An Adaptive Fuzzy Neural Network Based on Self-Organizing Map (SOM)
نویسندگان
چکیده
This chapter shows a new method of fuzzy network which can change the structure by the systems. This method is based on the self-organizing mapping (SOM) (Kohonen T. 1982), but this algorithm resolves the problem of the SOM which can’t change the number of the network nodes. Then, this new algorithm can change the number of fuzzy rules; it takes the experienced rules out of the necessary side for the number of the fuzzy rules. We use this new algorithm to control the dissolved oxygenic in the wastewater treatment processes. This proposed algorithm can adjust subjection function on-line, optimize control rules. The results of simulations show that the controller can take the dissolved oxygenic to achieve the presumed request, and prove the superiority of this proposed algorithm in the practical applications. The research of the structure of the Neural Network is a hotspot currently. A neural network model with strong relations to the area of fuzzy systems is the fuzzy neural network model (T.Poggio and F.Girosi, 1990). Based on the IF-THEN rules, the fuzzy logic rules can be clustered. The functional equivalence of restricted fuzzy neural networks has been shown as Fig1:
منابع مشابه
Landforms identification using neural network-self organizing map and SRTM data
During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...
متن کاملThe Time Adaptive Self Organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملGait Based Vertical Ground Reaction Force Analysis for Parkinson’s Disease Diagnosis Using Self Organizing Map
The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson’s disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysi...
متن کاملColour image segmentation using the self-organizing map and adaptive resonance theory
We propose a new competitive-learning neural network model for colour image segmentation. The model, which is based on the adaptive resonance theory (ART) of Carpenter and Grossberg and on the self-organizing map (SOM) of Kohonen, overcomes the limitations of (i) the stability–plasticity trade-offs in neural architectures that employ ART; and (ii) the lack of on-line learning property in the SO...
متن کاملA Modfied Self-organizing Map Neural Network to Recognize Multi-font Printed Persian Numerals (RESEARCH NOTE)
This paper proposes a new method to distinguish the printed digits, regardless of font and size, using neural networks.Unlike our proposed method, existing neural network based techniques are only able to recognize the trained fonts. These methods need a large database containing digits in various fonts. New fonts are often introduced to the public, which may not be truly recognized by the Opti...
متن کامل