Q-Value Based Particle Swarm Optimization for Reinforcement Neuro-Fuzzy System Design

Journal Title: International Journal on Computer Science and Engineering - Year 2011, Vol 3, Issue 10

Abstract

This paper proposes a combination of particle swarm optimization (PSO) and Q-value based safe reinforcement learning scheme for neuro-fuzzy systems (NFS). The proposed Q-value based particle swarm optimization (QPSO) fulfills PSO-based NFS with reinforcement learning; that is, it provides PSO-based NFS an alternative to learn optimal control policies under environments where only weak reinforcement signals are available. The reinforcement learning scheme is designed by Lyapunov principles and enjoys a number of practical benefits, including the ability of maintaining a system's state in a desired operating range and efficient learning. In the QPSO, parameters on a NFS are encoded in a particle evaluated by Q-value. The Q-value cumulates the reward received during a learning trial and is used as the fitness function for PSO evolution. During the trail, one particle is selected from the swarm; meanwhile, a corresponding NFS is built and applied to the environment with an immediate feedback reward. The applicability of QPSO is shown through simulations in single-link and double-link inverted pendulum system.

Authors and Affiliations

Yi-Chang Cheng , Sheng-Fuu Lin , Chi-Yao Hsu

Keywords

Related Articles

Relation based Ontology Matching using Alignment Strategies

The set of relation within a knowledge domain will be expressed with a help of Ontology, but data within the knowledge domain get scattered all over its space. To get a most precise result there must be necessary to rela...

Grid Scheduling using Differential Evolution (DE) for solving multi-objective optimization parameters

The computational grid is a collection and aggregation of parallel, distributed, and heterogeneous resources. Grid Scheduling is the complex issue to manage the heterogeneous resources. The proposed approach considers th...

Study of Data Mining Approach for Mobile Computing Environment

Efficient Data mining Techniques are required to discover useful Information andknowledge. This is due to the effective involvement of computers and the improvement inDatabase Technology which has provided large Data. Th...

A New Image Steganography Approach for Information Security Using Gray Level Images in Spatial Domain

A new image steganography method for hiding data using Gray Level Images in Spatial Domain is proposed in this paper. This method uses the 5th, 6th and 7th bits of pixel value for insertion and retrieval of message by us...

Observer Design for Simultaneous State and Faults Estimation

This paper addresses the problem of state and faults estimation for Takagi-Sugeno nonlinear systems. Based on this structure for modeling, a proportional integralmultiple observer with unknown inputs is proposed in order...

Download PDF file
  • EP ID EP150436
  • DOI -
  • Views 48
  • Downloads 0

How To Cite

Yi-Chang Cheng, Sheng-Fuu Lin, Chi-Yao Hsu (2011). Q-Value Based Particle Swarm Optimization for Reinforcement Neuro-Fuzzy System Design. International Journal on Computer Science and Engineering, 3(10), 3477-3489. https://europub.co.uk/articles/-A-150436