A comparison of the decision tree approach and the neural-networks-based heuristic dynamic programming approach for subcircuit extraction problem

2003 ◽  
Author(s):  
Nian Zhang ◽  
Donald C. Wunsch II
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Decision Tree ◽  
Programming Approach ◽  
Dynamic Programming Approach ◽  
The Neural Networks ◽  
Heuristic Dynamic Programming ◽  
Subcircuit Extraction ◽  
Tree Approach

ODT: Optimal deadline-based trajectory for mobile sinks in WSN: A decision tree and dynamic programming approach

2015 ◽  
Vol 77 ◽  
pp. 128-143 ◽  
Author(s):  
Farzad Tashtarian ◽  
M.H. Yaghmaee Moghaddam ◽  
Khosrow Sohraby ◽  
Sohrab Effati
Keyword(s):  
Dynamic Programming ◽  
Decision Tree ◽  
Programming Approach ◽  
Dynamic Programming Approach ◽  
Mobile Sinks

scholarly journals Discrete Globalised Dual Heuristic Dynamic Programming in Control of the Two-Wheeled Mobile Robot

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Marcin Szuster ◽  
Zenon Hendzel
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Control System ◽  
Mobile Robot ◽  
Tracking Control ◽  
Dynamic Programming Algorithm ◽  
Wheeled Mobile Robot ◽  
Programming Algorithm ◽  
The Neural Networks ◽  
Heuristic Dynamic Programming

Network-based control systems have been emerging technologies in the control of nonlinear systems over the past few years. This paper focuses on the implementation of the approximate dynamic programming algorithm in the network-based tracking control system of the two-wheeled mobile robot, Pioneer 2-DX. The proposed discrete tracking control system consists of the globalised dual heuristic dynamic programming algorithm, the PD controller, the supervisory term, and an additional control signal. The structure of the supervisory term derives from the stability analysis realised using the Lyapunov stability theorem. The globalised dual heuristic dynamic programming algorithm consists of two structures: the actor and the critic, realised in a form of neural networks. The actor generates the suboptimal control law, while the critic evaluates the realised control strategy by approximation of value function from the Bellman’s equation. The presented discrete tracking control system works online, the neural networks’ weights adaptation process is realised in every iteration step, and the neural networks preliminary learning procedure is not required. The performance of the proposed control system was verified by a series of computer simulations and experiments realised using the wheeled mobile robot Pioneer 2-DX.

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