Topic
Fuzzy associative matrix
About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.
Papers published on a yearly basis
Papers
More filters
Book•
10 Nov 2005TL;DR: In this paper, a crisp continuous system whose process of evolution depends on differential equations is considered, and fuzzy parameters convert the crisp system into a fuzzy system, and trajectories describing the behavior of the system become fuzzy curves.
Abstract: In previous studies we concentrated on utilizing crisp, numeric simulation to produce discrete event fuzzy systems simulations. Then we extended this research to the simulation of continuous fuzzy systems models. In this study, we continue our study of continuous fuzzy systems using crisp continuous simulation. Consider a crisp continuous system whose process of evolution depends on differential equations. Such a system contains a number of parameters that must be estimated. Usually point estimates are computed and used in the model. However, these point estimates typically have uncertainty associated with them. We propose to incorporate uncertainty by using fuzzy numbers as estimates of these unknown parameters. Fuzzy parameters convert the crisp system into a fuzzy system. Trajectories describing the behavior of the system become fuzzy curves. We will employ crisp continuous simulation to estimate these fuzzy trajectories. Three examples are discussed.
98 citations
TL;DR: The proposed novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC.
Abstract: This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.
98 citations
TL;DR: This approach exploits the quasi-linear nature of Takagi-Sugeno models and builds-up the control rule-base structure and adapts it in on-line mode with recursive, non-iterative learning.
97 citations
01 Oct 1997
TL;DR: A new methodology is introduced for designing and tuning the scaling gains of the conventional fuzzy logic controller (FLC) based on its well-tuned linear counterpart, and the relationship between the scale gains and the performance can be deduced to produce the comparative tuning algorithm, which can tune the scaled gains to their optimum by less trial and error.
Abstract: A new methodology is introduced for designing and tuning the scaling gains of the conventional fuzzy logic controller (FLC) based on its well-tuned linear counterpart. The conventional FLC with a linear rule base is very similar to its linear counterpart. The linear three-term controller has proportional, integral and/or derivative gains. Similarly, the conventional fuzzy three-term controller also has fuzzy proportional, integral and/or derivative gains. The new concept "fuzzy transfer function" is invented to connect these fuzzy gains with the corresponding scaling gains. The comparative gain design is presented by using the gains of the well-tuned linear counterpart as the initial fuzzy gains of the conventional FLC. Furthermore, the relationship between the scaling gains and the performance can be deduced to produce the comparative tuning algorithm, which can tune the scaling gains to their optimum by less trial and error. The performance comparison in the simulation demonstrates the viability of the new methodology.
97 citations
TL;DR: The improved neuro-fuzzy system incorporates the best of both technologies and compensates for the shortcomings of each and was significantly superior to those of the back-propagation based neural network and the maximum likelihood approaches.
Abstract: Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being a ‘black box’. It is extremely difficult to document how specific classification decisions are reached. Fuzzy systems, on the other hand, have the capability to represent classification decisions explicitly in the form of fuzzy ‘if-then’ rules. However, the construction of a knowledge base, especially the fine-tuning of the fuzzy set parameters of the fuzzy rules in a fuzzy expert system, is a tedious and subjective process. This research has developed a new, improved neuro-fuzzy image classification system based on the synergism between neural networks and fuzzy expert systems. It incorporates the best of both technologies and compensates for the shortcomings of each. The learning algorithms of neural networks developed here are used to automate the derivation of fuzzy set parameters for the ...
97 citations