A Self-Tuning Method of Fuzzy Inference Rules by Descent Method

Fuzzy Logic ◽  
1993 ◽  
pp. 465-475 ◽  
Author(s):  
Hiroyoshi Nomura ◽  
Isao Hayashi ◽  
Noboru Wakami
Author(s):  
Muhammad Aziz Muslim ◽  
Goegoes Dwi Nusantoro ◽  
Rini Nur Hasanah ◽  
Mokhammad Hasyim Asy’ari

This paper describes the method to control a hybrid robot whose main task is to climb a pole to place an object on the top of the pole. The hybrid pole-climbing robot considered in this paper uses 2 Planetary PG36 DC-motors as actuators and an external rotary encoder sensor to provide a feedback on the change in robot orientation during the climbing movement. The orientation control of the pole-climbing robot using self-tuning method has been realized by identifying the transfer function of the actuator system under consideration, being followed with the calculation of control parameters using the self-tuning pole-placement method, and furthermore being implemented on the external rotary encoder sensor. Self-tuning pole-placement method has been explored to control the parameters q<sub>0</sub>, q<sub>1</sub>, q<sub>2</sub>, and p<sub>1</sub> of the controller. The experiments were done on a movement path in a form of a cylindrical pole. The first experiment was done based one the change in rotation angle of the rotary sensor with the angle values greater than 50˚ in the positive direction, whereas the second experiment was done with the angle values greater than -50˚ in the negative direction. The experiment results show that the control of the robot under consideration could maintain its original position at the time of angle change disturbance and that the robot could climb in a straight direction within the specified tolerance of orientation angle change.


2017 ◽  
Vol 7 (1.5) ◽  
pp. 170 ◽  
Author(s):  
Saravanan Chandrasekaran ◽  
Vijay Bhanu Srinivasan ◽  
Latha Parthiban

The Quality of Service (QoS) is enforced in discovering an optimal web service (WS).The QoS is uncertain due to the fluctuating performance of WS in the dynamic cloud environment. We propose a Fuzzy based Bayesian Network (FBN) system for Efficient QoS prediction. The novel method comprises three processes namely Semantic QoS Annotation, QoS Prediction, and Adaptive QoS using cloud infrastructure. The FBN employs the compliance factor to measure the performance of QoS attributes and fuzzy inference rules to infer the service capability. The inference rules are defined according to the user preference which assists to achieve the user satisfaction. The FBN returns the optimal WSs from a set of functionally equivalent WS. The unpredictable and extreme access of the selected WS is handled using cloud infrastructure. The results show that the FBN approach achieves nearly 95% of QoS prediction accuracy when providing an adequate number of past QoS data, and improves the prediction probability by 2.6% more than that of the existing approach.  


2007 ◽  
Vol 177 (21) ◽  
pp. 4768-4784 ◽  
Author(s):  
Liming Hu ◽  
H.D. Cheng ◽  
Ming Zhang

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