Tool Wear Length Estimation with a Self-Learning Fuzzy Inference Algorithm in Finish Milling

1999 ◽  
Vol 15 (8) ◽  
pp. 537-545 ◽  
Author(s):  
Lei Ming ◽  
Yang Xiaohong ◽  
Yang Shuzi
Keyword(s):  
Tool Wear ◽  
Fuzzy Inference ◽  
Inference Algorithm ◽  
Length Estimation ◽  
Self Learning

scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

scholarly journals Matrix Neuro-Fuzzy Self-Organizing Clustering Network

2011 ◽  
Vol 45 (1) ◽  
pp. 54-58 ◽  
Author(s):  
Yevgeniy Bodyanskiy ◽  
Valentyna Volkova ◽  
Mark Skuratov
Keyword(s):  
Fuzzy Inference ◽  
Data Sets ◽  
Fuzzy Membership Functions ◽  
Neuro Fuzzy ◽  
The Matrix ◽  
On Line ◽  
Winner Take All ◽  
Overlapping Classes ◽  
Self Learning ◽  
Self Organizing

Matrix Neuro-Fuzzy Self-Organizing Clustering NetworkIn this article the problem of clustering massive data sets, which are represented in the matrix form, is considered. The article represents the 2-D self-organizing Kohonen map and its self-learning algorithms based on the winner-take-all (WTA) and winner-take-more (WTM) rules with Gaussian and Epanechnikov functions as the fuzzy membership functions, and without the winner. The fuzzy inference for processing data with overlapping classes in a neural network is introduced. It allows one to estimate membership levels for every sample to every class. This network is the generalization of a vector neuro- and neuro-fuzzy Kohonen network and allows for data processing as they are fed in the on-line mode.

Machining Error Analysis and its Compensation Using Fuzzy Inference in Crankshaft Non-Circular Grinding

2010 ◽  
Vol 44-47 ◽  
pp. 1084-1089 ◽  
Author(s):  
Nan Yan Shen ◽  
Ming Lun Fang ◽  
Jing Li ◽  
Yong Yi He
Keyword(s):  
Control System ◽  
High Efficiency ◽  
Fuzzy Inference ◽  
Mathematic Model ◽  
Numerical Control ◽  
Learning Ability ◽  
Strong Nonlinearity ◽  
Numerical Control System ◽  
Machining Error ◽  
Self Learning

The error sources, including technological system, numerical control system and motion control model, generate a machining error in the radial direction of the crankpin which varies with the rotating angle of the crankshaft journal in crankpin non-circular grinding. This machining error can be reduced in advance through giving the additional impulses as the displacement correction of grinding carriage to numerical control system. However due to the strong nonlinearity of non-circular grinding system, the machining error of crankpin is difficult to be described precisely by a certain mathematic model. In this paper, a compensation method is proposed, which utilizes the measured error after the last grinding circle and the change of error to find the initial compensation value in the next grinding circle by fuzzy reasoning. To increase the self-learning ability of this method, the final compensation value in the next circle is composed of the initial value and the final value in the last circle. The grinding experiments results show that the roundness error can be reduced into the expectation in only a few grinding circles by this method, which demonstrates its high efficiency and applicability.

About Premise Reduction of Fuzzy Inference Algorithm

2009 ◽  
Vol 1 (3) ◽  
pp. 247-257
Author(s):  
Cheng-yi Zhang ◽  
Qi Niu ◽  
De-jun Peng ◽  
Juan Li
Keyword(s):  
Fuzzy Inference ◽  
Inference Algorithm

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.

The Newly AND-OR FNN Modeling and Application

2012 ◽  
Vol 433-440 ◽  
pp. 846-852
Author(s):  
Jiang Hua Sui ◽  
Qiang Ma
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Self Organization ◽  
Rule Base ◽  
The Novel ◽  
Fuzzy Neural ◽  
Ship Control ◽  
Self Learning ◽  
Better Than

The novel multilayer feed-forward AND-OR fuzzy neural network (AND-OR FNN) is proposed in this paper. The main feature is shown not only in reducing the input space by special inner structure of neurons, but also auto-extracting the rules by the structure self-organization and parameter self-learning. The equivalent is proved that the network structure and fuzzy inference. The whole structure of network is optimized by genetic algorithm to extract if-then rules. This designing approach is employed to modeling an AND-OR FNN controller for ship control. Simulated results demonstrate that the number of rule base is decreased remarkably and the performance is much better than ordinary fuzzy control, illustrate the approach is practicable, simple and effective.

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