Generation of Fuzzy Neural Network Structure

How fuzzy technology and neural networks and genetic algorithm combine with each other has become the focus of research. A fuzzy neural network controller was proposed based on defuzzification and optimization around the fuzzy neural network structure. Genetic algorithm of fuzzy neural network was brought forward based on optimal control theory. Optimal structure and parameters of fuzzy neural network controller were Offline searched by way of controller performance indicators of genetic algorithm. Fuzzy neural network controller through genetic algorithm was accessed in fuzzy neural network intelligent control system.

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Research on Prediction of Excavation Deformation Based on Fuzzy Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.71-78.3992 ◽

2011 ◽

Vol 71-78 ◽

pp. 3992-3995

Author(s):

Xiang Chen ◽

Xue Feng Zhou

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Fuzzy Theory ◽

Linear Structure ◽

Displacement Prediction ◽

Deformation Control ◽

Fuzzy Neural ◽

Neural Network Structure ◽

Set Up ◽

Neural Network Technology

Seeing that there are a lot of uncertain and fuzzy factors in the deformation control system of excavation, in this paper, the author combined the fuzzy theory with neural network technology, adopted the ordinary non-linear structure as the fuzzy neural network of neural network structure formed of neuron directly, and it is under the degree fuzzy number which inputs and export the information to import the corresponding network. Thus according to fuzzy neural network, displacement prediction model has been set up.

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Faster Self-Organizing Fuzzy Neural Network Training and Improved Autonomy with Time-Delayed Synapses for Locally Recurrent Learning

System and Circuit Design for Biologically-Inspired Intelligent Learning ◽

10.4018/978-1-60960-018-1.ch008 ◽

2011 ◽

pp. 156-183 ◽

Cited By ~ 1

Author(s):

Damien Coyle ◽

Girijesh Prasad ◽

Martin McGinnity

Keyword(s):

Neural Network ◽

Network Structure ◽

Computational Efficiency ◽

Fuzzy Neural Network ◽

Learning Algorithm ◽

Feedback Connection ◽

Structure Changes ◽

Fuzzy Neural ◽

Output Behavior ◽

Self Organizing

This chapter describes a number of modifications to the learning algorithm and architecture of the self-organizing fuzzy neural network (SOFNN) to improve its computational efficiency and learning ability. To improve the SOFNN’s computational efficiency, a new method of checking the network structure after it has been modified is proposed. Instead of testing the entire structure every time it has been modified, a record is kept of each neuron’s firing strength for all data previously clustered by the network. This record is updated as training progresses and is used to reduce the computational load of checking network structure changes, to ensure performance degradation does not occur, resulting in significantly reduced training times. It is shown that the modified SOFNN compares favorably to other evolving fuzzy systems in terms of accuracy and structural complexity. In addition, a new architecture of the SOFNN is proposed where recurrent feedback connections are added to neurons in layer three of the structure. Recurrent connections allow the network to learn the temporal information from data and, in contrast to pure feed forward architectures which exhibit static input-output behavior in advance, recurrent models are able to store information from the past (e.g., past measurements of the time-series) and are therefore better suited to analyzing dynamic systems. Each recurrent feedback connection includes a weight which must be learned. In this work a learning approach is proposed where the recurrent feedback weight is updated online (not iteratively) and proportional to the aggregate firing activity of each fuzzy neuron. It is shown that this modification can significantly improve the performance of the SOFNN’s prediction capacity under certain constraints.

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A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

Matériaux & Techniques ◽

10.1051/mattech/2019002 ◽

2018 ◽

Vol 106 (6) ◽

pp. 603 ◽

Cited By ~ 2

Author(s):

Bendaoud Mebarek ◽

Mourad Keddam

Keyword(s):

Neural Network ◽

Experimental Data ◽

Fuzzy Neural Network ◽

Treatment Time ◽

Calculation Model ◽

Average Error ◽

Network Approach ◽

Neural Network Approach ◽

Fuzzy Neural ◽

Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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