Takagi–Sugeno Fuzzy Regression Trees With Application to Complex Industrial Modeling

Xia, Heng; Yu, Wen; Cui, Canlin; Qiao, Junfei

doi:10.1109/tfuzz.2022.3221790

Cited by 6 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Naderkhani et al [69] proposed an adaptive neuro-fuzzy inference system for analyzing and predicting nonparametric fuzzy regression functions with crisp-valued inputs and symmetric trapezoidal fuzzy outputs. Xia et al [70] developed a novel regression model built around a Takagi-Sugeno fuzzy regression tree to address complex industrial modeling problems, while Zhang et al [71] introduced an interpretable model based on graph community neural networks and time-series fuzzy decision trees for predicting the delays experienced by a high-speed train.…”

Section: A Non-deep Learning-based Methodsmentioning

confidence: 99%

Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review

Lu,

Ma,

Zhang

2024

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

Machine learning draws its power from various disciplines, including computer science, cognitive science, and statistics. Although machine learning has achieved great advancements in both theory and practice, its methods have some limitations when dealing with complex situations and highly uncertain environments. Insufficient data, imprecise observations, and ambiguous information/relationships can all confound traditional machine learning systems. To address these problems, researchers have integrate machine leaning from different aspects, and fuzzy techniques including fuzzy sets, fuzzy systems, fuzzy logic, fuzzy measures, fuzzy relations, and so on. This paper presents a systematic review of fuzzy machine learning, from theory, approach to application, with the overall objective of providing an overview of recent achievements in the field of fuzzy machine learning. To this end, the concepts and frameworks discussed are divided into five categories: (a) fuzzy classical machine learning; (b) fuzzy transfer learning; (c) fuzzy data stream learning; (d) fuzzy reinforcement learning; and (e) fuzzy recommender systems. The literature presented should provide researchers with a solid understanding of the current progress in fuzzy machine learning research and its applications.

show abstract

Section: A Non-deep Learning-based Methodsmentioning

confidence: 99%

Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review

Lu,

Ma,

Zhang

2024

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

show abstract

“…The conceptualization of the fuzzy inference system (FIS) was marked by the introduction of two distinguished methods, namely the Mamdani and the Takagi-Sugeno (T-S) methods [36][37][38][39]. The former relies on fuzzy outputs that require a subsequent defuzzification, while the latter employs fired multilinear equations followed by a weighted averaging technique.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, unlike prior works in refs. [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] which completely rely on designers' expert knowledge to relate the fuzzy inputs and outputs, this study avoids complete dependency on expert knowledge by utilizing the gathered knowledge of an RL agent after extensive training in a simulated environment.…”

Section: Introductionmentioning

confidence: 99%

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

Zaman,

2024

Robotica

View full text Add to dashboard Cite

In this study, a fuzzy reinforcement learning control (FRLC) is proposed to achieve trajectory tracking of a differential drive mobile robot (DDMR). The proposed FRLC approach designs fuzzy membership functions to fuzzify the relative position and heading between the current position and a prescribed trajectory. Instead of fuzzy inference rules, the relationship between the fuzzy inputs and actuator voltage outputs is built using a reinforcement learning (RL) agent. Herein, the deep deterministic policy gradient (DDPG) methodology consisted of actor and critic neural networks is employed in the RL agent. Simulations are conducted with considering varying slip ratio disturbances, different initial positions, and two different trajectories in the testing environment. In the meantime, a comparison with the classical DDPG model is presented. The results show that the proposed FRLC is capable of successfully tracking different trajectories under varying slip ratio disturbances as well as having performance superiority to the classical DDPG model. Moreover, experimental results validate that the proposed FRLC is also applicable to real mobile robots.

show abstract