Fast global optimization fuzzy neural network and its application in data fusion

1998 ◽  
Author(s):  
Xinxing Yang ◽  
Licheng Jiao
Keyword(s):  
Neural Network ◽  
Global Optimization ◽  
Data Fusion ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural

Study on Fuzzy Data Fusion for Real-Time Intelligent Recognition of Tool Wear State

2008 ◽  
Vol 375-376 ◽  
pp. 626-630
Author(s):  
Bang Yan Ye ◽  
Jian Ping Liu ◽  
Rui Tao Peng ◽  
Yong Tang ◽  
Xue Zhi Zhao
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Data Fusion ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Experimental Result ◽  
Fuzzy Data ◽  
Sample Classification ◽  
Fuzzy Neural ◽  
To Come

For detecting gradual tool wear state on line, the methods of Wavelet Fuzzy Neural Network, Regression Neural Network and Sample Classification Fuzzy Neural Network by detecting cutting force, motor power of machine tool and AE signal respectively are presented. Although these methods are not difficult to come true and processed accurately and rapidly, it is difficult to obtain comprehensive information of machining and exact value of tool wear when using single method of intelligent modeling and single signal detecting. For this purpose, fuzzy inference technique is adopted to fuse the recognized data. Emulation experiment is carried out by using Matlab software platform and this method is verified to be feasible. Experimental result indicates that by applying fuzzy data fusion, we can get an exact tool wear forecast rapidly.

Condenser Fault Diagnosis Based on FNN and Data Fusion

2010 ◽  
Vol 44-47 ◽  
pp. 3762-3766 ◽  
Author(s):  
Fei Xia ◽  
Hao Zhang ◽  
Dao Gang Peng ◽  
Hui Li ◽  
Yi Kang Su
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Fuzzy Neural Network ◽  
Membership Functions ◽  
Fusion Technology ◽  
New Approach ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
General Method

In order to improve the fault diagnosis result of the condenser, one new approach based on the fuzzy neural network and data fusion is proposed in this paper. Firstly, the data from the various sensors can be processed through the specific membership functions. With the fault symptoms and fault types of condenser, the fuzzy neural network is constructed for the primary fault diagnosis. Some likelihood of the neural network outputs is too close to make the correct decision of fault diagnosis. The problem can be solved by the data fusion technology. This method was successfully adopted in the application of condenser fault diagnosis. Compared with the general method of FNN, this approach can enhance the accuracy in the domain of fault diagnosis, especially for reducing the uncertainty in the fault diagnosis.

An Intelligent Data Mining System Through Integration Of Electromagnetism-Like Mechanism And Fuzzy Neural Network

2010 ◽  
pp. 61-84
Author(s):  
Peitsang Wu ◽  
Yung-Yao Hung
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Global Optimization ◽  
Fuzzy Neural Network ◽  
Local Optimum ◽  
Sample Point ◽  
Mining System ◽  
The Em Algorithm ◽  
Fuzzy Neural ◽  
Data Mining System

In this chapter, a meta-heuristic algorithm (Electromagnetism-like Mechanism, EM) for global optimization is introduced. The Electromagnetism-like mechanism simulates the electromagnetism theory of physics by considering each sample point to be an electrical charge. The EM algorithm utilizes an attraction-repulsion mechanism to move the sample points towards the optimum. The electromagnetism-like mechanism (EM) can be used as a stand-alone approach or as an accompanying algorithm for other methods. Besides, the electromagnetism-like mechanism is not easily trapped into local optimum. Therefore, the purpose of this chapter is using the electromagnetism-like mechanism (EM) to develop an electromagnetism-like mechanism based fuzzy neural network (EMFNN), and employ the EMFNN to train fuzzy if-then rules.

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