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.

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 CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chuandong Song ◽  
Haifeng Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Classification Model ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Post Translational Modification ◽  
Fuzzy Neural ◽  
Human Complex ◽  
Better Than

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

Dynamic Controller Based on Ying Learning Algorithm for Medical Temperature System

2011 ◽  
Vol 187 ◽  
pp. 371-376
Author(s):  
Ping Zhang ◽  
Xiao Hong Hao ◽  
Heng Jie Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
The Novel ◽  
Fuzzy Neural ◽  
Learning Set ◽  
And Training ◽  
Novel Algorithm

In order to avoid the over fitting and training and solve the knowledge extraction problem in fuzzy neural networks system. Ying Learning Dynamic Fuzzy Neural Network (YL-DFNN) algorithm is proposed. The Learning Set based on K-VNN is constituted from message. Then the framework of is designed and its stability is proved. Finally, Simulation indicates that the novel algorithm is fast, compact, and capable in generalization.

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.

RULE EXTRACTION WITH FUZZY NEURAL NETWORK

1994 ◽  
Vol 05 (01) ◽  
pp. 1-11 ◽  
Author(s):  
FLORENCE D’ALCHÉ-BUC ◽  
VINCENT ANDRÈS ◽  
JEAN-PIERRE NADAL
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Inverted Pendulum ◽  
Decision Rules ◽  
Rule Base ◽  
Control Rules ◽  
Learning Procedure ◽  
New Learning ◽  
Fuzzy Neural ◽  
Derivatives Of

This paper deals with the learning of understandable decision rules with connectionist systems. Our approach consists of extracting fuzzy control rules with a new fuzzy neural network. Whereas many other works on this area propose to use combinations of nonlinear neurons to approximate fuzzy operations, we use a fuzzy neuron that computes max-min operations. Thus, this neuron can be interpreted as a possibility estimator, just as sigma-pi neurons can support a probabilistic interpretation. Within this context, possibilistic inferences can be drawn through the multi-layered network, using a distributed representation of the information. A new learning procedure has been developed in order that each part of the network can be learnt sequentially, while other parts are frozen. Each step of the procedure is based on the same kind of learning scheme: the backpropagation of a well-chosen cost function with appropriate derivatives of max-min function. An appealing result of the learning phase is the ability of the network to automatically reduce the number of the condition-parts of the rules, if needed. The network has been successfully tested on the learning of a control rule base for an inverted pendulum.

Fuzzy Neural Network Market Clearing Power Price Forecasting Based on K-Means Clustering

2011 ◽  
Vol 121-126 ◽  
pp. 2035-2039 ◽  
Author(s):  
Xian Min Wei
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Complex Structure ◽  
Price Forecasting ◽  
Market Clearing ◽  
Market Clearing Price ◽  
Fuzzy Neural ◽  
Long Time

Normalized fuzzy neural network has complex structure, long-time study and other shortcomings. For these shortcomings, this paper applies an improved fuzzy neural network to predict market clearing price. The model is simple, just by k-means clustering to determine the number of fuzzy inference layer nodes, and with strong applicability, higher prediction accuracy.

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