Optimization of interval type-2 fuzzy systems for image edge detection

González, Claudia I.; Melín, Patricia; Castro, Juan R.; Castillo, Oscar; Mendoza, Olivia

doi:10.1016/j.asoc.2014.12.010

Cited by 143 publications

(39 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is multiple works using Type-2 FLS applied to different optimization problems, the use of this technique significantly improves the results, consult in [2,5,20,25]. The principles of Type-2 FLS can be consulted in [17,[26][27][28][29]. We decided to combine fuzzy logic with our proposal based on works found in the literature, where it is shown that type 2 fuzzy controllers offer a higher performance when applied to robust problems.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The robot body is symmetrical around the perpendicular axis and the center of mass is at the geometric center of the body. It has two driving wheels that are fixed to the axis that passes through the center of mass "C" represented by {C, Xm, Ym}, and one passive wheel that prevents the robot from tipping over as it moves on a plane [26,27]. The dynamics of the mobile robot is represented by the following set of Equations (6) and (7), [5,29,36].…”

Section: Problem To Be Optimizedmentioning

confidence: 99%

See 1 more Smart Citation

A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

2017

Self Cite

View full text Add to dashboard Cite

Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ) and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Section: Problem To Be Optimizedmentioning

confidence: 99%

A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, a type‐2 membership grade can be any subset in the range [0, 1]. With respect to each primary membership, there is an existence of secondary membership that briefs the possible formulation of primary membership, which can also lie between 0 and 1 . The concept of uncertainty is characterized by a region and it is notified as the Foot Print of Uncertainty (FOU).…”

Section: Methodsmentioning

confidence: 99%

“…With respect to each primary membership, there is an existence of secondary membership that briefs the possible formulation of primary membership, which can also lie between 0 and 1. 15,23,24 The concept of uncertainty is characterized by a region and it is notified as the Foot Print of Uncertainty (FOU). When we describe lÃ x; u ð Þ51; 8u 2 J x ½0; 1, we can obtain an interval type-2 membership function.…”

Section: Interval Type-2 Fuzzy C-meansmentioning

confidence: 99%

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Alagarsamy

Kamatchi

Govindaraj

et al. 2017

Int J Imaging Syst Tech

View full text Add to dashboard Cite

This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type‐2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard Tanimoto Coefficient Index. The proposed CBS combined with interval type‐2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph‐cut based, and self‐organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology.

show abstract

“…For example, the popular ANFIS model for T1 fuzzy systems [24] combines mathematical programming (least squares estimation) and a gradient-based algorithm in its optimization. EC algorithms are recommended for the optimization of IT2 fuzzy systems, because derivatives are difficult to compute in an IT2 fuzzy system (especially when the LMF and/or UMF formulas include tests about the location of their independent variable), and such algorithms are globally convergent [34], [4], [15], [44], [28]. There are many such EC algorithms, e.g., genetic algorithms, simulated annealing, particle swarm optimization, etc.…”

Section: Fuzzymentioning

confidence: 99%

Recommendations on designing practical interval type-2 fuzzy systems

Mendel

2019

Engineering Applications of Artificial Intelligence

105

View full text Add to dashboard Cite

Interval type-2 (IT2) fuzzy systems have become increasingly popular in the last 20 years. They have demonstrated superior performance in many applications. However, the operation of an IT2 fuzzy system is more complex than that of its type-1 counterpart. There are many questions to be answered in designing an IT2 fuzzy system: Should singleton or non-singleton fuzzifier be used? How many membership functions (MFs) should be used for each input? Should Gaussian or piecewise linear MFs be used? Should Mamdani or Takagi-Sugeno-Kang (TSK) inference be used? Should minimum or product t-norm be used? Should type-reduction be used or not? How to optimize the IT2 fuzzy system? These questions may look overwhelming and confusing to IT2 beginners. In this paper we recommend some representative starting choices for an IT2 fuzzy system design, which hopefully will make IT2 fuzzy systems more accessible to IT2 fuzzy system designers.

show abstract

Optimization of interval type-2 fuzzy systems for image edge detection

Cited by 143 publications

References 24 publications

A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Recommendations on designing practical interval type-2 fuzzy systems

Contact Info

Product

Resources

About