predictive Control 总被引:1,自引:0,他引:1
Gorazd Karer Gašper Mušič Igor Škrjanc Borut Zupančič 《Journal of Intelligent and Robotic Systems》2007,50(3):297-319
Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully
used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is
needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a
difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system
hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient
method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs
is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation
example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It
was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a
hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces
a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore,
this approach demonstrates a significant advantage for MPC resulting in a better control performance. 相似文献
13.
Classical fuzzy time series forecasts are comprised of three steps: fuzzification, identification of fuzzy relation, and defuzzification. In this paper, we propose a new approach and add an error learning step to improve forecasts. In the fuzzification step, a hybrid method, based on the fuzzy c-means clustering and the fuzzy Silhouette criterion, is employed to determine the optimal number of intervals, which avoids time-consuming iterations of the whole algorithm. In the defuzzification step, an optimization model is set up to explain the rule of defuzzification. In the model structure, an error term is assembled into the traditional model to express model error, which is predicted by linear fitting and abnormal errors processing. Learning of model errors and considering of data characteristics guarantee good interpretability and accuracy. The numerical results show that the proposed approach has superior forecast performance to existing methods. 相似文献
14.
An improved fuzzy neural network based on Takagi–Sugeno (T–S) model is proposed in this paper. According to characteristics of samples spatial distribution the number of linguistic values of every input and the means and deviations of corresponding membership functions are determined. So the reasonable fuzzy space partition is got. Further a subtractive clustering algorithm is used to derive cluster centers from samples. With the parameters of linguistic values the cluster centers are fuzzified to get a more concise rule set with importance for every rule. Thus redundant rules in the fuzzy space are deleted. Then antecedent parts of all rules determine how a fuzzification layer and an inference layer connect. Next, weights of the defuzzification layer are initialized by a least square algorithm. After the network is built, a hybrid method combining a gradient descent algorithm and a least square algorithm is applied to tune the parameters in it. Simultaneous, an adaptive learning rate which is identified from input-state stability theory is adopted to insure stability of the network. The improved T–S fuzzy neural network (ITSFNN) has a compact structure, high training speed, good simulation precision, and generalization ability. To evaluate the performance of the ITSFNN, we experiment with two nonlinear examples. A comparative analysis reveals the proposed T–S fuzzy neural network exhibits a higher accuracy and better generalization ability than ordinary T–S fuzzy neural network. Finally, it is applied to predict markup percent of the construction bidding system and has a better prediction capability in comparison to some previous models. 相似文献
15.
16.
17.
针对基于T-S模糊模型的非线性系统建模问题,提出了一种基于自组织神经网络的新方法.在T-S模糊模型的建模中,目前常用的模糊C均值聚类算法存在迭代次数多,计算耗时的缺点.首先,利用竞争学习算法对输入空间进行聚类,基于此结果,借助于模糊C均值聚类算法进一步优化聚类结果,提取T-S模糊模型的规则前件隶属函数参数.然后,采用最小二乘法求得T-S模糊模型的规则后件参数,从而建立起非线性系统的T-S模糊模型.最后,仿真结果表明,该方法可以为模糊建模提供好的模型结构,并且有较高的计算效率和精度. 相似文献
18.
Babak Rezaee 《Information Sciences》2010,180(2):241-255
This paper presents a systematic approach to design first order Tagaki-Sugeno-Kang (TSK) fuzzy systems. This approach attempts to obtain the fuzzy rules without any assumption about the structure of the data. The structure identification and parameter optimization steps in this approach are carried out automatically, and are capable of finding the optimal number of the rules with an acceptable accuracy. Starting with an initial structure, the system first tries to improve the structure and, then, as soon as an improved structure is found, it fine tunes its rules’ parameters. Then, it goes back to improve the structure again to find a better structure and re-fine tune the rules’ parameters. This loop continues until a satisfactory solution (TSK model) is found. The proposed approach has successfully been applied to well-known benchmark datasets and real-world problems. The obtained results are compared with those obtained with other methods from the literature. Experimental studies demonstrate that the predicted properties have a good agreement with the measured data by using the elicited fuzzy model with a small number of rules. Finally, as a case study, the proposed approach is applied to the desulfurization process of a real steel industry. Comparing the proposed approach with some other fuzzy systems and neural networks, it is shown that the developed TSK fuzzy system exhibits better results with higher accuracy and smaller size of architecture. 相似文献
19.
《Applied Soft Computing》2007,7(2):577-584
In the paper, as an improvement of fuzzy clustering neural network FCNN proposed by Zhang et al., a novel robust fuzzy clustering neural network RFCNN is presented to cope with the sensitive issue of clustering when outliers exist. This new algorithm is based on Vapnik's ɛ-insensitive loss function and quadratic programming optimization. Our experimental results demonstrate that RFCNN has much better robustness for outliers than FCNN. 相似文献