Real-Time Adaptive Fuzzy Motivations for Evolutionary Behavior Learning by a Mobile Robot

Freund, Wolfgang; Vidal, Tomas Arredondo; Muñoz, César; Navarro, Nicolás; Quirós, Fernando

doi:10.1007/11925231_10

Cited by 1 publication

(2 citation statements)

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“…Fuzzy logic provides the ability of modeling uncertainty, vagueness and imprecision present in the vast majority of real world problems. It has found successful applications in a wide variety of fields, such as decision making [1][2][3][4], control system design [5][6][7][8], data classification [9][10][11][12], decision analysis [13][14][15], expert systems [16][17][18], time-series prediction [19][20][21], robotics [22][23][24], pattern recognition [10,25,26] and so on.…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Parameterization of General Type-2 Fuzzy Sets

Castro

Sánchez

González

et al. 2018

Fuzzy Information and Engineering

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In this paper a new method for the parameterization of general type-2 fuzzy membership functions. The proposed method describes the methodology, equations and pseudo-code for building a set of general type-2 membership functions, which are a combination of two Gaussian-type primary membership functions (Gaussian with uncertain mean, and Gaussian with uncertain standard deviation), with multiple combinations of secondary membership functions (Gaussian, double Gaussian, general bell and trapezoidal). In addition, several application cases are used to illustrate the advantages of the proposed parameterization of general type-2 fuzzy sets; where the membership functions are designed using the parameterization approach and the general type-2 inference system is approximated using the α-planes theory. Simulation results illustrate that the parameterization offers an efficient way to represent these fuzzy sets. The main idea of the approach is to facilitate the use of general type-2 fuzzy systems in real world applications. The main contribution is a proposed new form of parameterizing general type-2 fuzzy sets that simplifies the efficient design of this type of sets.

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Section: Introductionmentioning

confidence: 99%

“…), or(21) and(23). But for a trapezoidal secondary membership function, these equations are not used; instead, (32) and (33) define the required modifiers for the membership function.…”

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confidence: 99%