Fuzzy-Neural Networks Based on Subtractive Clustering and its Applications in Soft Sensor

2012 ◽  
Vol 239-240 ◽  
pp. 1516-1521
Author(s):  
Yuan Yuan Sui
Keyword(s):  
Fuzzy Model ◽  
Fuzzy Neural Networks ◽  
Optimal Number ◽  
Soft Sensor ◽  
Fuzzy Rules ◽  
Cluster Center ◽  
Subtractive Clustering ◽  
Fuzzy Neural ◽  
Subtractive Cluster ◽  
Sensor Modeling

A soft sensor modeling method is presented in this paper，it selects optimal fuzzy rules by tuning the radius of a subtractive cluster center to generate a T-S fuzzy model. The radius of a cluster center is adjusted to select optimal number of fuzzy rules, to acquire a fuzzy model with perfect generalization capability. Then, the parameter is fine-tuned by means of a hybrid gradient descent (GD) and least-squares estimation (LSE) approach. Finally, the method is used to model a PDU Naphtha’s Dry Point, simulation results show that it can determine the optimal model quickly and achieve satisfactory prediction precision.

scholarly journals Optimization of parameters of neuro-fuzzy model

2020 ◽  
Vol 17 (3) ◽  
pp. 1206
Author(s):  
Lyalya Bakievna Khuzyatova ◽  
Lenar Ajratovich Galiullin
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support Systems ◽  
Fuzzy Neural Network ◽  
Support Systems ◽  
Fuzzy Model ◽  
Knowledge Bases ◽  
Fuzzy Neural Networks ◽  
Neural Network Models ◽  
Fuzzy Neural

<p>The questions and problems of the formation of knowledge bases of intelligent man-machine decision support systems are considered. The neuron-fuzzy model used in the work is described. The need for increasing the efficiency of the neuron-fuzzy model in the formation of knowledge bases is being updated. The task is to develop methods and algorithms for presetting and optimizing the parameters of a fuzzy neural network. To solve difficult formalized tasks, it is necessary to develop decision support systems - expert systems based on a knowledge base. ES developers are constantly faced with the problems of “extraction” and formalization of knowledge, as well as the search for new ways to obtain it. To do this, use the extraction, acquisition and formation of knowledge. Currently, the formation of knowledge bases is relevant for the creation of hybrid technologies - fuzzy neural networks that combine the advantages of neural network models and fuzzy systems. The analysis of the efficiency of the fuzzy neural network carried out in the work showed that the quality of training of the NN largely depends on the choice of the number of fuzzy granules for input drugs. In addition, to use fuzzy information formalized by the mathematical apparatus of fuzzy logic, procedures are required for selecting optimal forms and presetting the parameters of the corresponding membership functions (MF).</p>

Rough Set-Based Neuro-Fuzzy System

2011 ◽  
pp. 1396-1403
Author(s):  
Kai Keng Ang ◽  
Chai Quek
Keyword(s):  
Rough Set ◽  
Fuzzy System ◽  
Fuzzy Model ◽  
Fuzzy Rules ◽  
Membership Functions ◽  
Linguistic Terms ◽  
Neural Learning ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Modeling Data

Neuro-fuzzy hybridization is the oldest and most popular methodology in soft computing (Mitra & Hayashi, 2000). Neuro-fuzzy hybridization is known as Fuzzy Neural Networks, or Neuro-Fuzzy Systems (NFS) in the literature (Lin & Lee, 1996; Mitra & Hayashi, 2000). NFS is capable of abstracting a fuzzy model from given numerical examples using neural learning techniques to formulate accurate predictions on unseen samples. The fuzzy model incorporates the human-like style of fuzzy reasoning through a linguistic model that comprises of if-then fuzzy rules and linguistic terms described by membership functions. Hence, the main strength of NFS in modeling data is universal approximation (Tikk, Kóczy, & Gedeon, 2003) with the ability to solicit interpretable if-then fuzzy rules (Guillaume, 2001). However, modeling data using NFS involves the contradictory requirements of interpretability versus accuracy. Prevailingly, NFS that focused on accuracy employed optimization which resulted in membership functions that derailed from human-interpretable linguistic terms, or employed large number of if-then fuzzy rules on high-dimensional data that exceeded human level interpretation. This article presents a novel hybrid intelligent Rough set-based Neuro-Fuzzy System (RNFS). RNFS synergizes the sound concept of knowledge reduction from rough set theory with NFS. RNFS reinforces the strength of NFS by employing rough set-based techniques to perform attribute and rule reductions, thereby improving the interpretability without compromising the accuracy of the abstracted fuzzy model.

Sign in / Sign up

Export Citation Format

Share Document