On genetic representation of high dimensional fuzzy systems

Lee, M.A.

doi:10.1109/isuma.1995.527790

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…The first 9 elements in the vector, R (1)(2)(3)(4)(5)(6)(7)(8)(9), represent the rules as shown in Table 1 and R (10)(11)(12)(13)(14)(15) represent the boundaries of the membership functions as shown in Figure 3. The membership functions are assumed to be symmetric around zero.…”

Section: Methodsmentioning

confidence: 99%

“…The membership functions are assumed to be symmetric around zero. R (1)(2)(3)(4)(5)(6)(7)(8)(9) are integers with values from one to three. One, two and three represent negative (N), zero (ZO) and positive (P) membership functions for the inputs, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Optimizing the fuzzy system for a higher noise level results in good performance at lower noise levels. Lee presented three fuzzy system architectures and methods for automatically designing them for high dimensional problems [9]. The results indicate that the real coded algorithms consistently outperformed the binary coded algorithms in both the final performance of the system and the performance of the search algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Development of a Genetic Fuzzy Controller and Its Application to a Noisy Inverted Double Pendulum

Sathyan¹,

Cohen²

2018

Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications

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Fuzzy logic is used in a variety of applications due to its universal approximator attribute and non-linear characteristics. The tuning of the parameters of a fuzzy logic system, viz. the membership functions and the rulebase, requires a lot of trial and error. This process could be simplified by using a heuristic search algorithm like genetic algorithm (GA). In this chapter, we discuss the design of such a genetic fuzzy controller that can control an inverted double pendulum. GA improves the fuzzy logic controller (FLC) with each generation during the training process to obtain an FLC that can bring the pendulum to its inverted position. After training, the effectiveness of the FLC is tested for different scenarios by varying the initial conditions. We also show the effectiveness of the FLC even when subjected to noise and how the performance improves when the controller is tuned with noise.

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