Relation between Pareto-Optimal Fuzzy Rules and Pareto-Optimal Fuzzy Rule Sets

2007 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Isao Kuwajima ◽  
Yusuke Nojima
Keyword(s):  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Pareto Optimal ◽  
Rule Sets

Fault Diagnosis Simulation of a Space Propulsion System Based on Fuzzy Rule Set Neural Network Method

2015 ◽  
Vol 727-728 ◽  
pp. 876-879
Author(s):  
Min Chao Huang ◽  
Bao Yu Xing
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Rule ◽  
Fuzzy Neural Networks ◽  
Propulsion System ◽  
Fuzzy Rules ◽  
Space Propulsion ◽  
Logic Module ◽  
Rule Sets ◽  
Rule Set ◽  
Fuzzy Match

Based on fuzzy rule sets match method which is a series of fuzzy neural networks, a system framework used for the fault diagnosis is proposed. This fault diagnosis system consists of five parts, including the extraction of fuzzy rules, fuzzy reference rule sets, the fuzzy rule scheduled to detect, the fuzzy match module and the diagnosis logic module. The extraction of fuzzy rules involves two steps: step one adaptively divides the whole space of the trained data into the subspaces in the form of hypersphere, which is expected efficiently to work out the recognition questions in the high dimension space; step two generates a fuzzy rule in each sample subspace and calculates the membership degree of each fuzzy rule. Many fuzzy reference rule sets are produced by the extraction module of fuzzy rules for the offline learning, and a fuzzy rule set to be detected is online formed while the monitoring process is happening. Beliefs estimated from the fuzzy match process of fuzzy rule sets, which indicate the existence of the working classes in the plant, the diagnosis logic module can export fault detection time, fault isolation time, fault type and fault degree. The simulation researches of the fault diagnosis in space propulsion system demonstrate the superior qualities of the fault diagnosis method on the basis of the fuzzy match of the fuzzy rule sets.

Rule Extraction from Data

1999 ◽  
Vol 3 (5) ◽  
pp. 339-340
Author(s):  
Takeshi Furuhashi ◽  
Keyword(s):  
Neural Networks ◽  
Fuzzy Rule ◽  
Classification Problem ◽  
Fuzzy Modeling ◽  
Rule Extraction ◽  
Fuzzy Rules ◽  
High Dimensional ◽  
Special Issue ◽  
Rule Sets ◽  
Artificial Neural

Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This special issue entitled"Rule Extraction from Data" is aimed at providing the readers with good insights into the advanced studies in the field of rule extraction from data using neural networks/fuzzy rule sets. I invited seven research papers best suited for the theme of this special issue. All the papers were reviewed rigorously by two reviewers each. The first paper proposes an interesting rule extraction method from data using neural networks. Ishikawa presents a combination of learning with an immediate critic and a structural learning with forgetting. This method is capable of generating skeletal networks for logical rule extraction from data with correct and wrong answers. The proposed method is applied to rule extraction from lense data. The second paper presents a new methodology for logical rule extraction based on transformation of MLP (multilayered perceptron) to a logical network. Duck et al. applied their C-MLP2LN to the Iris benchmark classification problem as well as real-world medical data with very good results. In the third paper, Geczy and Usui propose fuzzy rule extraction from trained artificial neural networks. The proposed algorithm is implied from their theoretical study, not from heuristics. Their study enables to initially consider derivation of crisp rules from trained artificial neural network, and in case of conflict, application of fuzzy rules. The proposed algorithm is experimentally demonstrated with the Iris benchmark classification problem. The fourth paper presents a new framework for fuzzy modeling using genetic algorithm. The authors have broken new ground of fuzzy rule extraction from neural networks. For the fuzzy modeling, they have proposed a particular type of neural networks containing nodes representing membership functions. In this fourth paper, the authors discuss input variable selection for the fuzzy modeling under multiple criteria with different importance. A target system with a strong nonlinearity is used for demonstrating the proposed method. Kasabov, et al. present, in the fifth paper, a method for extraction of fuzzy rules that have different level of abstraction depending on several modifiable thresholds. Explanation quality becomes better with higher threshold values. They apply the proposed method to the Iris benchmark classification problem and to a real world problem. J. Yen and W. Gillespie address interpretability issue of Takagi-Sugeno-Kang model, one of the most popular fuzzy mdoels, in the fifth paper. They propose a new approach of fuzzy modeling that ensures not only a high approximation of the input-output relationship in the data, but also good insights about the local behavior of the model. The proposed method is applied to fuzzy modeling of sinc function and Mackey-Glass chaotic time series data. The last paper discusses fuzzy rule extraction from numerical data for high-dimensional classification problems. H.Ishibuchi, et al. have been pioneering methods for classification of data using fuzzy rules and genetic algorithm. In this last paper, they introduced a new criterion, simplicity of each rule, together with the conventional ones, compactness of rule base and classification ability, for high-dimensional problem. The Iris data is used for demonstrating their new classification method. They applied it also to wine data and credit data. I hope that the readers will be encouraged to explore the frontier to establish a new paradigm in the field of knowledge representation and rule extraction.

scholarly journals Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules

2015 ◽  
Vol 1 (1) ◽  
pp. 77-79
Author(s):  
C. Walther ◽  
A. Wenzel ◽  
M. Schneider ◽  
M. Trommer ◽  
K.-P. Sturm ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Embedded System ◽  
Vital Signs ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Classification Model ◽  
Multiobjective Evolutionary Algorithms ◽  
Automated Classification ◽  
Rule Sets

AbstractThe detection of stages of anaesthesia is mainly performed on evaluating the vital signs of the patient. In addition the frontal one-channel electroencephalogram can be evaluated to increase the correct detection of stages of anaesthesia. As a classification model fuzzy rules are used. These rules are able to classify the stages of anaesthesia automatically and were optimized by multiobjective evolutionary algorithms. As a result the performance of the generated population of fuzzy rule sets is presented. A concept of the construction of an autonomic embedded system is introduced. This system should use the generated rules to classify the stages of anaesthesia using the frontal one-channel electroencephalogram only.

Integrating membership functions and fuzzy rule sets from multiple knowledge sources

2000 ◽  
Vol 112 (1) ◽  
pp. 141-154 ◽  
Author(s):  
Ching-Hung Wang ◽  
Tzung-Pei Hong ◽  
Shian-Shyong Tseng
Keyword(s):  
Fuzzy Rule ◽  
Membership Functions ◽  
Knowledge Sources ◽  
Rule Sets

Corner Detection Using Fuzzy Principles

2013 ◽  
pp. 498-512
Author(s):  
Erik Cuevas ◽  
Daniel Zaldivar ◽  
Marco Perez-Cisneros
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Systems ◽  
Fuzzy Rule ◽  
Important Task ◽  
Fuzzy Rules ◽  
Corner Detection ◽  
Illumination Change ◽  
Benchmark Test ◽  
Rule Based ◽  
Rule Based System

Reliable corner detection is an important task in pattern recognition applications. In this chapter an approach based on fuzzy-rules to detect corners even under imprecise information is presented. The uncertainties arising due to various types of imaging defects such as blurring, illumination change, noise, et cetera. Fuzzy systems are well known for efficient handling of impreciseness. In order to handle the incompleteness arising due to imperfection of data, it is reasonable to model corner properties by a fuzzy rule-based system. The robustness of the proposed algorithm is compared with well known conventional detectors. The performance is tested on a number of benchmark test images to illustrate the efficiency of the algorithm in noise presence.

Support Vector Machine based Hybrid Classifiers and Rule Extraction thereof

2010 ◽  
pp. 404-426 ◽  
Author(s):  
M. A.H. Farquad ◽  
V. Ravi ◽  
Raju S. Bapi
Keyword(s):  
Radial Basis Function Network ◽  
Hybrid Approach ◽  
Fuzzy Rule ◽  
Rule Extraction ◽  
Bankruptcy Prediction ◽  
Fuzzy Rules ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Support Vectors ◽  
Hybrid Classifiers

Support vector machines (SVMs) have proved to be a good alternative compared to other machine learning techniques specifically for classification problems. However just like artificial neural networks (ANN), SVMs are also black box in nature because of its inability to explain the knowledge learnt in the process of training, which is very crucial in some applications like medical diagnosis, security and bankruptcy prediction etc. In this chapter a novel hybrid approach for fuzzy rule extraction based on SVM is proposed. This approach handles rule-extraction as a learning task, which proceeds in two major steps. In the first step the authors use labeled training patterns to build an SVM model, which in turn yields the support vectors. In the second step extracted support vectors are used as input patterns to fuzzy rule based systems (FRBS) to generate fuzzy “if-then” rules. To study the effectiveness and validity of the extracted fuzzy rules, the hybrid SVM+FRBS is compared with other classification techniques like decision tree (DT), radial basis function network (RBF) and adaptive network based fuzzy inference system. To illustrate the effectiveness of the hybrid developed, the authors applied it to solve a bank bankruptcy prediction problem. The dataset used pertain to Spanish, Turkish and US banks. The quality of the extracted fuzzy rules is evaluated in terms of fidelity, coverage and comprehensibility.

Optimal Design for Fuzzy Controller by Ant Colony Algorithm

2010 ◽  
Vol 439-440 ◽  
pp. 1190-1196 ◽  
Author(s):  
Bao Jiang Zhao
Keyword(s):  
Ant Colony Algorithm ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Real Time Control ◽  
Time Control ◽  
Information Updating ◽  
Fuzzy Logical

Fuzzy logical controller is one of the most important applications of fuzzy-rule-based system that models the human decision processing with a collection of fuzzy rules. In this paper, an adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of selection of the paths and the strategy of the trail information updating. The algorithm is used to design a fuzzy logical controller automatically for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due to multivariable inputs, state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. Experimental results show that the designed controller can control actual inverted pendulum successfully.

New classification technique: fuzzy oblique decision tree

2018 ◽  
Vol 41 (8) ◽  
pp. 2185-2195
Author(s):  
Yuliang Cai ◽  
Huaguang Zhang ◽  
Qiang He ◽  
Shaoxin Sun
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Main Idea ◽  
Fuzzy Rule ◽  
Decision Function ◽  
Fuzzy Rules ◽  
Leaf Node ◽  
Biomedical Data ◽  
Data Set ◽  
Afs Theory

Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold [Formula: see text] to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.

Combining Machine Learning and Fuzzy Rule-Based System in Automating Signal Timing Experts’ Decisions during Non-Recurrent Congestion

2020 ◽  
Vol 2674 (6) ◽  
pp. 163-176
Author(s):  
Mosammat Tahnin Tariq ◽  
Aidin Massahi ◽  
Rajib Saha ◽  
Mohammed Hadi
Keyword(s):  
Machine Learning ◽  
Recursive Partitioning ◽  
Fuzzy Rule ◽  
Traffic Signal ◽  
Fuzzy Rules ◽  
Signal Timing ◽  
Rule Based ◽  
Rule Based System ◽  
Machine Learning Model ◽  
History Of

Events such as surges in demand or lane blockages can create queue spillbacks even during off-peak periods, resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change signal timings in real time based on traffic signal engineers’ observations of incident and traffic conditions at the intersections upstream and downstream of the congested locations. Decisions to change the signal timing are governed by many factors, such as queue length, conditions of the main and side streets, potential of traffic spilling back to upstream intersections, the importance of upstream cross streets, and the potential of the queue backing up to a freeway ramp. This paper investigates and assesses automating the process of updating the signal timing plans during non-recurrent conditions by capturing the history of the responses of the traffic signal engineers to non-recurrent conditions and utilizing this experience to train a machine learning model. A combination of recursive partitioning and regression decision tree (RPART) and fuzzy rule-based system (FRBS) is utilized in this study to deal with the vagueness and uncertainty of human decisions. Comparing the decisions made based on the resulting fuzzy rules from applying the methodology with previously recorded expert decisions for a project case study indicates accurate recommendations for shifts in the green phases of traffic signals. The simulation results indicate that changing the green times based on the output of the fuzzy rules decreased delays caused by lane blockages or demand surge.

scholarly journals A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

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