scholarly journals Fuzzy Neural Models Based on Some New Fuzzy* Arithmetic Operations

1999 ◽  
Vol 3 (4) ◽  
pp. 245-254
Author(s):  
Petr Musilekl ◽  
◽  
Madan M. Gupta
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Theoretical Description ◽  
Fuzzy Arithmetic ◽  
Neural Processing ◽  
Neural Models ◽  
Arithmetic Operations ◽  
Neural Structure ◽  
Novel Approach ◽  
Fuzzy Neural

This paper introduces a novel approach to fuzzy arithmetic computation in fuzzy neural networks. The first part provides an overview of the standard fuzzy arithmetic operations and limitations of their use in fuzzy arithmetic based neural models. Consequently, alternative fuzzy arithmetic operations are developed and their aspects for the neural models are discussed in more detail. Originality of our approach lies in the treatment of neural inputs and weights as interactive variables which allows control of uncertainty growth in neural processing. Besides the detailed theoretical description of these operations, corresponding implementation algorithms are given as well. Combination of the alternative fuzzy arithmetic operations is briefly shown on two particular fuzzy arithmetic neurons providing fuzzy extensions of common crisp neural models. Finally, an example of a simple fuzzy neural structure for pattern classification is given.

scholarly journals A Novel Approach for Early detection of Alzheimer's disease Based on Multi Level Fuzzy Neural Networks

2021 ◽  
Author(s):  
Hamid Akramifard ◽  
MohammadAli Balafar ◽  
SeyedNaser Razavi ◽  
Abd Rahman Ramli
Keyword(s):  
Neural Networks ◽  
Early Detection ◽  
Real Life ◽  
Fuzzy Neural Networks ◽  
Healthy People ◽  
Novel Approach ◽  
Optimum Algorithm ◽  
Single Feature ◽  
Fuzzy Neural ◽  
Multi Level

Timely diagnosis of Alzheimer's diseases(AD) is crucial to obtain more practical treatments. In this paper, a novel approach Based on Multi-Level Fuzzy Neural Networks (MLFNN) for early detection of AD is proposed. The focus of study was on the problem of diagnosing AD and MCI patients from healthy people using MLFNN and selecting the best feature(s) and most compatible classification algorithm. In this way, we achieve an excellent performance using only a single feature i.e. MMSE score, by fitting the optimum algorithm to the best area using optimum possible feature(s) namely one feature for a real life problem. It can be said, the proposed method is a discovery that help patients and healthy people get rid of painful and time consuming experiments. Experiments shows the effectiveness of proposed method in current research for diagnosis of AD with one of the highest performance (accuracy rates of 96.6%), ever reported in the literature.

scholarly journals Genetic Algorithm for Fuzzy Neural Networks using Locally Crossover

2011 ◽  
Vol 6 (1) ◽  
pp. 8 ◽  
Author(s):  
Dragos Arotaritei
Keyword(s):  
Neural Networks ◽  
Fuzzy Numbers ◽  
Signal Propagation ◽  
Fuzzy Neural Networks ◽  
Fuzzy Arithmetic ◽  
Recurrent Networks ◽  
Triangular Fuzzy Numbers ◽  
Fuzzy Neural ◽  
Sinusoidal Oscillations ◽  
Real World Problems

Fuzzy feed-forward (FFNR) and fuzzy recurrent networks (FRNN) proved to be solutions for "real world problems". In the most cases, the learning algorithms are based on gradient techniques adapted for fuzzy logic with heuristic rules in the case of fuzzy numbers. In this paper we propose a learning mechanism based on genetic algorithms (GA) with locally crossover that can be applied to various topologies of fuzzy neural networks with fuzzy numbers. The mechanism is applied to FFNR and FRNN with L-R fuzzy numbers as inputs, outputs and weights and fuzzy arithmetic as forward signal propagation. The α-cuts and fuzzy biases are also taken into account. The effectiveness of the proposed method is proven in two applications: the mapping a vector of triangular fuzzy numbers into another vector of triangular fuzzy numbers for FFNR and the dynamic capture of fuzzy sinusoidal oscillations for FRNN.

A novel approach to short-term load forecasting using fuzzy neural networks

1998 ◽  
Vol 13 (2) ◽  
pp. 480-492 ◽  
Author(s):  
S.E. Papadakis ◽  
J.B. Theocharis ◽  
S.J. Kiartzis ◽  
A.G. Bakirtzis
Keyword(s):  
Neural Networks ◽  
Load Forecasting ◽  
Fuzzy Neural Networks ◽  
Short Term ◽  
Novel Approach ◽  
Short Term Load Forecasting ◽  
Fuzzy Neural

scholarly journals Stability Conditions for Fuzzy Neural Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-4
Author(s):  
Choon Ki Ahn
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Fuzzy Neural Networks ◽  
External Input ◽  
Linear Matrix ◽  
Stability Conditions ◽  
Matrix Inequality ◽  
Novel Approach ◽  
Fuzzy Neural ◽  
The Stability

This paper presents a novel approach to assess the stability of fuzzy neural networks. First, we propose a new condition for the stability of fuzzy neural networks. Second, a new stability condition based on linear matrix inequality (LMI) is presented for fuzzy neural networks. These conditions also ensure asymptotic stability without external input.

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.

Hotbox level detection of railway vehicle using fuzzy neural networks

2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Railway Vehicle ◽  
Fuzzy Neural

scholarly journals Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Dongyeop Kang ◽  
Sangmoon Lee
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Memory Control
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