Optimal state space partitioning

1994 ◽  
Vol 24 (8) ◽  
pp. 1174-1190
Author(s):  
C. Olivier
Keyword(s):  
State Space ◽  
Space Partitioning ◽  
Optimal State

Hierarchical Neuro-Fuzzy Systems Part II

2011 ◽  
pp. 817-824
Author(s):  
Marley Vellasco ◽  
Marco Pacheco ◽  
Karla Figueiredo ◽  
Flavio Souza
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Learning Process ◽  
Fuzzy Systems ◽  
Space Partitioning ◽  
Learning Methods ◽  
New Class ◽  
Neuro Fuzzy ◽  
Binary Space Partitioning ◽  
Priori Information

This paper describes a new class of neuro-fuzzy models, called Reinforcement Learning Hierarchical Neuro- Fuzzy Systems (RL-HNF). These models employ the BSP (Binary Space Partitioning) and Politree partitioning of the input space [Chrysanthou,1992] and have been developed in order to bypass traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity to create their own structure and rules (ANFIS [Jang,1997], NEFCLASS [Kruse,1995] and FSOM [Vuorimaa,1994]). These new models, named Reinforcement Learning Hierarchical Neuro-Fuzzy BSP (RL-HNFB) and Reinforcement Learning Hierarchical Neuro-Fuzzy Politree (RL-HNFP), descend from the original HNFB that uses Binary Space Partitioning (see Hierarchical Neuro-Fuzzy Systems Part I). By using hierarchical partitioning, together with the Reinforcement Learning (RL) methodology, a new class of Neuro-Fuzzy Systems (SNF) was obtained, which executes, in addition to automatically learning its structure, the autonomous learning of the actions to be taken by an agent, dismissing a priori information (number of rules, fuzzy rules and sets) relative to the learning process. These characteristics represent an important differential when compared with existing intelligent agents learning systems, because in applications involving continuous environments and/or environments considered to be highly dimensional, the use of traditional Reinforcement Learning methods based on lookup tables (a table that stores value functions for a small or discrete state space) is no longer possible, since the state space becomes too large. This second part of hierarchical neuro-fuzzy systems focus on the use of reinforcement learning process. The first part presented HNFB models based on supervised learning methods. The RL-HNFB and RL-HNFP models were evaluated in a benchmark control application and a simulated Khepera robot environment with multiple obstacles.

scholarly journals Optimal State Space Control of DC Motor

2008 ◽  
Vol 41 (2) ◽  
pp. 5796-5801 ◽  
Author(s):  
M. Ruderman ◽  
J. Krettek ◽  
F. Hoffmann ◽  
T. Bertram
Keyword(s):  
State Space ◽  
Dc Motor ◽  
Optimal State
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