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To solve the problem of the neural network architecture design, a dynamic feedforward neural network architecture design method based on information entropy is proposed. In this method, the neural network's cost function is composed of the cross entropy of the neural network's expected output and actual output and Renyi's entropy of the hidden node's output. This does not require the learning samples...
Many Takagi-Sugeno (T-S) fuzzy systems use the linear matrix inequality (LMI) to design the controllers. Although LMI can be solved off-line, there are still some difficulties in T-S fuzzy control design with LMI, such as complexities in the analysis and the computation, and conservativeness for complex systems. In this paper, we first transform the T-S fuzzy system into a time-varying nonlinear system...
Many uncertain nonlinear systems can be modeled by the linear-in-parameter model, and the parameters are uncertain in the sense of fuzzy numbers. Dual fuzzy equations are alternative models for these nonlinear systems identification, while the solutions of the fuzzy equations are the controllers. In this paper, we propose a novel fuzzy controller via dual fuzzy equations. Two types of neural networks...
Since the fuzzy cerebellar model articulation controller (FCMAC) uses linguistic variables, it is highly intuitive and easily comprehended. Despite the FCMAC's good local generalization capability for approximating nonlinear functions and fast learning, a normal FCMAC requires huge memory, and its dimension increases exponentially with the number of inputs. In order to overcome the memory explosion...
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 proposes a novel anti-swing control strategy for an overhead crane. The controller includes both position regulation and anti-swing control. Since the crane model is not exactly known, fuzzy CMAC are used to compensate friction, gravity as well as the coupling between position and anti-swing control. Real-time experiments are presented comparing this new stable anti-swing PD control strategy...
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.
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