A Fuzzy Radial Basis Adaptive Inference Network and Its Application to Time-Varying Signal Classification

A fuzzy radial basis adaptive inference network (FRBAIN) is proposed for multichannel time-varying signal fusion analysis and feature knowledge embedding. The model which combines the prior signal feature embedding mechanism of the radial basis kernel function with the rule-based logic inference ability of fuzzy system is composed of a multichannel time-varying signal input layer, a radial basis fuzzification layer, a rule layer, a regularization layer, and a T-S fuzzy classifier layer. The dynamic fuzzy clustering algorithm was used to divide the sample set pattern class into several subclasses with similar features. The fuzzy radial basis neurons (FRBNs) were defined and used as parameterized membership functions, and typical feature samples of each pattern subclass were used as kernel centers of the FRBN to realize the embedding of the diverse prior feature knowledge and the fuzzification of the input signals. According to the signal categories of FRBN kernel centers, nodes in the rule layer were selectively connected with nodes in the FRBN layer. A fuzzy multiplication operation was used to achieve synthesis of pattern class membership information and establishment of fuzzy inference rules. The excitation intensity of each rule was used as the input of T-S fuzzy classifier to classify the input signals. The FRBAIN can adaptively establish fuzzy set membership functions, fuzzy inference, and classification rules based on the learning of sample set, realize structural and data constraints of the model, and improve the modeling properties of imbalanced datasets. In this paper, the properties of FRBAIN were analyzed and a comprehensive learning algorithm was established. Experimental validation was performed with classification diagnoses from four complex cardiovascular diseases based on 12-lead ECG signals. Results demonstrated that, in the case of small-scale imbalanced datasets, the proposed method significantly improved both classification accuracy and generalizability comparing with other methods in the experiment.

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A Fuzzy Radial Basis Inference Network with Grouped Signal Feature Embedding and its Application in Multi-source Signals Classification

IEEE Access ◽

10.1109/access.2021.3089727 ◽

2021 ◽

pp. 1-1

Author(s):

Xianyu Zha ◽

Shaohua Xu ◽

Kun Liu ◽

Long Huang

Keyword(s):

Radial Basis ◽

Inference Network ◽

Source Signals

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Online functional prediction for spatio-temporal systems using time-varying Radial Basis Function networks

2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010) ◽

10.1109/car.2010.5456749 ◽

2010 ◽

Cited By ~ 2

Author(s):

J. Su ◽

T.J. Dodd

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Networks ◽

Time Varying ◽

Functional Prediction ◽

Radial Basis ◽

Spatio Temporal

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A new fuzzy classifier with triangular membership functions

Proceedings of International Conference on Neural Networks (ICNN'97) ◽

10.1109/icnn.1997.611715 ◽

2002 ◽

Cited By ~ 2

Author(s):

Yong-Sheng Yang ◽

F.H.Y. Chan ◽

F.K. Lam ◽

H. Nguyen

Keyword(s):

Membership Functions ◽

Fuzzy Classifier

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Radial basis function neural networks for adaptive on-line identification of rapidly time-varying nonlinear systems with optimal adaptation to new structures

IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.2002.1176369 ◽

2003 ◽

Author(s):

G. Apostolikas ◽

S. Tzafestas

Keyword(s):

Neural Networks ◽

Nonlinear Systems ◽

Radial Basis Function ◽

Basis Function ◽

Time Varying ◽

Line Identification ◽

Radial Basis ◽

On Line ◽

Optimal Adaptation

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ANFIS Model for Time Series Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.385-386.1411 ◽

2013 ◽

Vol 385-386 ◽

pp. 1411-1414 ◽

Cited By ~ 4

Author(s):

Xue Bo Jin ◽

Jiang Feng Wang ◽

Hui Yan Zhang ◽

Li Hong Cao

Keyword(s):

Time Series ◽

Fuzzy Inference System ◽

Prediction Error ◽

Fuzzy Inference ◽

Time Series Prediction ◽

Prediction Performance ◽

Membership Functions ◽

Adaptive Network ◽

Anfis Model ◽

Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

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High-Performance Adaptive Neurofuzzy Classifier with a Parametric Tuning

MATEC Web of Conferences ◽

10.1051/matecconf/201815501037 ◽

2018 ◽

Vol 155 ◽

pp. 01037

Author(s):

Sergey Gorbachev ◽

Vladimir Syryamkin

Keyword(s):

Decision Tree ◽

High Performance ◽

Fuzzy Inference ◽

Back Propagation ◽

Adaptive Algorithms ◽

Fuzzy Classifier ◽

Network Adaptation ◽

Error Back Propagation ◽

Multicriteria Problems ◽

Neuro Fuzzy

The article is devoted to research and development of adaptive algorithms for neuro-fuzzy inference when solving multicriteria problems connected with analysis of expert (foresight) data to identify technological breakthroughs and strategic perspectives of scientific, technological and innovative development. The article describes the optimized structuralfunctional scheme of the high-performance adaptive neuro-fuzzy classifier with a logical output, which has such specific features as a block of decision tree-based fuzzy rules and a hybrid algorithm for neural network adaptation of parameters based on the error back-propagation to the root of the decision tree.

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Real-Time Identification of Nonlinear Time-Varying Systems Using Radial Basis Function Network

Cybernetics and Systems Analysis ◽

10.1023/b:casa.0000020236.15477.4a ◽

2003 ◽

Vol 39 (6) ◽

pp. 927-934 ◽

Cited By ~ 6

Author(s):

O. G. Rudenko ◽

A. A. Bessonov

Keyword(s):

Radial Basis Function ◽

Real Time ◽

Basis Function ◽

Radial Basis Function Network ◽

Time Varying ◽

Radial Basis ◽

Time Varying Systems ◽

Real Time Identification ◽

Function Network

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Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

10.21203/rs.3.rs-41675/v2 ◽

2020 ◽

Author(s):

Adel Bakhshipour ◽

Hemad Zareiforoush

Keyword(s):

Fuzzy Logic ◽

Feature Selection ◽

Decision Trees ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Principal Component ◽

Image Features ◽

Fuzzy Classifier ◽

Inference System ◽

Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

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Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽

10.3390/pr6080103 ◽

2018 ◽

Vol 6 (8) ◽

pp. 103 ◽

Cited By ~ 12

Author(s):

Muhammad Fayaz ◽

Israr Ullah ◽

Do-Hyeun Kim

Keyword(s):

Fuzzy Logic ◽

Kalman Filter ◽

Fuzzy Inference ◽

Risk Index ◽

Significant Risk ◽

Assessment Method ◽

Logic Model ◽

Membership Functions ◽

Inference System ◽

Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

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