Optimization of fuzzy-neural structure through genetic algorithms and its application in artificial odor recognition-system

2003 ◽  
Author(s):  
B. Kusumoputro ◽  
P. Irwanto ◽  
W. Jatmiko
Keyword(s):  
Genetic Algorithms ◽  
Recognition System ◽  
Neural Structure ◽  
Odor Recognition ◽  
Fuzzy Neural

Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms

2005 ◽  
Vol 9 (3) ◽  
pp. 290-296 ◽  
Author(s):  
Benyamin Kusumoputro ◽  
◽  
Teguh P. Arsyad
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Multilayer Perceptron ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Odor Recognition ◽  
Fuzzy Neural ◽  
Odor Mixtures

Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.

Research on visual recognition intelligent system of city brand image based on fuzzy theory and regional culture

2021 ◽  
pp. 1-9
Author(s):  
Lin Han ◽  
Lu Han
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Intelligent System ◽  
Brand Image ◽  
Fuzzy Theory ◽  
Recognition System ◽  
Market Competitiveness ◽  
Visual Identity ◽  
Fuzzy Neural ◽  
Intelligent Recognition

With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better.

scholarly journals Decoupling Fuzzy-Neural PID Controller for TITO Nonlinear Systems

2016 ◽  
Vol 14 (2) ◽  
pp. 12-19 ◽  
Author(s):  
I. Ganchev ◽  
S. Ahmed ◽  
A. Taneva ◽  
M. Petrov
Keyword(s):  
Nonlinear System ◽  
Pid Controller ◽  
Fuzzy Pid ◽  
Neural Structure ◽  
Tuning Parameters ◽  
Fuzzy Pid Controller ◽  
Fuzzy Neural ◽  
Design Paradigm ◽  
Self Tuning ◽  
Set Up

AbstractThis paper presents a fuzzy-neural structure of a Decoupling Fuzzy PID controller with self-tuning parameters. This structure is appropriate for Two-Input-Two-Output (TITO) nonlinear system. The main advantage here is that the equation of classical PID control and decoupling coefficients are used as a Sugeno function into the fuzzy rules. Hence the designed decoupling fuzzy PID controller can be viewed as a natural similarity to the conventional one with decoupling elements. A benchmark quadruple tank, implementing a TITO nonlinear system is considered to illustrate the benefits of the design paradigm. The performance of this set up was studied for reference tracking and disturbance rejection cases. Simulation results confirm the effectiveness of the proposed solution.

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