Data Doubling and Fuzzy Coding

2014 ◽  
pp. 281-296
Keyword(s):  
Fuzzy Coding

DYNAMICAL RECURRENT NEURAL NETWORKS — TOWARDS ENVIRONMENTAL TIME SERIES PREDICTION

1995 ◽  
Vol 06 (02) ◽  
pp. 145-170 ◽  
Author(s):  
ALEX AUSSEM ◽  
FIONN MURTAGH ◽  
MARC SARAZIN
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Prediction ◽  
Predictive Ability ◽  
Complex Problem ◽  
Internal Variables ◽  
Internal Dynamic ◽  
K Nearest Neighbors ◽  
Fuzzy Coding

Dynamical Recurrent Neural Networks (DRNN) (Aussem 1995a) are a class of fully recurrent networks obtained by modeling synapses as autoregressive filters. By virtue of their internal dynamic, these networks approximate the underlying law governing the time series by a system of nonlinear difference equations of internal variables. They therefore provide history-sensitive forecasts without having to be explicitly fed with external memory. The model is trained by a local and recursive error propagation algorithm called temporal-recurrent-backpropagation. The efficiency of the procedure benefits from the exponential decay of the gradient terms backpropagated through the adjoint network. We assess the predictive ability of the DRNN model with meteorological and astronomical time series recorded around the candidate observation sites for the future VLT telescope. The hope is that reliable environmental forecasts provided with the model will allow the modern telescopes to be preset, a few hours in advance, in the most suited instrumental mode. In this perspective, the model is first appraised on precipitation measurements with traditional nonlinear AR and ARMA techniques using feedforward networks. Then we tackle a complex problem, namely the prediction of astronomical seeing, known to be a very erratic time series. A fuzzy coding approach is used to reduce the complexity of the underlying laws governing the seeing. Then, a fuzzy correspondence analysis is carried out to explore the internal relationships in the data. Based on a carefully selected set of meteorological variables at the same time-point, a nonlinear multiple regression, termed nowcasting (Murtagh et al. 1993, 1995), is carried out on the fuzzily coded seeing records. The DRNN is shown to outperform the fuzzy k-nearest neighbors method.

scholarly journals Precise and Fuzzy Coding by Olfactory Sensory Neurons

2008 ◽  
Vol 28 (39) ◽  
pp. 9710-9722 ◽  
Author(s):  
D. J. Hoare ◽  
C. R. McCrohan ◽  
M. Cobb
Keyword(s):  
Sensory Neurons ◽  
Olfactory Sensory Neurons ◽  
Fuzzy Coding

scholarly journals Matching Golfers’ Movement Patterns during a Golf Swing

2018 ◽  
Vol 8 (12) ◽  
pp. 2452 ◽  
Author(s):  
Aimée Mears ◽  
Jonathan Roberts ◽  
Stephanie Forrester
Keyword(s):  
Data Analysis ◽  
Multiple Correspondence Analysis ◽  
Movement Patterns ◽  
Principal Component ◽  
Golf Swing ◽  
Fuzzy Coding ◽  
Linear Relationships ◽  
Ball Velocity ◽  
Impact Parameters ◽  
Dimension One

The golf swing is a multidimensional movement requiring alternative data analysis methods to interpret non-linear relationships in biomechanics data related to golf shot outcomes. The purpose of this study was to use a combined principal component analysis (PCA), fuzzy coding, and multiple correspondence analysis (MCA) data analysis approach to visualise associations within key biomechanics movement patterns and impact parameters in a group of low handicap golfers. Biomechanics data was captured and analysed for 22 golfers when hitting shots with their own driver. Relationships between biomechanics variables were firstly achieved by quantifying principal components, followed by fuzzy coding and finally MCA. Clubhead velocity and ball velocity were included as supplementary data in MCA. A total of 35.9% of inertia was explained by the first factor plane of MCA. Dimension one and two, and subsequent visualisation of MCA results, showed a separation of golfers’ biomechanics (i.e., swing techniques). The MCA plot can be used to simply and quickly identify movement patterns of a group of similar handicap golfers if supported with appropriate descriptive interpretation of the data. This technique also has the potential to highlight mismatched golfer biomechanics variables which could be contributing to weaker impact parameters.

Using fuzzy coding with qualitative data: example with subjective data in human-computer interaction

2019 ◽  
Vol 20 (4) ◽  
pp. 459-488
Author(s):  
Pierre Loslever ◽  
Taisa Guidini Gonçalves ◽  
Káthia Marçal de Oliveira ◽  
Christophe Kolski
Keyword(s):  
Human Computer Interaction ◽  
Qualitative Data ◽  
Subjective Data ◽  
Fuzzy Coding ◽  
Computer Interaction

scholarly journals A NEW VIEW OF FUZZY ORDERED SEMIGROUPS

2018 ◽  
Vol 1 (1) ◽  
pp. 9-17
Author(s):  
Hidayat Ullah Khan ◽  
Asghar Khan ◽  
Faiz Muhammad Khan ◽  
Amir Khan ◽  
Muhammad Taj
Keyword(s):  
Coding Theory ◽  
State Machines ◽  
Ordered Semigroup ◽  
Fuzzy Filter ◽  
Fuzzy Coding ◽  
Finite State ◽  
Fuzzy Subgroup ◽  
Characterization Theorems ◽  
Fuzzy Filters ◽  
Interval Valued

fuzzy coding theory, fuzzy finite state machines, and fuzzy languages. In this paper, we introduce the concept of an interval-valued -fuzzy filter of an ordered semigroup, where with. Since the concept of an interval-valued -fuzzy filter is an important and useful generalization of the ordinary interval-valued fuzzy filter, we discuss some fundamental aspects of an interval-valued -fuzzy filters. An interval-valued -fuzzy filter is a generalization of the existing concept of an interval-valued fuzzy filter. We discuss the concept of an interval-valued -fuzzy left (right)-filters and provide some characterization theorems. Finally, we extend the concept of an interval-valued fuzzy subgroup with thresholds to the concept of an interval-valued fuzzy left (right)-filter with thresholds of s.

A Bulb Model Implementing Fuzzy Coding of Odor Concentration

2009 ◽  
Author(s):  
Malin Sandström ◽  
Thomas Proschinger ◽  
Anders Lansner ◽  
Matteo Pardo ◽  
Giorgio Sberveglieri
Keyword(s):  
Odor Concentration ◽  
Fuzzy Coding
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