The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Combining neural networks and fuzzy systems is a great tool for modeling nonlinear systems. Few researches have presented useful or practical results on the case of lack of data, which does not provide necessary information for training the model. In this paper, we proposed a new modeling idea based on nonparametric regression, which provide us prior information for constructing the fuzzy system....
In this brief, we propose a new fuzzy-neural-network (FNN) modeling approach which is applied for the modeling of crude-oil blending. The structure and parameters of FNNs are updated online. The new idea for the structure identification is that the input (precondition) and the output (consequent) spaces partitioning are carried out in the same time index. This idea gives a better explanation for input-output...
In this paper, adaptive hierarchical fuzzy CMAC neural network controller (HFCMAC), for a certain class of nonlinear dynamical system is presented. The main advantages of adaptive HFCMAC control are: Better performance of the controller because adaptive HFCMAC can adjust itself to the changing enviroment and can be implemented in real time applications. The proposed method provides a simple control...
This paper describes a novel fuzzy rule-based modeling approach for some industrial processes. Structure identification is realized by clustering and support vector machines. When the process is slow, fuzzy rules can be obtained automatically. Parameters identification uses the techniques of fuzzy neural networks. A time-varying learning rate assures stability of the modeling error.
Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs, and it is difficult for its static structure to model a dynamic system. In this paper, we use two types...
The conventional fuzzy CMAC neural networks perform well in terms of their fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires an enormous memory and the dimension increase exponentially with the input number. In this paper, we use two techniques to overcome these problems: recurrent and hierarchical structures and propose a new CMAC,...
In this paper we propose a novel online clustering approach which can be applied for nonlinear system modeling. Fuzzy neural networks are used as models whose structure and parameters are updated online. The new idea for the structure identification is that the input (precondition) and the output (consequent) spaces partitioning are carried out in the same time index. This idea gives better explanation...
In this paper we propose a novel on-line clustering approach which can be applied for nonlinear system identification. Both structure and parameters of fuzzy neural networks are updated on-line. The new clustering method for the structure identification can divide input/output data into different groups (rule number) by on-line data. For the parameter learning, our algorithm has two advantages over...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.