Journal ArticleDOI
A reinforcement learning algorithm with evolving fuzzy neural networks
Hitesh Shah,Madan Gopal +1 more
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TLDR
A dynamic evolving fuzzy neural network (DENFIS) function approximation approach to RL systems and results have demonstrated that the proposed approach performs well in reinforcement learning problems.About:
This article is published in IFAC Proceedings Volumes.The article was published on 2014-01-01. It has received 8 citations till now. The article focuses on the topics: Learning classifier system & Unsupervised learning.read more
Citations
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Journal ArticleDOI
Interval Valued T-Spherical Fuzzy Information Aggregation Based on Dombi t-Norm and Dombi t-Conorm for Multi-Attribute Decision Making Problems
TL;DR: In this paper, the authors enhance the notion of Dombi aggregation operators by introducing the DAOs in the interval-valued T-spherical fuzzy (IVTSF) environment where the uncertain and ambiguous information is described with the help of membership grade (MG), abstinence grade (AG), non-membership grade (NMG), and refusal grade (RG) using closed sub-intervals of [0, 1].
Book ChapterDOI
Application of Evolutionary Reinforcement Learning (ERL) Approach in Control Domain: A Review
TL;DR: This paper presents a concise review on implementing reinforcement learning with evolutionary algorithms and proposes a Q-value-based GRL for fuzzy controller (QGRF) where evolution is performed after each trial in contrast to GA where many trials are required to be performed before evolution.
Journal Article
Reinforcement Learning in Neural Networks: A Survey
TL;DR: This paper describes the state of the art of NNRL algorithms, with a focus on robotics applications and a comprehensive survey is started with a discussion on the concepts of RL.
Journal ArticleDOI
On the Application of a Design of Experiments along with an ANFIS and a Desirability Function to Model Response Variables
Luis Pérez,J Carmelo +1 more
TL;DR: In this paper, a soft computing technique based on fuzzy logic is used to overcome the limitations of conventional regression models for multi-objective optimization in manufacturing processes, and more accurate results than those obtained from regression techniques are obtained.
References
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Proceedings ArticleDOI
Reinforcement Learning is Direct Adaptive Optimal Control
TL;DR: An emerging deeper understanding of neural network reinforcement learning methods is summarized that is obtained by viewing them as a synthesis of dynamic programming and stochastic approximation methods.
Journal ArticleDOI
Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications
TL;DR: Parts of fuzzy logic, neural networks and genetic algorithms that pertain to realisation of intelligent control systems are reviewed, providing a compact reference for their application.
Proceedings ArticleDOI
Fuzzy Q-learning
P.Y. Glorennec,Lionel Jouffe +1 more
TL;DR: This paper proposes an adaptation of Watkins' Q-learning for fuzzy inference systems where both the actions and the Q-functions are inferred from fuzzy rules, showing its effectiveness.
Journal ArticleDOI
Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning
Meng Joo Er,Chang Deng +1 more
TL;DR: A dynamic fuzzy Q-learning method that is capable of tuning fuzzy inference systems (FIS) online and a novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q- learning.
Journal Article
Neuro-Fuzzy Systems: A Survey
TL;DR: A general vision of the area describing the most known hybrid neuro-fuzzy techniques, its advantages and disadvantages is summarized.