THE INTEGRATION OF EXTENSION THEORY TO DESIGN A NEW FUZZY INFERENCE MODEL

2000 ◽  
Vol 09 (04) ◽  
pp. 473-492 ◽  
Author(s):  
YO-PING HUANG ◽  
HUNG-JIN CHEN ◽  
CHI-PENG OUYANG
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Extension Theory ◽  
Turning Process ◽  
Inference Model ◽  
Single Input Single Output ◽  
Identification Process ◽  
Single Output ◽  
System Requirement ◽  
Single Input

A novel extension-based fuzzy model is proposed in this paper. The newly established extension theory is integrated into the conventional fuzzy system to enhance the reasoning capability. In parameter identification process, adjusting a membership function to satisfy one pattern may deteriorate the others performance and result in a lengthy turning process. This incompatible issue is alleviated by the extension theory. We will investigate how to define the extended relational functions and how to refine the roughly designed model to meet the system requirement. During the refining process, both the fired and the neighborhood of the fired membership functions are adjusted simultaneously. Simulation results from single-input-single-output and double-input-single-output models verified that better results than the conventional methods have been obtained.

scholarly journals A Mixed Binary-Real NSGA II Algorithm Ensuring Both Accuracy and Interpretability of a Neuro-Fuzzy Controller

2017 ◽  
Vol 7 (5) ◽  
pp. 2614 ◽  
Author(s):  
Faouzi Titel ◽  
Khaled Belarbi
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Mimo System ◽  
Nsga Ii ◽  
Inference System ◽  
Single Input Single Output ◽  
Single Output ◽  
Neuro Fuzzy ◽  
Single Input ◽  
The Mean

<p>In this work, a Neuro-Fuzzy Controller network, called NFC that implements a Mamdani fuzzy inference system is proposed. This network includes neurons able to perform fundamental fuzzy operations. Connections between neurons are weighted through binary and real weights. Then a mixed binary-real Non dominated Sorting Genetic Algorithm II (NSGA II) is used to perform both accuracy and interpretability of the NFC by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpretability of fuzzy rules during the optimization process, some constraints are imposed. The  approach  is  tested  on  two  control examples:  a single  input  single  output (SISO) system  and  a  multivariable (MIMO) system.<strong></strong></p>

Comparative analysis of SISO and wavelength diversity-based FSO systems at different transmitter power levels

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Varun Srivastava ◽  
Abhilash Mandloi ◽  
Dhiraj Kumar Patel
Keyword(s):  
Optical Signal ◽  
Free Space Optical ◽  
Optical Source ◽  
Transmit Power ◽  
Single Input Single Output ◽  
Transmitter Power ◽  
Single Output ◽  
Communication Links ◽  
Single Input ◽  
Matching Performance

AbstractFree space optical (FSO) communication refers to a line of sight technology, which comprises optical source and detector to create a link without the use of physical connections. Similar to other wireless communication links, these are severely affected by losses that emerged due to atmospheric turbulence and lead to deteriorated intensity of the optical signal at the receiver. This impairment can be compensated easily by enhancing the transmitter power. However, increasing the transmitter power has some limitations as per radiation regulations. The requirement of high transmit power can be reduced by employing diversity methods. This paper presents, a wavelength-based diversity method with equal gain combining receiver, an effective technique to provide matching performance to single input single output at a comparatively low transmit power.

Sign in / Sign up

Export Citation Format

Share Document