Type reduction techniques for two-dimensional interval type-2 fuzzy sets

Saxena, Vaibhav; Yadala, Nikhil; Chourasia, Rishav; Rhee, Frank Chung-Hoon

doi:10.1109/fuzz-ieee.2017.8015724

Cited by 3 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To conduct the type-reduction for n-D IT2FS, Saxena et al 34 first estimated the centroid boundary of 2-D IT2FS through the projection. In their method, the 2-D IT2FS is projected into a reference axis defined by a normal vector [cos 𝜃, sin 𝜃], where 𝜃 ∈ [0, 𝜋] is the direction angle.…”

Section: N-dimensional Interval Type-2 Fuzzy Inference Systemmentioning

confidence: 99%

“…Inspired by this method, a type-reduction method for n-D IT2FS is proposed with the consideration of computing-efficiency. In Saxena et al 34 's algorithm for 2-D IT2FS, the normal vectors are expressed through an angle 𝜃. However, in n-D IT2FS, the number of angles used to express normal vector will increase to n − 1.…”

Section: N-dimensional Interval Type-2 Fuzzy Inference Systemmentioning

confidence: 99%

See 1 more Smart Citation

Continuous interval type‐2 fuzzy Q‐learning algorithm for trajectory tracking tasks for vehicles

Xuan

Lam

Shi

et al. 2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Trajectory tracking is a fundamental but challenging task for vehicle automation. In addition to the system nonlinearity, the main difficulties in the trajectory tracking task are due to the environmental noise and the model uncertainties under different driving scenarios. Considering the uncertainties in the environment, the reinforcement learning method with continuous action and noise-resistance capability could be a promising way to overcome these issues. In this article, a novel continuous interval type-2 fuzzy Q-learning (CIT2FQL) algorithm is proposed to deal with the trajectory tracking task. By introducing the n-dimensional interval type-2 fuzzy inference system (n-D IT2FIS) in fuzzy Q-learning, our proposed method achieves the continuous Q-learning by combining the action interpolation with IT2FIS for the first time. We also proposed a simplified type-reduction method for n-D IT2FIS to improve the computing efficiency of the proposed method. Moreover, a radial basis function (RBF) layer is chosen as the basis function to achieve the q-value interpolation. Finally, a trajectory tracking task in a simulation environment is conducted to verify the effectiveness and robustness of the proposed method under different scenarios. The results demonstrate that the proposed method has better robustness and noise-resistance capability while maintaining good tracking performance compared with the state-of-the-art baseline algorithms including double deep Q network (DDQN), proximal policy optimization (PPO), and interval type-2 dynamic fuzzy Q-learning (IT2DFQL).

show abstract

Section: N-dimensional Interval Type-2 Fuzzy Inference Systemmentioning

confidence: 99%

Section: N-dimensional Interval Type-2 Fuzzy Inference Systemmentioning

confidence: 99%

Continuous interval type‐2 fuzzy Q‐learning algorithm for trajectory tracking tasks for vehicles

Xuan

Lam

Shi

et al. 2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Over the past decade, many researchers have developed Type-2 Fuzzy Logic System (T2FLS) which is an improvement from conventional fuzzy or Type-1 Fuzzy Logic System [8][9][10] [11]. Type 2 has a better ability to overcome uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Design of Automatic Braking System Using Interval Type-2 Fuzzy Logic System

Setiady¹,

Wahyunggoro²,

Nugroho³

2019

Journal of Electronic Systems

View full text Add to dashboard Cite

The car braking system is a very important part of car safety. The effectiveness of Conventional braking system is strongly influenced by the ability and the driver's experience. It is very vulnerable to accidents if the driver fails to operate the braking system. Because of that, an automation of the braking system is needed that can decrease the accident's impact. The Automatic braking systems require the reasoning to manage information before making a decision that brakes or slows down. There is uncertainty in determining membership function of distance and velocity. It is difficult to determine an exact membership function. In this paper, the system used Interval type-2 fuzzy logic control that can work well in an unstructured environment and have the ability to overcome the uncertainty of information. The result is that the pressure on the brake pads is gradually increased with an average increase of 5.6%. At a distance of 34 m, the pressure is at maximum value.

show abstract