Sentence examples for degrees of fuzzy from inspiring English sources

In this section, we present the details of the RL-FNN algorithm and how it computes the degrees of fuzzy membership functions and does the inference for our simulation scenario.

The operation of the fuzzification calculates the degrees for each evaluated parameter (input) belonging to the three membership functions, e.g., for RC_S this operation calculates {(upmu _{N})(RC_S), (upmu _{M} RC_S), (upmu _{F})(RC_S),} with (upmu _{N})(RC_S), (upmu _{M} RC_S) and (upmu _{F})(RC_S) the membership degrees of fuzzy sets N, M, and F, respectively.

A membership value between 0 and 1 indicate the degree of fuzzy relationship.

In this regard, preference degree of fuzzy numbers and the concept of fuzzy distance can be employed.

To address these problems, we propose a method to define the image definition evaluation function based on the fuzzy entropy, which was proposed to measure the fuzzy degree of fuzzy set reasonably [17].

Two parameters are set: deterministic factor is used to express confidence degree of fuzzy rules while reliability value is used to express the reliability degree of storing proposition in decision information place.

In this investigation, a two-degree-of-freedom fuzzy controller, consisting of a one-step-ahead fuzzy prefilter in the feed-forward loop and a PI-like fuzzy controller in the feedback loop, has been proposed for foot trajectory tracking control of a hydraulically actuated hexapod robot.

Quantitative evaluation is converted to qualitative evaluation by FCE based on the membership degree theory of fuzzy mathematics.

Key objective of this work is to guide DMs in finding out the optimal product-mix with higher degree of satisfaction with lesser degree of fuzziness under tripartite fuzzy environment.

Experimental results exhibit that the proposed controller manifests better foot trajectory tracking performance compared to single-degree-of-freedom (SDF) fuzzy controller or optimal classical controller like state feedback LQR controller.

Given a datum, degrees of truth of fuzzy predicates associated with each cluster are computed using continuous membership functions defined over data features.