A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems

In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.

Mobile robot path planning using membrane evolutionary artificial potential field

A Generalized Type-2 Fuzzy Controller (GT2FC) was developed.Simulation of a GT2FC for a mobile robot is presented.Experiments support the notion that GT2FC handles more uncertainty. The aim of this paper is to show that a Generalized Type-2 Fuzzy Control System can outperform Type-1 and Interval Type-2 Fuzzy Control Systems when external perturbations are present. A Generalized Type-2 Fuzzy System can handle better uncertainty because of the nature of its membership functions, and as such, they are better tailored for situations where external noise is present. To test the noise resilience of Fuzzy Controllers, the design of a Fuzzy Controller for a mobile robot is presented in this paper, in conjunction with three types of external perturbations: band-limited white noise, pulse noise, and uniform random number noise. Noise resilience is measured through different performance indices, such as ITAE, ITSE, IAE, and ISE. Simulation results show that Generalized Type-2 Fuzzy Controllers outperform their Type-1 and Interval Type-2 Fuzzy Controller counterparts in the presence of external perturbations.

Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems

The study introduces and discusses a principle of justifiable granularity, which supports a coherent way of designing information granules in presence of experimental evidence (either of numerical or granular character) The term ''justifiable'' pertains to the construction of the information granule, which is formed in such a way that it is (a) highly legitimate (justified) in light of the experimental evidence, and (b) specific enough meaning it comes with a well-articulated semantics (meaning) The design process associates with a well-defined optimization problem with the two requirements of experimental justification and specificity A series of experiments is provided as well as a number of constructs carried for various formalisms of information granules (intervals, fuzzy sets, rough sets, and shadowed sets) are discussed as well

Building the fundamentals of granular computing: A principle of justifiable granularity

The Fuzzy c-means is one of the most popular ongoing area of research among all types of researchers including Computer science, Mathematics and other areas of engineering, as well as all areas of optimization practices. Several problems from various areas have been effectively solved by using FCM and its different variants. But, for efficient use of the algorithm in various diversified applications, some modifications or hybridization with other algorithms are needed. A comprehensive survey on FCM and its applications in more than one decade has been carried out in this paper to show the efficiency and applicability in a mixture of domains. Also, another intention of this survey is to encourage new researchers to make use of this simple algorithm (which is popularly called soft classification model) in problem solving.

Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014

A generalized type-2 fuzzy granular approach with applications to aerospace

Explanatory diagram of how the proposed approach measures and defines the uncertainty, and forms an IT2 Fuzzy Set with such uncertainty. A technique for forming information granules is presented in this paper.Based on the theory of uncertainty-based information, an approach which forms information granules is presented.Two implementations are proposed which form Interval Type-2 Fuzzy information granules.These approaches capture multiple evaluations of uncertainty from different samples and use these models to measure the uncertainty from the difference among them.The proposed approaches are tested with classification and curve identification benchmark datasets with very good results. A technique for forming information granules is shown in this paper. Based on the theory of uncertainty-based information, an approach toward a general base is given which forms information granules. Two implementations are proposed which form Interval Type-2 Fuzzy information granules, both with Takagi-Sugeno-Kang consequents optimized with Cuckoo search algorithm. These approaches capture multiple evaluations of uncertainty from taken samples and use these models to measure the uncertainty from the difference between them. The proposed approaches are tested with classification and curve identification datasets.

Information granule formation via the concept of uncertainty-based information with Interval Type-2 Fuzzy Sets representation and Takagi-Sugeno-Kang consequents optimized with Cuckoo search

Mauricio A. Sanchez

Papers

Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems

A generalized type-2 fuzzy granular approach with applications to aerospace

Information granule formation via the concept of uncertainty-based information with Interval Type-2 Fuzzy Sets representation and Takagi-Sugeno-Kang consequents optimized with Cuckoo search

Fuzzy granular gravitational clustering algorithm for multivariate data

Design of an interval Type-2 fuzzy model with justifiable uncertainty