Learning Algorithm for Fuzzy Perceptron with Max-Product Composition

2014 ◽  
Vol 687-691 ◽  
pp. 1359-1362
Author(s):  
Xin Yu ◽  
Mian Xie ◽  
Li Xia Tang ◽  
Chen Yu Li
Keyword(s):  
Neural Networks ◽  
Topological Structure ◽  
Fuzzy Systems ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Finite Convergence ◽  
Working Process ◽  
Logical Operations ◽  
Product Composition ◽  
Fuzzy Neural

Fuzzy neural networks is a powerful computational model, which integrates fuzzy systems with neural networks, and fuzzy perceptron is a kind of this neural networks. In this paper, a learning algorithm is proposed for a fuzzy perceptron with max-product composition, and the topological structure of this fuzzy perceptron is the same as conventional linear perceptrons. The inner operations involved in the working process of this fuzzy perceptron are based on the max-product logical operations rather than conventional multiplication and summation etc. To illustrate the finite convergence of proposed algorithm, some numerical experiments are provided.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Takagi Sugeno

A learning algorithm of fuzzy neural networks with triangular fuzzy weights

1995 ◽  
Vol 71 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Kitaek Kwon ◽  
Hideo Tanaka
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Fuzzy Weights

Optimization and Research of the Adaptive Wiener Filter Based on Fuzzy Neural Networks

2013 ◽  
Vol 411-414 ◽  
pp. 1660-1664
Author(s):  
Yan Jun Zhao ◽  
Li LIU
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Wiener Filter ◽  
Fuzzy Neural Networks ◽  
Central Value ◽  
Fuzzy Neural ◽  
Training Error ◽  
Neural Network Technology

This paper introduces fuzzy neural network technology into the adaptive filter and makes further research on its structure and algorithms. At first, fuzzy rules are determined and the network structure is built by means of dividing fuzzy subspaces. Secondly, membership functions are chosen layers are defined and the network is trained by adaptive learning algorithm. Thirdly, training error is the minimum with repeating debugging. Finally, linking weight, the central value and width of the network membership function is adjusted by using experience of experts. The optimal performance of Adaptive Wiener Filter is realized based on Fuzzy Neural Networks.

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.

scholarly journals Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems

2018 ◽  
pp. 114-133
Author(s):  
Paulo Vitor de Campos Souza
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Compact Set ◽  
Regularization Theory ◽  
Promising Alternative ◽  
Fuzzy Neural ◽  
Regression Problems ◽  
Learning Machine

This paper presents a novel learning algorithm for fuzzy logic neuron based on neural networks and fuzzy systems able to generate accurate and transparent models. The learning algorithm is based on ideas from Extreme Learning Machine [36], to achieve a low time complexity, and regularization theory, resulting in sparse and accurate models. A compact set of incomplete fuzzy rules can be extracted from the resulting network topology. Experiments considering regression problems are detailed. Results suggest the proposed approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability.

Neuro-Fuzzy Systems Approaches

1999 ◽  
Vol 3 (3) ◽  
pp. 177-185 ◽  
Author(s):  
Danuta Rutkowska ◽  
◽  
Yoichi Hayashi ◽  
Keyword(s):  
Neural Networks ◽  
Fuzzy Systems ◽  
Transfer Functions ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Fuzzy Neural Networks ◽  
Nonparametric Approach ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Fuzzy Weights

Two major approaches to neuro-fuzzy systems are distinguished in the paper. The previous one refers to fuzzy neural networks, which are neural networks with fuzzy signals, and/or fuzzy weights, as well as fuzzy transfer functions. The latter approach concerns neuro-fuzzy systems in the form of multilayer feed-forward networks, which differ from standard neural networks, because elements of particular layers conduct different operations than standard neurons. These structures are neural network representations of fuzzy systems and they are also called connectionist models of fuzzy systems, adaptive fuzzy systems, fuzzy inference neural networks, etc. Two different defuzzifiers, applied to fuzzy systems, are in focus of the paper. Center-of-sums method is an example of parametric defuzzification. Standard neural networks a defuzzifier presents nonparametric approach to defuzzification. For both cases learning algorithms of neuro-fuzzy systems are proposed. These algorithms take a form of recursions derived based on the momentum back-propagation method. Computer simulation demonstrates a comparison between performance of neuro-fuzzy systems with the parametric and nonparametric defuzzifier. Truck backer-upper control problem has been used to illustrate the systems performance. Conclusions concerning the simulation results are summarized. The paper pertains many references on neuro-fuzzy systems, especially selected publications of Czogala, whom it is dedicated.

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