Advanced inference in fuzzy systems by rule base compression
Gegov, Alexander; Gobalakrishnan, N.
2007-01-01
This paper describes a method for rule base compression of fuzzy systems. The method compresses a fuzzy system with an arbitrarily large number of rules into a smaller fuzzy system by removing the redundancy in the fuzzy rule base. As a result of this compression, the number of on-line operations during the fuzzy inference process is significantly reduced without compromising the solution. This rule base compression method outperforms significantly other known methods for fuzzy rule base redu...
Optical Generation of Fuzzy-Based Rules
Gur, Eran; Mendlovic, David; Zalevsky, Zeev
2002-08-01
In the last third of the 20th century, fuzzy logic has risen from a mathematical concept to an applicable approach in soft computing. Today, fuzzy logic is used in control systems for various applications, such as washing machines, train-brake systems, automobile automatic gear, and so forth. The approach of optical implementation of fuzzy inferencing was given by the authors in previous papers, giving an extra emphasis to applications with two dominant inputs. In this paper the authors introduce a real-time optical rule generator for the dual-input fuzzy-inference engine. The paper briefly goes over the dual-input optical implementation of fuzzy-logic inferencing. Then, the concept of constructing a set of rules from given data is discussed. Next, the authors show ways to implement this procedure optically. The discussion is accompanied by an example that illustrates the transformation from raw data into fuzzy set rules.
A Fuzzy Rule-based Controller For Automotive Vehicle Guidance
Hessburg, Thomas; Tomizuka, Masayoshi
1991-01-01
A fuzzy rule-based controller is applied to lateral guidance of a vehicle for an automated highway system. The fuzzy rules, based on human drivers' experiences, are developed to track the center of a lane in the presence of external disturbances and over a range of vehicle operating conditions.
Gain ratio based fuzzy weighted association rule mining classifier for ...
Indian Academy of Sciences (India)
2: 271–277. Chen C-H, Tseng V S and Hong T-P 2008 Cluster-based evaluation in fuzzy-genetic data mining. IEEE. Trans. Fuzzy Syst. 16(1): 249 del Jesus M J, González P, Herrera F and Mesonero M 2007 Evolutionary fuzzy rule induction process for subgroup discovery: A case study in marketing. IEEE Trans. Fuzzy Syst.
Protein superfamily classification using fuzzy rule-based classifier.
Mansoori, Eghbal G; Zolghadri, Mansoor J; Katebi, Seraj D
2009-03-01
In this paper, we have proposed a fuzzy rule-based classifier for assigning amino acid sequences into different superfamilies of proteins. While the most popular methods for protein classification rely on sequence alignment, our approach is alignment-free and so more human readable. It accounts for the distribution of contiguous patterns of n amino acids ( n-grams) in the sequences as features, alike other alignment-independent methods. Our approach, first extracts a plenty of features from a set of training sequences, then selects only some best of them, using a proposed feature ranking method. Thereafter, using these features, a novel steady-state genetic algorithm for extracting fuzzy classification rules from data is used to generate a compact set of interpretable fuzzy rules. The generated rules are simple and human understandable. So, the biologists can utilize them, for classification purposes, or incorporate their expertise to interpret or even modify them. To evaluate the performance of our fuzzy rule-based classifier, we have compared it with the conventional nonfuzzy C4.5 algorithm, beside some other fuzzy classifiers. This comparative study is conducted through classifying the protein sequences of five superfamily classes, downloaded from a public domain database. The obtained results show that the generated fuzzy rules are more interpretable, with acceptable improvement in the classification accuracy.
Designing Fuzzy Rule Based Expert System for Cyber Security
Goztepe, Kerim
2016-01-01
The state of cyber security has begun to attract more attention and interest outside the community of computer security experts. Cyber security is not a single problem, but rather a group of highly different problems involving different sets of threats. Fuzzy Rule based system for cyber security is a system consists of a rule depository and a mechanism for accessing and running the rules. The depository is usually constructed with a collection of related rule sets. The aim of this study is to...
Gain ratio based fuzzy weighted association rule mining classifier for ...
Indian Academy of Sciences (India)
and 'chest pain=high', 'a risk level of heart disease =medium' is a fuzzy quantitative associa- tion rule ... to assign different support value at each level of abstraction to produce a large number of rules generated as a .... gain ratio based ranking is used as a user defined weight value for each potential attribute as shown in ...
Graph Cuts based Image Segmentation using Fuzzy Rule Based System
Directory of Open Access Journals (Sweden)
M. R. Khokher
2012-12-01
Full Text Available This work deals with the segmentation of gray scale, color and texture images using graph cuts. From input image, a graph is constructed using intensity, color and texture profiles of the image simultaneously. Based on the nature of image, a fuzzy rule based system is designed to find the weight that should be given to a specific image feature during graph development. The graph obtained from the fuzzy rule based weighted average of different image features is further used in normalized graph cuts framework. Graph is iteratively bi-partitioned through the normalized graph cuts algorithm to get optimum partitions resulting in the segmented image. Berkeley segmentation database is used to test our algorithm and the segmentation results are evaluated through probabilistic rand index, global consistency error, sensitivity, positive predictive value and Dice similarity coefficient. It is shown that the presented segmentation method provides effective results for most types of images.
Generalized regression for fuzzy rule bases using the Hough transform
Barone, Joseph M.; Fileu, Dimitar P.
1993-12-01
The extended Hough transform permits weight functions of arbitrary type and complexity to help guide the choice of a `regression' line in polar coordinate space. This paper suggests that this transform may be helpful in locating the best linear approximation to gaps and areas of conflict in fuzzy rule bases. Using the sliding mode approximation of a fuzzy controller as an example, this paper shows how global properties of the rule base can be used to help guide the search for good approximations. The notion of `representativeness' of centroids and its effect on regression via the Hough transform is also considered. Finally, a different approach based on OWA operators is discussed briefly.
Building an Associative Classifier Based on Fuzzy Association Rules
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Zuoliang Chen
2008-08-01
Full Text Available Classification based on association rules is considered to be effective and advantageous in many cases. However, there is a so-called qsharp boundaryq problem in association rules mining with quantitative attribute domains. This paper aims at proposing an associative classification approach, namely Classification with Fuzzy Association Rules (CFAR, where fuzzy logic is used in partitioning the domains. In doing so, the notions of support and confidence are extended, along with the notion of compact set in dealing with rule redundancy and conflict. Furthermore, the corresponding mining algorithm is introduced and tested on benchmarking datasets. The experimental results revealed that CFAR generated better understandability in terms of fewer rules and smother boundaries than the traditional CBA approach while maintaining satisfactory accuracy.
Horizontal and Vertical Rule Bases Method in Fuzzy Controllers
Directory of Open Access Journals (Sweden)
Sadegh Aminifar
2013-01-01
Full Text Available Concept of horizontal and vertical rule bases is introduced. Using this method enables the designers to look for main behaviors of system and describes them with greater approximations. The rules which describe the system in first stage are called horizontal rule base. In the second stage, the designer modulates the obtained surface by describing needed changes on first surface for handling real behaviors of system. The rules used in the second stage are called vertical rule base. Horizontal and vertical rule bases method has a great roll in easing of extracting the optimum control surface by using too lesser rules than traditional fuzzy systems. This research involves with control of a system with high nonlinearity and in difficulty to model it with classical methods. As a case study for testing proposed method in real condition, the designed controller is applied to steaming room with uncertain data and variable parameters. A comparison between PID and traditional fuzzy counterpart and our proposed system shows that our proposed system outperforms PID and traditional fuzzy systems in point of view of number of valve switching and better surface following. The evaluations have done both with model simulation and DSP implementation.
Uncertain rule-based fuzzy systems introduction and new directions
Mendel, Jerry M
2017-01-01
The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material. Presents fully updated material on new breakthroughs in human-inspired rule-based techniques for handling real-world uncertainties; Allows those already familiar with type-1 fuzzy se...
Gain ratio based fuzzy weighted association rule mining classifier for ...
Indian Academy of Sciences (India)
The health care environment still needs knowledge based discovery for handling wealth of data. Extraction of the potential causes of the diseases is the most important factor for medical data mining. Fuzzy association rule mining is wellperformed better than traditional classifiers but it suffers from the exponential growth of ...
Rule-based fuzzy classifier for spinal deformities.
Birtane, Sibel; Korkmaz, Hayriye
2014-01-01
In this paper, 2-steps software using image processing and enhancement technologies is developed to obtain a scoliosis patient's spine pattern from 2D coronal X-Ray images without manual land marking. Then, a Rule-based Fuzzy classifier is implemented on those images to classify the spine patterns using the King-Moe classification approach.
Rule based fuzzy logic approach for classification of fibromyalgia syndrome.
Arslan, Evren; Yildiz, Sedat; Albayrak, Yalcin; Koklukaya, Etem
2016-06-01
Fibromyalgia syndrome (FMS) is a chronic muscle and skeletal system disease observed generally in women, manifesting itself with a widespread pain and impairing the individual's quality of life. FMS diagnosis is made based on the American College of Rheumatology (ACR) criteria. However, recently the employability and sufficiency of ACR criteria are under debate. In this context, several evaluation methods, including clinical evaluation methods were proposed by researchers. Accordingly, ACR had to update their criteria announced back in 1990, 2010 and 2011. Proposed rule based fuzzy logic method aims to evaluate FMS at a different angle as well. This method contains a rule base derived from the 1990 ACR criteria and the individual experiences of specialists. The study was conducted using the data collected from 60 inpatient and 30 healthy volunteers. Several tests and physical examination were administered to the participants. The fuzzy logic rule base was structured using the parameters of tender point count, chronic widespread pain period, pain severity, fatigue severity and sleep disturbance level, which were deemed important in FMS diagnosis. It has been observed that generally fuzzy predictor was 95.56 % consistent with at least of the specialists, who are not a creator of the fuzzy rule base. Thus, in diagnosis classification where the severity of FMS was classified as well, consistent findings were obtained from the comparison of interpretations and experiences of specialists and the fuzzy logic approach. The study proposes a rule base, which could eliminate the shortcomings of 1990 ACR criteria during the FMS evaluation process. Furthermore, the proposed method presents a classification on the severity of the disease, which was not available with the ACR criteria. The study was not limited to only disease classification but at the same time the probability of occurrence and severity was classified. In addition, those who were not suffering from FMS were
A hierarchical fuzzy rule-based approach to aphasia diagnosis.
Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid
2007-10-01
Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.
Fuzzy Rules for Ant Based Clustering Algorithm
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Amira Hamdi
2016-01-01
Full Text Available This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS algorithm with the fuzzy c-means (FCM clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K-means, K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.
Fuzzy-rule-based image reconstruction for positron emission tomography
Mondal, Partha P.; Rajan, K.
2005-09-01
Positron emission tomography (PET) and single-photon emission computed tomography have revolutionized the field of medicine and biology. Penalized iterative algorithms based on maximum a posteriori (MAP) estimation eliminate noisy artifacts by utilizing available prior information in the reconstruction process but often result in a blurring effect. MAP-based algorithms fail to determine the density class in the reconstructed image and hence penalize the pixels irrespective of the density class. Reconstruction with better edge information is often difficult because prior knowledge is not taken into account. The recently introduced median-root-prior (MRP)-based algorithm preserves the edges, but a steplike streaking effect is observed in the reconstructed image, which is undesirable. A fuzzy approach is proposed for modeling the nature of interpixel interaction in order to build an artifact-free edge-preserving reconstruction. The proposed algorithm consists of two elementary steps: (1) edge detection, in which fuzzy-rule-based derivatives are used for the detection of edges in the nearest neighborhood window (which is equivalent to recognizing nearby density classes), and (2) fuzzy smoothing, in which penalization is performed only for those pixels for which no edge is detected in the nearest neighborhood. Both of these operations are carried out iteratively until the image converges. Analysis shows that the proposed fuzzy-rule-based reconstruction algorithm is capable of producing qualitatively better reconstructed images than those reconstructed by MAP and MRP algorithms. The reconstructed images are sharper, with small features being better resolved owing to the nature of the fuzzy potential function.
Fuzzy-rule-based image reconstruction for positron emission tomography.
Mondal, Partha P; Rajan, K
2005-09-01
Positron emission tomography (PET) and single-photon emission computed tomography have revolutionized the field of medicine and biology. Penalized iterative algorithms based on maximum a posteriori (MAP) estimation eliminate noisy artifacts by utilizing available prior information in the reconstruction process but often result in a blurring effect. MAP-based algorithms fail to determine the density class in the reconstructed image and hence penalize the pixels irrespective of the density class. Reconstruction with better edge information is often difficult because prior knowledge is not taken into account. The recently introduced median-root-prior (MRP)-based algorithm preserves the edges, but a steplike streaking effect is observed in the reconstructed image, which is undesirable. A fuzzy approach is proposed for modeling the nature of interpixel interaction in order to build an artifact-free edge-preserving reconstruction. The proposed algorithm consists of two elementary steps: (1) edge detection, in which fuzzy-rule-based derivatives are used for the detection of edges in the nearest neighborhood window (which is equivalent to recognizing nearby density classes), and (2) fuzzy smoothing, in which penalization is performed only for those pixels for which no edge is detected in the nearest neighborhood. Both of these operations are carried out iteratively until the image converges. Analysis shows that the proposed fuzzy-rule-based reconstruction algorithm is capable of producing qualitatively better reconstructed images than those reconstructed by MAP and MR P algorithms. The reconstructed images a resharper, with small features being better resolved owing to the nature of the fuzzy potential function.
Applications of fuzzy sets to rule-based expert system development
Lea, Robert N.
1989-01-01
Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic software development shell is used that allows inclusion of both crisp and fuzzy rules in decision making and process control problems. Results are given that compare this type of expert system to a human expert in some specific applications. Advantages and disadvantages of such systems are discussed.
Inference in fuzzy rule bases with conflicting evidence
Koczy, Laszlo T.
1992-01-01
Inference based on fuzzy 'If ... then' rules has played a very important role since when Zadeh proposed the Compositional Rule of Inference and, especially, since the first successful application presented by Mamdani. From the mid-1980's when the 'fuzzy boom' started in Japan, numerous industrial applications appeared, all using simplified techniques because of the high levels of computational complexity. Another feature is that antecedents in the rules are distributed densely in the input space, so the conclusion can be calculated by some weighted combination of the consequents of the matching (fired) rules. The CRI works in the following way: If R is a rule and A* is an observation, the conclusion is computed by B* = R o A* (o stands for the max-min composition). Algorithms implementing this idea directly have an exponential time complexity (maybe the problem is NP-hard) as the rules are relations in X x Y, a k1 x k2 dimensional space, if X is k1, Y is k2 dimensional. The simplified techniques usually decompose the relation into k1 projections in X(sub i) and measure in some way the degree of similarity between observation and antecedent by some parameter of the overlapping. These parameters are aggregated to a single value in (0,1) which is applied as a resulting weight for the given rule. The projections of rules in dimensions Y(sub i) are weighted by these aggregated values and then they are combined in order to obtain a resulting conclusion separately in every dimension. This method is unapplicable with sparse bases as there is no guarantee that an arbitrary observation matches with any of the antecedents. Then, the degree of similarity is 0 and all consequents are weighted by 0. Some considerations for such a situation are summarized in the next sections.
A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital
Directory of Open Access Journals (Sweden)
Mohammad Hossein Fazel Zarandi
2012-01-01
Full Text Available A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.
Modified risk graph method using fuzzy rule-based approach
Energy Technology Data Exchange (ETDEWEB)
Nait-Said, R., E-mail: r_nait_said@hotmail.com [LARPI Laboratory, Safety Department, Institute of Health and Occupational Safety, University of Batna, Road Med El-Hadi Boukhlouf, Batna (Algeria); Zidani, F., E-mail: fati_zidani@lycos.com [LSPIE Laboratory, Electrical Engineering Department, Faculty of Engineering, University of Batna, Road Med El-Hadi Boukhlouf, Batna 05000 (Algeria); Ouzraoui, N., E-mail: ouzraoui@yahoo.fr [LARPI Laboratory, Safety Department, Institute of Health and Occupational Safety, University of Batna, Road Med El-Hadi Boukhlouf, Batna (Algeria)
2009-05-30
The risk graph is one of the most popular methods used to determine the safety integrity level for safety instrumented functions. However, conventional risk graph as described in the IEC 61508 standard is subjective and suffers from an interpretation problem of risk parameters. Thus, it can lead to inconsistent outcomes that may result in conservative SILs. To overcome this difficulty, a modified risk graph using fuzzy rule-based system is proposed. This novel version of risk graph uses fuzzy scales to assess risk parameters and calibration may be made by varying risk parameter values. Furthermore, the outcomes which are numerical values of risk reduction factor (the inverse of the probability of failure on demand) can be compared directly with those given by quantitative and semi-quantitative methods such as fault tree analysis (FTA), quantitative risk assessment (QRA) and layers of protection analysis (LOPA).
Modified risk graph method using fuzzy rule-based approach.
Nait-Said, R; Zidani, F; Ouzraoui, N
2009-05-30
The risk graph is one of the most popular methods used to determine the safety integrity level for safety instrumented functions. However, conventional risk graph as described in the IEC 61508 standard is subjective and suffers from an interpretation problem of risk parameters. Thus, it can lead to inconsistent outcomes that may result in conservative SILs. To overcome this difficulty, a modified risk graph using fuzzy rule-based system is proposed. This novel version of risk graph uses fuzzy scales to assess risk parameters and calibration may be made by varying risk parameter values. Furthermore, the outcomes which are numerical values of risk reduction factor (the inverse of the probability of failure on demand) can be compared directly with those given by quantitative and semi-quantitative methods such as fault tree analysis (FTA), quantitative risk assessment (QRA) and layers of protection analysis (LOPA).
Discussion on fuzzy decision making based on fuzzy number and compositional rule of inference
Directory of Open Access Journals (Sweden)
Chang Ping-Teng
2015-01-01
Full Text Available This paper provides an improved decision making approach based on fuzzy numbers and the compositional rule of inference by Yao and Yao (2001. They claimed to have created a new method that combines statistical methods and fuzzy theory for medical diagnosis. Currently, numerous papers have cited that work. In this study, we show that if we follow their matrix multiplication operation approach, we will obtain the same result as the original method proposed by Klir and Yuan (1995. Owing to a wellknown property of (row stochastic matrices, if the multiplication is closed, the fuzzy and defuzzy procedure of Yao and Yao (2001 is redundant. Therefore, we advise researchers to think twice before applying this approach to medical diagnosis.
Unexpected rules using a conceptual distance based on fuzzy ontology
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Mohamed Said Hamani
2014-01-01
Full Text Available One of the major drawbacks of data mining methods is that they generate a notably large number of rules that are often obvious or useless or, occasionally, out of the user’s interest. To address such drawbacks, we propose in this paper an approach that detects a set of unexpected rules in a discovered association rule set. Generally speaking, the proposed approach investigates the discovered association rules using the user’s domain knowledge, which is represented by a fuzzy domain ontology. Next, we rank the discovered rules according to the conceptual distances of the rules.
Design of a Fuzzy Rule Base Expert System to Predict and Classify ...
African Journals Online (AJOL)
The main objective of design of a rule base expert system using fuzzy logic approach is to predict and forecast the risk level of cardiac patients to avoid sudden death. In this proposed system, uncertainty is captured using rule base and classification using fuzzy c-means clustering is discussed to overcome the risk level, ...
Fuzzy rule base design using tabu search algorithm for nonlinear system modeling.
Bagis, Aytekin
2008-01-01
This paper presents an approach to fuzzy rule base design using tabu search algorithm (TSA) for nonlinear system modeling. TSA is used to evolve the structure and the parameter of fuzzy rule base. The use of the TSA, in conjunction with a systematic neighbourhood structure for the determination of fuzzy rule base parameters, leads to a significant improvement in the performance of the model. To demonstrate the effectiveness of the presented method, several numerical examples given in the literature are examined. The results obtained by means of the identified fuzzy rule bases are compared with those belonging to other modeling approaches in the literature. The simulation results indicate that the method based on the use of a TSA performs an important and very effective modeling procedure in fuzzy rule base design in the modeling of the nonlinear or complex systems.
Assessing flood vulnerability using a rule-based fuzzy system.
Yazdi, J; Neyshabouri, S A A S
2012-01-01
Population growth and urbanization in the last decades have increased the vulnerability of properties and societies in flood-prone areas. Vulnerability analysis is one of the main factors used to determine the necessary measures of flood risk reduction in floodplains. At present, the vulnerability of natural disasters is analyzed by defining the various physical and social indices. This study presents a model based on a fuzzy rule-based system to address various ambiguities and uncertainties from natural variability, and human knowledge and preferences in vulnerability analysis. The proposed method is applied for a small watershed as a case study and the obtained results are compared with one of the index approaches. Both approaches present the same ranking for the sub-basin's vulnerability in the watershed. Finally, using the scores of vulnerability in different sub-basins, a vulnerability map of the watershed is presented.
A hybrid learning method for constructing compact rule-based fuzzy models.
Zhao, Wanqing; Niu, Qun; Li, Kang; Irwin, George W
2013-12-01
The Takagi–Sugeno–Kang-type rule-based fuzzy model has found many applications in different fields; a major challenge is, however, to build a compact model with optimized model parameters which leads to satisfactory model performance. To produce a compact model, most existing approaches mainly focus on selecting an appropriate number of fuzzy rules. In contrast, this paper considers not only the selection of fuzzy rules but also the structure of each rule premise and consequent, leading to the development of a novel compact rule-based fuzzy model. Here, each fuzzy rule is associated with two sets of input attributes, in which the first is used for constructing the rule premise and the other is employed in the rule consequent. A new hybrid learning method combining the modified harmony search method with a fast recursive algorithm is hereby proposed to determine the structure and the parameters for the rule premises and consequents. This is a hard mixed-integer nonlinear optimization problem, and the proposed hybrid method solves the problem by employing an embedded framework, leading to a significantly reduced number of model parameters and a small number of fuzzy rules with each being as simple as possible. Results from three examples are presented to demonstrate the compactness (in terms of the number of model parameters and the number of rules) and the performance of the fuzzy models obtained by the proposed hybrid learning method, in comparison with other techniques from the literature.
Fuzzy-Rule-Based Object Identification Methodology for NAVI System
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Yaacob Sazali
2005-01-01
Full Text Available We present an object identification methodology applied in a navigation assistance for visually impaired (NAVI system. The NAVI has a single board processing system (SBPS, a digital video camera mounted headgear, and a pair of stereo earphones. The captured image from the camera is processed by the SBPS to generate a specially structured stereo sound suitable for vision impaired people in understanding the presence of objects/obstacles in front of them. The image processing stage is designed to identify the objects in the captured image. Edge detection and edge-linking procedures are applied in the processing of image. A concept of object preference is included in the image processing scheme and this concept is realized using a fuzzy-rule base. The blind users are trained with the stereo sound produced by NAVI for achieving a collision-free autonomous navigation.
Fuzzy-Rule-Based Object Identification Methodology for NAVI System
Nagarajan, R.; Sainarayanan, G.; Yaacob, Sazali; Porle, Rosalyn R.
2005-12-01
We present an object identification methodology applied in a navigation assistance for visually impaired (NAVI) system. The NAVI has a single board processing system (SBPS), a digital video camera mounted headgear, and a pair of stereo earphones. The captured image from the camera is processed by the SBPS to generate a specially structured stereo sound suitable for vision impaired people in understanding the presence of objects/obstacles in front of them. The image processing stage is designed to identify the objects in the captured image. Edge detection and edge-linking procedures are applied in the processing of image. A concept of object preference is included in the image processing scheme and this concept is realized using a fuzzy-rule base. The blind users are trained with the stereo sound produced by NAVI for achieving a collision-free autonomous navigation.
Directory of Open Access Journals (Sweden)
Bima Sena Bayu Dewantara
2014-12-01
Full Text Available Fuzzy rule optimization is a challenging step in the development of a fuzzy model. A simple two inputs fuzzy model may have thousands of combination of fuzzy rules when it deals with large number of input variations. Intuitively and trial‐error determination of fuzzy rule is very difficult. This paper addresses the problem of optimizing Fuzzy rule using Genetic Algorithm to compensate illumination effect in face recognition. Since uneven illumination contributes negative effects to the performance of face recognition, those effects must be compensated. We have developed a novel algorithmbased on a reflectance model to compensate the effect of illumination for human face recognition. We build a pair of model from a single image and reason those modelsusing Fuzzy.Fuzzy rule, then, is optimized using Genetic Algorithm. This approachspendsless computation cost by still keepinga high performance. Based on the experimental result, we can show that our algorithm is feasiblefor recognizing desired person under variable lighting conditions with faster computation time. Keywords: Face recognition, harsh illumination, reflectance model, fuzzy, genetic algorithm
RSPOP: rough set-based pseudo outer-product fuzzy rule identification algorithm.
Ang, Kai Keng; Quek, Chai
2005-01-01
System modeling with neuro-fuzzy systems involves two contradictory requirements: interpretability verses accuracy. The pseudo outer-product (POP) rule identification algorithm used in the family of pseudo outer-product-based fuzzy neural networks (POPFNN) suffered from an exponential increase in the number of identified fuzzy rules and computational complexity arising from high-dimensional data. This decreases the interpretability of the POPFNN in linguistic fuzzy modeling. This article proposes a novel rough set-based pseudo outer-product (RSPOP) algorithm that integrates the sound concept of knowledge reduction from rough set theory with the POP algorithm. The proposed algorithm not only performs feature selection through the reduction of attributes but also extends the reduction to rules without redundant attributes. As many possible reducts exist in a given rule set, an objective measure is developed for POPFNN to correctly identify the reducts that improve the inferred consequence. Experimental results are presented using published data sets and real-world application involving highway traffic flow prediction to evaluate the effectiveness of using the proposed algorithm to identify fuzzy rules in the POPFNN using compositional rule of inference and singleton fuzzifier (POPFNN-CRI(S)) architecture. Results showed that the proposed rough set-based pseudo outer-product algorithm reduces computational complexity, improves the interpretability of neuro-fuzzy systems by identifying significantly fewer fuzzy rules, and improves the accuracy of the POPFNN.
Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG.
Rabbi, Ahmed F; Aarabi, Ardalan; Fazel-Rezai, Reza
2010-01-01
In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on expert's knowledge and reasoning. A spatial-temporal filtering method was used in accordance with the fuzzy rule-based inference system for issuing forecasting alarms. The system was evaluated on EEG data from 10 patients having 15 seizures.
Liu, Feng; Quek, Chai; Ng, Geok See
2007-06-01
There are two important issues in neuro-fuzzy modeling: (1) interpretability--the ability to describe the behavior of the system in an interpretable way--and (2) accuracy--the ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models.
Horizontal and Vertical Rule Bases Method in Fuzzy Controllers
Aminifar, Sadegh; bin Marzuki, Arjuna
2013-01-01
Concept of horizontal and vertical rule bases is introduced. Using this method enables the designers to look for main behaviors of system and describes them with greater approximations. The rules which describe the system in first stage are called horizontal rule base. In the second stage, the designer modulates the obtained surface by describing needed changes on first surface for handling real behaviors of system. The rules used in the second stage are called vertical rule base. Horizontal...
Constructing a fuzzy rule-based system using the ILFN network and Genetic Algorithm.
Yen, G G; Meesad, P
2001-10-01
In this paper, a method for automatic construction of a fuzzy rule-based system from numerical data using the Incremental Learning Fuzzy Neural (ILFN) network and the Genetic Algorithm is presented. The ILFN network was developed for pattern classification applications. The ILFN network, which employed fuzzy sets and neural network theory, equips with a fast, one-pass, on-line, and incremental learning algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly interpreted into if then rule bases. However, the rules extracted from the ILFN network are not in an optimized fuzzy linguistic form. In this paper, a knowledge base for fuzzy expert system is extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only discriminate features from input patterns needed to provide a fuzzy rule-based system. Three computer simulations using a simulated 2-D 3-class data, the well-known Fisher's Iris data set, and the Wisconsin breast cancer data set were performed. The fuzzy rule-based system derived from the proposed method achieved 100% and 97.33% correct classification on the 75 patterns for training set and 75 patterns for test set, respectively. For the Wisconsin breast cancer data set, using 400 patterns for training and 299 patterns for testing, the derived fuzzy rule-based system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.
On Decision-Making Among Multiple Rule-Bases in Fuzzy Control Systems
Tunstel, Edward; Jamshidi, Mo
1997-01-01
Intelligent control of complex multi-variable systems can be a challenge for single fuzzy rule-based controllers. This class of problems cam often be managed with less difficulty by distributing intelligent decision-making amongst a collection of rule-bases. Such an approach requires that a mechanism be chosen to ensure goal-oriented interaction between the multiple rule-bases. In this paper, a hierarchical rule-based approach is described. Decision-making mechanisms based on generalized concepts from single-rule-based fuzzy control are described. Finally, the effects of different aggregation operators on multi-rule-base decision-making are examined in a navigation control problem for mobile robots.
Prediction on carbon dioxide emissions based on fuzzy rules
Pauzi, Herrini; Abdullah, Lazim
2014-06-01
There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.
On-line channel instability localisation with fuzzy rule-based systems
International Nuclear Information System (INIS)
Tambouratzis, T.; Xanthos, S.; Antonopoulos-Domis, M.
2004-01-01
A fuzzy rule-based system is proposed for on-line channel instability localisation within a nuclear reactor, employing a limited number of detector responses. The signals used for constructing the fuzzy rule-based system are obtained from a rough simulation of the reactor and correspond to a restricted number of channel instability locations. Tests with novel channels of instability, which are obtained from a more detailed simulation and cover an extensive number of channel instability locations, demonstrate the potential of the proposed methodology to accurately, robustly and efficiently localise channel instability
Fuzzy rule-based macroinvertebrate habitat suitability models for running waters
Broekhoven, Van E.; Adriaenssens, V.; Baets, De B.; Verdonschot, P.F.M.
2006-01-01
A fuzzy rule-based approach was applied to a macroinvertebrate habitat suitability modelling problem. The model design was based on a knowledge base summarising the preferences and tolerances of 86 macroinvertebrate species for four variables describing river sites in springs up to small rivers in
Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease.
Mohammadpour, Reza Ali; Abedi, Seyed Mohammad; Bagheri, Somayeh; Ghaemian, Ali
2015-01-01
The aim of this study was to determine the accuracy of fuzzy rule-based classification that could noninvasively predict CAD based on myocardial perfusion scan test and clinical-epidemiological variables. This was a cross-sectional study in which the characteristics, the results of myocardial perfusion scan (MPS), and coronary artery angiography of 115 patients, 62 (53.9%) males, in Mazandaran Heart Center in the north of Iran have been collected. We used membership functions for medical variables by reviewing the related literature. To improve the classification performance, we used Ishibuchi et al. and Nozaki et al. methods by adjusting the grade of certainty CF j of each rule. This system includes 144 rules and the antecedent part of all rules has more than one part. The coronary artery disease data used in this paper contained 115 samples. The data was classified into four classes, namely, classes 1 (normal), 2 (stenosis in one single vessel), 3 (stenosis in two vessels), and 4 (stenosis in three vessels) which had 39, 35, 17, and 24 subjects, respectively. The accuracy in the fuzzy classification based on if-then rule was 92.8 percent if classification result was considered based on rule selection by expert, while it was 91.9 when classification result was obtained according to the equation. To increase the classification rate, we deleted the extra rules to reduce the fuzzy rules after introducing the membership functions.
Mansor, Rosnalini; Kasim, Maznah Mat; Othman, Mahmod
2016-08-01
Fuzzy rules are very important elements that should be taken consideration seriously when applying any fuzzy system. This paper proposes the framework of Mamdani Fuzzy Rule-based System with Weighted Subset-hood Based Algorithm (MFRBS-WSBA) in the fuzzy rule extraction for electricity load demand forecasting. The framework consist of six main steps: (1) Data Collection and Selection; (2) Preprocessing Data; (3) Variables Selection; (4) Fuzzy Model; (5) Comparison with Other FIS and (6) Performance Evaluation. The objective of this paper is to show the fourth step in the framework which applied the new electricity load forecasting rule extraction by WSBA method. Electricity load demand in Malaysia data is used as numerical data in this framework. These preliminary results show that the WSBA method can be one of alternative methods to extract fuzzy rules for forecast electricity load demand
Mansourypoor, Fatemeh; Asadi, Shahrokh
2017-12-01
The early diagnosis of disease is critical to preventing the occurrence of severe complications. Diabetes is a serious health problem. A variety of methods have been developed for diagnosing diabetes. The majority of these methods have been developed in a black-box manner, which cannot be used to explain the inference and diagnosis procedure. Therefore, it is essential to develop methods with high accuracy and interpretability. In this study, a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System (RLEFRBS) is developed for diabetes diagnosis. The proposed model involves the building of a Rule Base (RB) and rule optimization. The initial RB is constructed using numerical data without initial rules; after learning the rules, redundant rules are eliminated based on the confidence measure. Next, redundant conditions in the antecedent parts are pruned to yield simpler rules with higher interpretability. Finally, an appropriate subset of the rules is selected using a Genetic Algorithm (GA), and the RB is constructed. Evolutionary tuning of the membership functions and weight adjusting using Reinforcement Learning (RL) are used to improve the performance of RLEFRBS. Moreover, to deal with uncovered instances, it makes use of an efficient rule stretching method. The performance of RLEFRBS was examined using two common datasets: Pima Indian Diabetes (PID) and BioSat Diabetes Dataset (BDD). The experimental results show that the proposed model provides a more compact, interpretable and accurate RB that can be considered to be a promising alternative for diagnosis of diabetes. Copyright © 2017 Elsevier Ltd. All rights reserved.
Capacities and overlap indexes with an application in fuzzy rule-based classification systems
Czech Academy of Sciences Publication Activity Database
Paternain, D.; Bustince, H.; Pagola, M.; Sussner, P.; Kolesárová, A.; Mesiar, Radko
2016-01-01
Roč. 305, č. 1 (2016), s. 70-94 ISSN 0165-0114 Institutional support: RVO:67985556 Keywords : Capacity * Overlap index * Overlap function * Choquet integral * Fuzzy rule-based classification systems Subject RIV: BA - General Mathematics Impact factor: 2.718, year: 2016 http://library.utia.cas.cz/separaty/2016/E/mesiar-0465739.pdf
Generation of facial expressions from emotion using a fuzzy rule based system
Bui, T.D.; Heylen, Dirk K.J.; Poel, Mannes; Nijholt, Antinus; Stumptner, Markus; Corbett, Dan; Brooks, Mike
2001-01-01
We propose a fuzzy rule-based system to map representations of the emotional state of an animated agent onto muscle contraction values for the appropriate facial expressions. Our implementation pays special attention to the way in which continuous changes in the intensity of emotions can be
Directory of Open Access Journals (Sweden)
C. Boldisor
2009-12-01
Full Text Available A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. System’s desired performance is defined by a reference model and rules are extracted from recorded data, after the correct control actions are learned. The presented algorithm is tested in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed.
Ho, Shinn-Ying; Hsieh, Chih-Hung; Chen, Hung-Ming; Huang, Hui-Ling
2006-09-01
An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimized: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9%), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers.
Directory of Open Access Journals (Sweden)
R Poorva Devi
2016-04-01
Full Text Available So far, in cloud computing distinct customer is accessed and consumed enormous amount of services through web, offered by cloud service provider (CSP. However cloud is providing one of the services is, security-as-a-service to its clients, still people are terrified to use the service from cloud vendor. Number of solutions, security components and measurements are coming with the new scope for the cloud security issue, but 79.2% security outcome only obtained from the different scientists, researchers and other cloud based academy community. To overcome the problem of cloud security the proposed model that is, “Quality based Enhancing the user data protection via fuzzy rule based systems in cloud environment”, will helps to the cloud clients by the way of accessing the cloud resources through remote monitoring management (RMMM and what are all the services are currently requesting and consuming by the cloud users that can be well analyzed with Managed service provider (MSP rather than a traditional CSP. Normally, people are trying to secure their own private data by applying some key management and cryptographic based computations again it will direct to the security problem. In order to provide good quality of security target result by making use of fuzzy rule based systems (Constraint & Conclusion segments in cloud environment. By using this technique, users may obtain an efficient security outcome through the cloud simulation tool of Apache cloud stack simulator.
Kaya, Mehmet; Alhajj, Reda
2005-04-01
Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment. of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.
Hierarchization process by possibilistic fuzzy clustering of fuzzy rules
Salgado, Paulo; Cunha, Manuela; Pavão, João; Igrejas, Getúlio
2010-01-01
This paper presents a possibilistic fuzzy clustering algorithm that is applied to a multidimensional fuzzy set or fuzzy rules. This method can be used to decompose the fuzzy system into an hierarchical structure. The methodology presented leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. This technique is tested to organize the fuzzy model into a new and more comprehensive structure.
Fuzzy rule based estimation of agricultural diffuse pollution concentration in streams.
Singh, Raj Mohan
2008-04-01
Outflow from the agricultural fields carries diffuse pollutants like nutrients, pesticides, herbicides etc. and transports the pollutants into the nearby streams. It is a matter of serious concern for water managers and environmental researchers. The application of chemicals in the agricultural fields, and transport of these chemicals into streams are uncertain that cause complexity in reliable stream quality predictions. The chemical characteristics of applied chemical, percentage of area under the chemical application etc. are some of the main inputs that cause pollution concentration as output in streams. Each of these inputs and outputs may contain measurement errors. Fuzzy rule based model based on fuzzy sets suits to address uncertainties in inputs by incorporating overlapping membership functions for each of inputs even for limited data availability situations. In this study, the property of fuzzy sets to address the uncertainty in input-output relationship is utilized to obtain the estimate of concentrations of a herbicide, atrazine, in a stream. The data of White river basin, a part of the Mississippi river system, is used for developing the fuzzy rule based models. The performance of the developed methodology is found encouraging.
Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.
Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi
2013-01-01
The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.
CONSTRUCTION OF FUZZY C CONTROL CHARTS BASED ON FUZZY RULE METHOD
ŞENTÜRK, Sevil
2017-01-01
A control chart is a tool thatis used for representing and monitoring the process. Also control chartdetected process shifts and abnormal conditions in a process. In a processmonitored the c control charts, due to the uncertainty of the attribute data, ccontrol chart may not applicable for the process since it’s required certaininformation. Many papers of fuzzy control charts with type-1 fuzzy sets basedon transformation techniques are exist in literature. This paper constructedthe fuzzy c co...
A fuzzy rule-based system for epileptic seizure detection in intracranial EEG.
Aarabi, A; Fazel-Rezai, R; Aghakhani, Y
2009-09-01
We present a method for automatic detection of seizures in intracranial EEG recordings from patients suffering from medically intractable focal epilepsy. We designed a fuzzy rule-based seizure detection system based on knowledge obtained from experts' reasoning. Temporal, spectral, and complexity features were extracted from IEEG segments, and spatio-temporally integrated using the fuzzy rule-based system for seizure detection. A total of 302.7h of intracranial EEG recordings from 21 patients having 78 seizures was used for evaluation of the system. The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11s. There was only one missed seizure. Most of false detections were caused by high-amplitude rhythmic activities. The results from the system correlate well with those from expert visual analysis. The fuzzy rule-based seizure detection system enabled us to deal with imprecise boundaries between interictal and ictal IEEG patterns. This system may serve as a good seizure detection tool with high sensitivity and low false detection rate for monitoring long-term IEEG.
Fuzzy Rule-based Analysis of Promotional Efficiency in Vietnam’s Tourism Industry
Nguyen Quang VINH; Dam Van KHANH; Nguyen Viet ANH
2015-01-01
This study aims to determine an effective method of measuring the efficiency of promotional strategies for tourist destinations. Complicating factors that influence promotional efficiency (PE), such as promotional activities (PA), destination attribute (DA), and destination image (DI), make it difficult to evaluate the effectiveness of PE. This study develops a rule-based decision support mechanism using fuzzy set theory and the Analytic Hierarchy Process (AHP) to evaluate the effectiveness o...
Improving the anesthetic process by a fuzzy rule based medical decision system.
Mendez, Juan Albino; Leon, Ana; Marrero, Ayoze; Gonzalez-Cava, Jose M; Reboso, Jose Antonio; Estevez, Jose Ignacio; Gomez-Gonzalez, José F
2018-01-01
The main objective of this research is the design and implementation of a new fuzzy logic tool for automatic drug delivery in patients undergoing general anesthesia. The aim is to adjust the drug dose to the real patient needs using heuristic knowledge provided by clinicians. A two-level computer decision system is proposed. The idea is to release the clinician from routine tasks so that he can focus on other variables of the patient. The controller uses the Bispectral Index (BIS) to assess the hypnotic state of the patient. Fuzzy controller was included in a closed-loop system to reach the BIS target and reject disturbances. BIS was measured using a BIS VISTA monitor, a device capable of calculating the hypnosis level of the patient through EEG information. An infusion pump with propofol 1% is used to supply the drug to the patient. The inputs to the fuzzy inference system are BIS error and BIS rate. The output is infusion rate increment. The mapping of the input information and the appropriate output is given by a rule-base based on knowledge of clinicians. To evaluate the performance of the fuzzy closed-loop system proposed, an observational study was carried out. Eighty one patients scheduled for ambulatory surgery were randomly distributed in 2 groups: one group using a fuzzy logic based closed-loop system (FCL) to automate the administration of propofol (42 cases); the second group using manual delivering of the drug (39 cases). In both groups, the BIS target was 50. The FCL, designed with intuitive logic rules based on the clinician experience, performed satisfactorily and outperformed the manual administration in patients in terms of accuracy through the maintenance stage. Copyright © 2018 Elsevier B.V. All rights reserved.
A fuzzy-rule-based approach for single frame super resolution.
Purkait, Pulak; Pal, Nikhil Ranjan; Chanda, Bhabatosh
2014-05-01
In this paper, a novel fuzzy rule-based prediction framework is developed for high-quality image zooming. In classical interpolation-based image zooming, resolution is increased by inserting pixels using certain interpolation techniques. Here, we propose a patch-based image zooming technique, where each low-resolution (LR) image patch is replaced by an estimated high-resolution (HR) patch. Since an LR patch can be generated from any of the many possible HR patches, it would be natural to develop rules to find different possible HR patches and then to combine them according to rule strength to get the estimated HR patch. Here, we generate a large number of LR–HR patch pairs from a collection of natural images, group them into different clusters, and then generate a fuzzy rule for each of these clusters. The rule parameters are also learned from these LR-HR patch pairs. As a result, an efficient mapping from LR patch space to HR patch space can be formulated. The performance of the proposed method is tested on different images, and is also compared with other representative as well as state-of-the-art image zooming techniques. Experimental results show that the proposed method is better than the competing methods and is capable of reconstructing thin lines, edges, fine details, and textures in the image efficiently.
Fuzzy rule-based forecast of meteorological drought in western Niger
Abdourahamane, Zakari Seybou; Acar, Reşat
2018-01-01
Understanding the causes of rainfall anomalies in the West African Sahel to effectively predict drought events remains a challenge. The physical mechanisms that influence precipitation in this region are complex, uncertain, and imprecise in nature. Fuzzy logic techniques are renowned to be highly efficient in modeling such dynamics. This paper attempts to forecast meteorological drought in Western Niger using fuzzy rule-based modeling techniques. The 3-month scale standardized precipitation index (SPI-3) of four rainfall stations was used as predictand. Monthly data of southern oscillation index (SOI), South Atlantic sea surface temperature (SST), relative humidity (RH), and Atlantic sea level pressure (SLP), sourced from the National Oceanic and Atmosphere Administration (NOAA), were used as predictors. Fuzzy rules and membership functions were generated using fuzzy c-means clustering approach, expert decision, and literature review. For a minimum lead time of 1 month, the model has a coefficient of determination R 2 between 0.80 and 0.88, mean square error (MSE) below 0.17, and Nash-Sutcliffe efficiency (NSE) ranging between 0.79 and 0.87. The empirical frequency distributions of the predicted and the observed drought classes are equal at the 99% of confidence level based on two-sample t test. Results also revealed the discrepancy in the influence of SOI and SLP on drought occurrence at the four stations while the effect of SST and RH are space independent, being both significantly correlated (at α fuzzy model compared to decision tree-based forecast model shows better forecast skills.
Farivar, Faezeh; Shoorehdeli, Mahdi Aliyari
2012-01-01
In this paper, fault tolerant synchronization of chaotic gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control is investigated. Taking the general nature of faults in the slave system into account, a new synchronization scheme, namely, fault tolerant synchronization, is proposed, by which the synchronization can be achieved no matter whether the faults and disturbances occur or not. By making use of a slave observer and a Lyapunov rule-based fuzzy control, fault tolerant synchronization can be achieved. Two techniques are considered as control methods: classic Lyapunov-based control and Lyapunov rule-based fuzzy control. On the basis of Lyapunov stability theory and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic synchronization are obtained. The fuzzy rules are directly constructed subject to a common Lyapunov function such that the error dynamics of two identical chaotic motions of symmetric gyros satisfy stability in the Lyapunov sense. Two proposed methods are compared. The Lyapunov rule-based fuzzy control can compensate for the actuator faults and disturbances occurring in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method for fault tolerant synchronization. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.
Clustering algorithms for fuzzy rules decomposition
Salgado, Paulo; Igrejas, Getúlio
2007-01-01
This paper presents the development, testing and evaluation of generalized Possibilistic fuzzy c-means (FCM) algorithms applied to fuzzy sets. Clustering is formulated as a constrained minimization problem, whose solution depends on the constraints imposed on the membership function of the cluster and on the relevance measure of the fuzzy rules. This fuzzy clustering of fuzzy rules leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresp...
Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.
Pasquier, M; Quek, C; Toh, M
2001-10-01
This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.
A Fuzzy Rule-Based Penalty Function Approach for Constrained Evolutionary Optimization.
Saha, Chiranjib; Das, Swagatam; Pal, Kunal; Mukherjee, Satrajit
2016-12-01
This paper proposes a novel fuzzy rule-based penalty function approach for solving single-objective nonlinearly constrained optimization problems. Of all the existing state-of-the-art constraint handling techniques, the conventional method of penalty can be easily implemented because of its simplicity but suffers from the lack of robustness. To mitigate the problem of parameter dependency, several forms of adaptive penalties have been suggested in literature. Instead of identifying a complex mathematical function to compute the penalty for constraint violation, we propose a Mamdani type IF-THEN rule-based fuzzy inference system that incorporates all the required criteria of self-adaptive penalty without formulating an explicit mapping. Effectiveness of the proposed constrained optimization algorithm has been empirically validated on the basis of the standard optimality theorems from the literature on mathematical programming. Simulation results show that fuzzy penalty not only surpasses its existing counterpart i.e., self adaptive penalty, but also remain competitive against several other standard as well as currently developed complex constraint handling strategies.
Directory of Open Access Journals (Sweden)
Sanping Rao
2013-01-01
Full Text Available This paper is an attempt to develop quantitative domain theory over frames. Firstly, we propose the notion of a fuzzy basis, and several equivalent characterizations of fuzzy bases are obtained. Furthermore, the concept of a fuzzy algebraic domain is introduced, and a relationship between fuzzy algebraic domains and fuzzy domains is discussed from the viewpoint of fuzzy basis. We finally give an application of fuzzy bases, where the image of a fuzzy domain can be preserved under some special kinds of fuzzy Galois connections.
Zhang, Chi; Wang, Yilun; Zhang, Lili; Zhou, Huicheng
2012-01-01
In this paper, a computationally efficient version of the widely used Takagi-Sugeno (T-S) fuzzy reasoning method is proposed, and applied to river flood forecasting. It is well known that the number of fuzzy rules of traditional fuzzy reasoning methods exponentially increases as the number of input parameters increases, often causing prohibitive computational burden. The proposed method greatly reduces the number of fuzzy rules by making use of the association rule analysis on historical data, and therefore achieves computational efficiency for the cases of a large number of input parameters. In the end, we apply this new method to a case study of river flood forecasting, which demonstrates that the proposed fuzzy reasoning engine can achieve better prediction accuracy than the widely used Muskingum-Cunge scheme.
Fuzzy Rule-based Analysis of Promotional Efficiency in Vietnam’s Tourism Industry
Directory of Open Access Journals (Sweden)
Nguyen Quang VINH
2015-06-01
Full Text Available This study aims to determine an effective method of measuring the efficiency of promotional strategies for tourist destinations. Complicating factors that influence promotional efficiency (PE, such as promotional activities (PA, destination attribute (DA, and destination image (DI, make it difficult to evaluate the effectiveness of PE. This study develops a rule-based decision support mechanism using fuzzy set theory and the Analytic Hierarchy Process (AHP to evaluate the effectiveness of promotional strategies. Additionally, a statistical analysis is conducted using SPSS (Statistics Package for Social Science to confirm the results of the fuzzy AHP analysis. This study finds that government policy is the most important factor for PE and that service staff (internal beauty is more important than tourism infrastructure (external beauty in terms of customer satisfaction and long-term strategy in PE. With respect to DI, experts are concerned first with tourist perceived value, second with tourist satisfaction and finally with tourist loyalty.
An interpretable fuzzy rule-based classification methodology for medical diagnosis.
Gadaras, Ioannis; Mikhailov, Ludmil
2009-09-01
The aim of this paper is to present a novel fuzzy classification framework for the automatic extraction of fuzzy rules from labeled numerical data, for the development of efficient medical diagnosis systems. The proposed methodology focuses on the accuracy and interpretability of the generated knowledge that is produced by an iterative, flexible and meaningful input partitioning mechanism. The generated hierarchical fuzzy rule structure is composed by linguistic; multiple consequent fuzzy rules that considerably affect the model comprehensibility. The performance of the proposed method is tested on three medical pattern classification problems and the obtained results are compared against other existing methods. It is shown that the proposed variable input partitioning leads to a flexible decision making framework and fairly accurate results with a small number of rules and a simple, fast and robust training process.
Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier.
Davoodi, Raheleh; Moradi, Mohammad Hassan
2018-03-01
Electronic health records (EHRs) contain critical information useful for clinical studies. Early assessment of patients' mortality in intensive care units is of great importance. In this paper, a Deep Rule-Based Fuzzy System (DRBFS) was proposed to develop an accurate in-hospital mortality prediction in the intensive care unit (ICU) patients employing a large number of input variables. Our main contribution is proposing a system, which is capable of dealing with big data with heterogeneous mixed categorical and numeric attributes. In DRBFS, the hidden layer in each unit is represented by interpretable fuzzy rules. Benefiting the strength of soft partitioning, a modified supervised fuzzy k-prototype clustering has been employed for fuzzy rule generation. According to the stacked approach, the same input space is kept in every base building unit of DRBFS. The training set in addition to random shifts, obtained from random projections of prediction results of the current base building unit is presented as the input of the next base building unit. A cohort of 10,972 adult admissions was selected from Medical Information Mart for Intensive Care (MIMIC-III) data set, where 9.31% of patients have died in the hospital. A heterogeneous feature set of first 48 h from ICU admissions, were extracted for in-hospital mortality rate. Required preprocessing and appropriate feature extraction were applied. To avoid biased assessments, performance indexes were calculated using holdout validation. We have evaluated our proposed method with several common classifiers including naïve Bayes (NB), decision trees (DT), Gradient Boosting (GB), Deep Belief Networks (DBN) and D-TSK-FC. The area under the receiver operating characteristics curve (AUROC) for NB, DT, GB, DBN, D-TSK-FC and our proposed method were 73.51%, 61.81%, 72.98%, 70.07%, 66.74% and 73.90% respectively. Our results have demonstrated that DRBFS outperforms various methods, while maintaining interpretable rule bases
Butt, Muhammad Arif; Akram, Muhammad
2016-01-01
We present a new intuitionistic fuzzy rule-based decision-making system based on intuitionistic fuzzy sets for a process scheduler of a batch operating system. Our proposed intuitionistic fuzzy scheduling algorithm, inputs the nice value and burst time of all available processes in the ready queue, intuitionistically fuzzify the input values, triggers appropriate rules of our intuitionistic fuzzy inference engine and finally calculates the dynamic priority (dp) of all the processes in the ready queue. Once the dp of every process is calculated the ready queue is sorted in decreasing order of dp of every process. The process with maximum dp value is sent to the central processing unit for execution. Finally, we show complete working of our algorithm on two different data sets and give comparisons with some standard non-preemptive process schedulers.
Ho, Shinn-Ying; Chen, Hung-Ming; Ho, Shinn-Jang; Chen, Tai-Kang
2004-04-01
An evolutionary approach to designing accurate classifiers with a compact fuzzy-rule base using a scatter partition of feature space is proposed, in which all the elements of the fuzzy classifier design problem have been moved in parameters of a complex optimization problem. An intelligent genetic algorithm (IGA) is used to effectively solve the design problem of fuzzy classifiers with many tuning parameters. The merits of the proposed method are threefold: 1) the proposed method has high search ability to efficiently find fuzzy rule-based systems with high fitness values, 2) obtained fuzzy rules have high interpretability, and 3) obtained compact classifiers have high classification accuracy on unseen test patterns. The sensitivity of control parameters of the proposed method is empirically analyzed to show the robustness of the IGA-based method. The performance comparison and statistical analysis of experimental results using ten-fold cross validation show that the IGA-based method without heuristics is efficient in designing accurate and compact fuzzy classifiers using 11 well-known data sets with numerical attribute values.
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
Directory of Open Access Journals (Sweden)
Y.-M. Chiang
2011-01-01
Full Text Available Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.
Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?
Directory of Open Access Journals (Sweden)
Alberto Fernandez
2017-01-01
Full Text Available The significance of addressing Big Data applications is beyond all doubt. The current ability of extracting interesting knowledge from large volumes of information provides great advantages to both corporations and academia. Therefore, researchers and practitioners must deal with the problem of scalability so that Machine Learning and Data Mining algorithms can address Big Data properly. With this end, the MapReduce programming framework is by far the most widely used mechanism to implement fault-tolerant distributed applications. This novel framework implies the design of a divide-and-conquer mechanism in which local models are learned separately in one stage (Map tasks whereas a second stage (Reduce is devoted to aggregate all sub-models into a single solution. In this paper, we focus on the analysis of the behavior of Linguistic Fuzzy Rule Based Classification Systems when embedded into a MapReduce working procedure. By retrieving different information regarding the rules learned throughout the MapReduce process, we will be able to identify some of the capabilities of this particular paradigm that allowed them to provide a good performance when addressing Big Data problems. In summary, we will show that linguistic fuzzy classifiers are a robust approach in case of scalability requirements.
Huang, Hui-Ling; Chang, Fang-Lin; Ho, Shinn-Jang; Shu, Li-Sun; Huang, Wen-Lin; Ho, Shinn-Ying
2013-03-01
Numerous prediction methods of DNA-binding domains/proteins were proposed by identifying informative features and designing effective classifiers. These researches reveal that the DNA-protein binding mechanism is complicated and existing accurate predictors such as support vector machine (SVM) with position specific scoring matrices (PSSMs) are regarded as black-box methods which are not easily interpretable for biologists. In this study, we propose an ensemble fuzzy rule base classifier consisting of a set of interpretable fuzzy rule classifiers (iFRCs) using informative physicochemical properties as features. In designing iFRCs, feature selection, membership function design, and fuzzy rule base generation are all simultaneously optimized using an intelligent genetic algorithm (IGA). IGA maximizes prediction accuracy, minimizes the number of features selected, and minimizes the number of fuzzy rules to generate an accurate and concise fuzzy rule base. Benchmark datasets of DNA-binding domains are used to evaluate the proposed ensemble classifier of 30 iFRCs. Each iFRC has a mean test accuracy of 77.46%, and the ensemble classifier has a test accuracy of 83.33%, where the method of SVM with PSSMs has the accuracy of 82.81%. The physicochemical properties of the first two ranks according to their contribution are positive charge and Van Der Waals volume. Charge complementarity between protein and DNA is thought to be important in the first step of recognition between protein and DNA. The amino acid residues of binding peptides have larger Van Der Waals volumes and positive charges than those of non-binding ones. The proposed knowledge acquisition method by establishing a fuzzy rule-based classifier can also be applicable to predict and analyze other protein functions from sequences.
A FORMALISM FOR FUZZY BUSINESS RULES
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Vasile Mazilescu
2015-05-01
Full Text Available The aim of this paper is to provide a formalism for fuzzy rule bases, included in our prototype system FUZZY_ENTERPRISE. This framework can be used in Distributed Knowledge Management Systems (DKMSs, real-time interdisciplinary decision making systems, that often require increasing technical support to high quality decisions in a timely manner. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques.
Evolving fuzzy rules in a learning classifier system
Valenzuela-Rendon, Manuel
1993-01-01
The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.
Probabilistic fuzzy clustering algorithm for fuzzy rules decomposition
Salgado, Paulo; Igrejas, Getúlio
2007-01-01
The Fuzzy C-Means (FCM) clustering algorithm is the best known and the most used method for fuzzy clustering and is generally applied to well defined sets of data. In this work a generalized Probabilistic Fuzzy C-Means (PFCM) algorithm is proposed and applied to fuzzy sets clustering. The methodology presented leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of a flat fuzzy system the resul...
DEFF Research Database (Denmark)
Dounias, George; Tsakonas, Athanasios; Jantzen, Jan
2002-01-01
programming system for the generation of fuzzy rule-based systems. Two different medical domains are used to evaluate the models. The first field is the diagnosis of subtypes of Aphasia. Two models for crisp rule-bases are presented. The first one discriminates between four major types and the second attempts...... systems. Comparisons on the system's comprehensibility and the transparency are included. These comparisons include for the Aphasia domain, previous work consisted of two neural network models....
An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems
Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim
2017-03-01
Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.
Fuzzy Rule Suram for Wood Drying
Situmorang, Zakarias
2017-12-01
Implemented of fuzzy rule must used a look-up table as defuzzification analysis. Look-up table is the actuator plant to doing the value of fuzzification. Rule suram based of fuzzy logic with variables of weather is temperature ambient and humidity ambient, it implemented for wood drying process. The membership function of variable of state represented in error value and change error with typical map of triangle and map of trapezium. Result of analysis to reach 4 fuzzy rule in 81 conditions to control the output system can be constructed in a number of way of weather and conditions of air. It used to minimum of the consumption of electric energy by heater. One cycle of schedule drying is a serial of condition of chamber to process as use as a wood species.
Probabilistic clustering algorithms for fuzzy rules decomposition
Salgado, Paulo; Igrejas, Getúlio
2007-01-01
The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of a flat fuzzy system results a set of...
DEFF Research Database (Denmark)
Jarre, Astrid; Paterson, B.; Moloney, C.L.
2008-01-01
rule-based Boolean and fuzzy-logic models have been used successfully as knowledge-based decision support tools. This study compares two such systems relevant to fisheries management in an EAF developed for the southern Benguela. The first is a rule-based system for the prediction of anchovy...
Sanitizing sensitive association rules using fuzzy correlation scheme
International Nuclear Information System (INIS)
Hameed, S.; Shahzad, F.; Asghar, S.
2013-01-01
Data mining is used to extract useful information hidden in the data. Sometimes this extraction of information leads to revealing sensitive information. Privacy preservation in Data Mining is a process of sanitizing sensitive information. This research focuses on sanitizing sensitive rules discovered in quantitative data. The proposed scheme, Privacy Preserving in Fuzzy Association Rules (PPFAR) is based on fuzzy correlation analysis. In this work, fuzzy set concept is integrated with fuzzy correlation analysis and Apriori algorithm to mark interesting fuzzy association rules. The identified rules are called sensitive. For sanitization, we use modification technique where we substitute maximum value of fuzzy items with zero, which occurs most frequently. Experiments demonstrate that PPFAR method hides sensitive rules with minimum modifications. The technique also maintains the modified data's quality. The PPFAR scheme has applications in various domains e.g. temperature control, medical analysis, travel time prediction, genetic behavior prediction etc. We have validated the results on medical dataset. (author)
Compensation of the effects of muscle fatigue on EMG-based control using fuzzy rules based scheme.
Lalitharatne, Thilina Dulantha; Hayashi, Yoshiaki; Teramoto, Kenbu; Kiguchi, Kazuo
2013-01-01
Estimation of the correct motion intention of the user is very important for most of the Electromyography (EMG) based control applications such as prosthetics, power-assist exoskeletons, rehabilitation and teleoperation robots. On the other hand, safety and long term reliability are also vital for those applications, as they interact with human users. By considering these requirements, many EMG-based control applications have been proposed and developed. However, there are still many challenges to be addressed in the case of EMG based control systems. One of the challenges that had not been considered in such EMG-based control in common is the muscle fatigue. The muscle fatiguing effects of the user can deteriorate the effectiveness of the EMG-based control in the long run, which makes the EMG-based control to produce less accurate results. Therefore, in this study we attempted to develop a fuzzy rule based scheme to compensate the effects of muscle fatigues on EMG based control. Fuzzy rule based weights have been estimated based on time and frequency domain features of the EMG signals. Eventually, these weights have been used to modify the controller output according with the muscle fatigue condition in the muscles. The effectiveness of the proposed method has been evaluated by experiments.
Learning and Tuning of Fuzzy Rules
Berenji, Hamid R.
1997-01-01
In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.
An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules
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Antonio
2012-04-01
Full Text Available Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able to represent the knowledge using this model is proposed as well. On the other hand, potential relations among initial variables imply an exponential growth in the feasible rule search space. Consequently, two filters for detecting relevant potential relations are added to the learning algorithm. These filters allows to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an experimental study to demonstrate the benefits of using fuzzy relational rules.
Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model
Farahi Ghasre Aboonasr, Sedigheh; Zamani, Ahmad; Razavipour, Fatemeh; Boostani, Reza
2017-08-01
Producing accurate seismic hazard map and predicting hazardous areas is necessary for risk mitigation strategies. In this paper, a fuzzy logic inference system is utilized to estimate the earthquake potential and seismic zoning of Zagros Orogenic Belt. In addition to the interpretability, fuzzy predictors can capture both nonlinearity and chaotic behavior of data, where the number of data is limited. In this paper, earthquake pattern in the Zagros has been assessed for the intervals of 10 and 50 years using fuzzy rule-based model. The Molchan statistical procedure has been used to show that our forecasting model is reliable. The earthquake hazard maps for this area reveal some remarkable features that cannot be observed on the conventional maps. Regarding our achievements, some areas in the southern (Bandar Abbas), southwestern (Bandar Kangan) and western (Kermanshah) parts of Iran display high earthquake severity even though they are geographically far apart.
Prakash, Pattan; Mytri, V D; Hiremath, P S
2011-12-01
The quantification of three classes of graphite inclusions in cast iron, namely, nodular, flake, and irregular, is the most important process in the foundry industry. This classification is based on the ISO 945 proposed morphology of graphite inclusions. This work presents a novel solution for automatic quantitative analysis of graphite inclusions into the three mentioned classes. The proposed work comprises three stages, namely, preprocessing of micrographs, classification of graphite inclusions, and then quantification of inclusions in each class. An effort has been made in this work to propose a minimum set of features to represent graphite inclusion morphology. The method employs just two geometric shape descriptors: the diameter ratio and the area ratio. A fuzzy rule based classifier is built using known feature values that are efficient in the classification of the three classes of graphite inclusions. The proposed method is automatic, fast, and provides the basis for determining many more morphological parameters that can be determined with the least effort. The results obtained by the proposed method are compared with the manual method. It is observed that the results obtained from the proposed method are useful in the optimization of cast iron manufacturing in the foundry industry.
Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.
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Jure Demšar
Full Text Available Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging, group decision-making process, and group behaviour types. The question 'why,' however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.
Colonic polyp detection in CT colonography with fuzzy rule based 3D template matching.
Kilic, Niyazi; Ucan, Osman N; Osman, Onur
2009-02-01
In this paper, we introduced a computer aided detection (CAD) system to facilitate colonic polyp detection in computer tomography (CT) data using cellular neural network, genetic algorithm and three dimensional (3D) template matching with fuzzy rule based tresholding. The CAD system extracts colon region from CT images using cellular neural network (CNN) having A, B and I templates that are optimized by genetic algorithm in order to improve the segmentation performance. Then, the system performs a 3D template matching within four layers with three different cell of 8 x 8, 12 x 12 and 20 x 20 to detect polyps. The CAD system is evaluated with 1043 CT colonography images from 16 patients containing 15 marked polyps. All colon regions are segmented properly. The overall sensitivity of proposed CAD system is 100% with the level of 0.53 false positives (FPs) per slice and 11.75 FPs per patient for the 8 x 8 cell template. For the 12 x 12 cell templates, detection sensitivity is 100% at 0.494 FPs per slice and 8.75 FPs per patient and for the 20 x 20 cell templates, detection sensitivity is 86.66% with the level of 0.452 FPs per slice and 6.25 FPs per patient.
Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.
Demšar, Jure; Lebar Bajec, Iztok
2017-01-01
Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question 'why,' however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.
Directory of Open Access Journals (Sweden)
H. Aksoy
2006-01-01
Full Text Available The evaluation of the rockfall initiation mechanism and the simulation of the runout behavior is an important issue in the prevention and remedial measures for potential rockfall hazards in highway protection, in forest preservation, and especially in urban settlement areas. In most of the studies in the literature, the extent of the rockfall hazard was determined by various techniques basing on the selection of a rockfall source, generally defined as zones of rock bodies having slope angles higher than a certain value, proposed by general practice. In the present study, it was aimed to carry out a rule-based fuzzy analysis on the discontinuity data of andesites in the city of Ankara, Turkey, in order to bring a different and rather systematic approach to determine the source areas for rockfall hazard in an urban settlement, based on the discontinuity and natural slope features. First, to obtain rock source areas (RSAs, data obtained from the field studies were combined with a rule-based fuzzy evaluation, incorporating the altitude difference, the number of discontinuities, the number of wedges and the number of potential slides as the parameters of the fuzzy sets. After processing the outputs of the rule-based fuzzy system and producing the linguistic definitions, it could be possible to obtain potential RSAs. According to the RSA maps, 1.7% of the study area was found to have "high RSA", and 5.8% of the study area was assigned as "medium RSA". Then, potential rockfall hazard map was prepared. At the final stage, based upon the high and medium RSAs, 3.6% of the study area showed "high rockfall potential", while areal distribution of "medium rockfall potential" was found as 7.9%. Both RSA and potential rockfall hazard map were in accordance with the observations performed in the field.
Chaubey, I.; Vema, V. K.; Sudheer, K.
2016-12-01
Site suitability evaluation of water conservation structures in water scarce rainfed agricultural areas consist of assessment of various landscape characteristics and various criterion. Many of these landscape characteristic attributes are conveyed through linguistic terms rather than precise numeric values. Fuzzy rule based system are capable of incorporating uncertainty and vagueness, when various decision making criteria expressed in linguistic terms are expressed as fuzzy rules. In this study a fuzzy rule based decision support system is developed, for optimal site selection of water harvesting technologies. Water conservation technologies like farm ponds, Check dams, Rock filled dams and percolation ponds aid in conserving water for irrigation and recharging aquifers and development of such a system will aid in improving the efficiency of the structures. Attributes and criteria involved in decision making are classified into different groups to estimate the suitability of the particular technology. The developed model is applied and tested on an Indian watershed. The input attributes are prepared in raster format in ArcGIS software and suitability of each raster cell is calculated and output is generated in the form of a thematic map showing the suitability of the cells pertaining to different technologies. The output of the developed model is compared against the already existing structures and results are satisfactory. This developed model will aid in improving the sustainability and efficiency of the watershed management programs aimed at enhancing in situ moisture content.
Liu, Xiaojia; An, Haizhong; Wang, Lijun; Guan, Qing
2017-09-01
The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions.
Macian-Sorribes, Hector; Pulido-Velazquez, Manuel
2013-04-01
Water resources systems are operated, mostly, using a set of pre-defined rules not regarding, usually, to an optimal allocation in terms of water use or economic benefits, but to historical and institutional reasons. These operating policies are reproduced, commonly, as hedging rules, pack rules or zone-based operations, and simulation models can be used to test their performance under a wide range of hydrological and/or socio-economic hypothesis. Despite the high degree of acceptation and testing that these models have achieved, the actual operation of water resources systems hardly follows all the time the pre-defined rules with the consequent uncertainty on the system performance. Real-world reservoir operation is very complex, affected by input uncertainty (imprecision in forecast inflow, seepage and evaporation losses, etc.), filtered by the reservoir operator's experience and natural risk-aversion, while considering the different physical and legal/institutional constraints in order to meet the different demands and system requirements. The aim of this work is to expose a fuzzy logic approach to derive and assess the historical operation of a system. This framework uses a fuzzy rule-based system to reproduce pre-defined rules and also to match as close as possible the actual decisions made by managers. After built up, the fuzzy rule-based system can be integrated in a water resources management model, making possible to assess the system performance at the basin scale. The case study of the Mijares basin (eastern Spain) is used to illustrate the method. A reservoir operating curve regulates the two main reservoir releases (operated in a conjunctive way) with the purpose of guaranteeing a high realiability of supply to the traditional irrigation districts with higher priority (more senior demands that funded the reservoir construction). A fuzzy rule-based system has been created to reproduce the operating curve's performance, defining the system state (total
A PROPOSAL OF FUZZY MULTIDIMENSIONAL ASSOCIATION RULES
Directory of Open Access Journals (Sweden)
Rolly Intan
2006-01-01
Full Text Available Association rules that involve two or more dimensions or predicates can be referred as multidimensional association rules. Rather than searching for frequent itemsets (as is done in mining single-dimensional association rules, in multidimensional association rules, we search for frequent predicate sets. In general, there are two types of multidimensional association rules, namely interdimension association rules and hybrid-dimension association rules. Interdimension association rules are multidimensional association rules with no repeated predicates. This paper introduces a method for generating interdimension association rules. A more meaningful association rules can be provided by generalizing crisp value of attributes to be fuzzy value. To generate the multidimensional association rules implying fuzzy value, this paper introduces an alternative method for mining the rules by searching for the predicate sets.
Refining Linear Fuzzy Rules by Reinforcement Learning
Berenji, Hamid R.; Khedkar, Pratap S.; Malkani, Anil
1996-01-01
Linear fuzzy rules are increasingly being used in the development of fuzzy logic systems. Radial basis functions have also been used in the antecedents of the rules for clustering in product space which can automatically generate a set of linear fuzzy rules from an input/output data set. Manual methods are usually used in refining these rules. This paper presents a method for refining the parameters of these rules using reinforcement learning which can be applied in domains where supervised input-output data is not available and reinforcements are received only after a long sequence of actions. This is shown for a generalization of radial basis functions. The formation of fuzzy rules from data and their automatic refinement is an important step in closing the gap between the application of reinforcement learning methods in the domains where only some limited input-output data is available.
Perréard, S
1993-01-01
Many processes are controlled by experts using some kind of mental model to decide actions and make conclusions. This model, based on heuristic knowledge, can often be conveniently represented in rules and has not to be particularly accurate. This is the case for the problem of conditioning high voltage radio-frequency cavities: the expert has to decide, by observing some criteria, if he can increase or if he has to decrease the voltage and by how much. A program has been implemented which can be applied to a class of similar problems. The kernel of the program is a small rule base, which is independent of the kind of cavity. To model a specific cavity, we use fuzzy logic which is implemented as a separate routine called by the rule base. We use fuzzy logic to translate from numeric to symbolic information. The example we chose for applying this kind of technique can be implemented by sequential programming. The two versions exist for comparison. However, we believe that this kind of programming can be powerf...
Radiologist model for cardiac rest period determination based on fuzzy rule.
Arief, Zainal; Sato, Tetsuo; Okada, Tomohisa; Kuhara, Shigehide; Kanao, Shotaro; Togashi, Kaori; Minato, Kotaro
2010-01-01
Image data acquisition for the coronary arteries is generally implemented during the diastole rest period, in order to suppress blurring due to cardiac movement. The purpose of this study is to improve the semi-automated application to determine the cardiac rest period based on fuzzy logic. The cardiac rest period from 25 subjects were determined based on their normalized cross-correlation of consecutive frame images as well as normalized frame number as the measured variables. The fuzzy set and membership are generated based on the measured variables from the radiologist's visual assessment. That visual assessment is also regarded as a gold standard for verification. The distance difference between the proposed method and visual assessment was analyzed. The fuzzy logic approach for cardiac rest period determination has no significant difference compared to the visual assessment (p>0.05) in terms of start frame and end frame. The algorithm could be extended easily in case of there are some necessary variables should be added to accommodate rest period definition from different radiologist.
FUZZY MODELING BY SUCCESSIVE ESTIMATION OF RULES ...
African Journals Online (AJOL)
This paper presents an algorithm for automatically deriving fuzzy rules directly from a set of input-output data of a process for the purpose of modeling. The rules are extracted by a method termed successive estimation. This method is used to generate a model without truncating the number of fired rules, to within user ...
A new method for constructing membership functions and fuzzy rules from training examples.
Wu, T P; Chen, S M
1999-01-01
To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the alpha-cuts of equivalence relations and the alpha-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.
Jarre, Astrid; Paterson, Barbara; Moloney, Coleen L.; Miller, David C. M.; Field, John G.; Starfield, Anthony M.
2008-10-01
In an ecosystem approach to fisheries (EAF), management must draw on information of widely different types, and information addressing various scales. Knowledge-based systems assist in the decision-making process by summarising this information in a logical, transparent and reproducible way. Both rule-based Boolean and fuzzy-logic models have been used successfully as knowledge-based decision support tools. This study compares two such systems relevant to fisheries management in an EAF developed for the southern Benguela. The first is a rule-based system for the prediction of anchovy recruitment and the second is a fuzzy-logic tool to monitor implementation of an EAF in the sardine fishery. We construct a fuzzy-logic counterpart to the rule-based model, and a rule-based counterpart to the fuzzy-logic model, compare their results, and include feedback from potential users of these two decision support tools in our evaluation of the two approaches. With respect to the model objectives, no method clearly outperformed the other. The advantages of numerically processing continuous variables, and interpreting the final output, as in fuzzy-logic models, can be weighed up against the advantages of using a few, qualitative, easy-to-understand categories as in rule-based models. The natural language used in rule-based implementations is easily understood by, and communicated among, users of these systems. Users unfamiliar with fuzzy-set theory must “trust” the logic of the model. Graphical visualization of intermediate and end results is an important advantage of any system. Applying the two approaches in parallel improved our understanding of the model as well as of the underlying problems. Even for complex problems, small knowledge-based systems such as the ones explored here are worth developing and using. Their strengths lie in (i) synthesis of the problem in a logical and transparent framework, (ii) helping scientists to deliberate how to apply their science to
Tseng, Chris; Gupta, Pramod; Schumann, Johann
2006-01-01
The Cooper-Harper rating of Aircraft Handling Qualities has been adopted as a standard for measuring the performance of aircraft since it was introduced in 1966. Aircraft performance, ability to control the aircraft, and the degree of pilot compensation needed are three major key factors used in deciding the aircraft handling qualities in the Cooper- Harper rating. We formulate the Cooper-Harper rating scheme as a fuzzy rule-based system and use it to analyze the effectiveness of the aircraft controller. The automatic estimate of the system-level handling quality provides valuable up-to-date information for diagnostics and vehicle health management. Analyzing the performance of a controller requires a set of concise design requirements and performance criteria. Ir, the case of control systems fm a piloted aircraft, generally applicable quantitative design criteria are difficult to obtain. The reason for this is that the ultimate evaluation of a human-operated control system is necessarily subjective and, with aircraft, the pilot evaluates the aircraft in different ways depending on the type of the aircraft and the phase of flight. In most aerospace applications (e.g., for flight control systems), performance assessment is carried out in terms of handling qualities. Handling qualities may be defined as those dynamic and static properties of a vehicle that permit the pilot to fully exploit its performance in a variety of missions and roles. Traditionally, handling quality is measured using the Cooper-Harper rating and done subjectively by the human pilot. In this work, we have formulated the rules of the Cooper-Harper rating scheme as fuzzy rules with performance, control, and compensation as the antecedents, and pilot rating as the consequent. Appropriate direct measurements on the controller are related to the fuzzy Cooper-Harper rating system: a stability measurement like the rate of change of the cost function can be used as an indicator if the aircraft is under
Liu, Yan-Xin; Doctor, Faiyaz; Fan, Shou-Zen; Shieh, Jiann-Shing
2014-01-01
We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability.
Fan, Shou-Zen; Shieh, Jiann-Shing
2014-01-01
We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability. PMID:25587533
Fuzzy rule-based model for optimum orientation of solar panels using satellite image processing
International Nuclear Information System (INIS)
Zaher, A; Thiery, F; Grieu, S; Traoré, A; N’goran, Y
2017-01-01
In solar energy converting systems, a particular attention is paid to the orientation of solar collectors in order to optimize the overall system efficiency. In this context, the collectors can be fixed or oriented by a continuous solar tracking system. The proposed approach is based on METEOSAT images processing in order to detect the cloud coverage and its duration. These two parameters are treated by a fuzzy inference system deciding the optimal position of the solar panel. In fact, three weather cases can be considered: clear, partly covered or overcast sky. In the first case, the direct sunlight is more important than the diffuse radiation, thus the panel is always pointed towards the sun. In the overcast case, the solar beam is close to zero and the panel is placed horizontally to receive the diffuse radiation. Under partly covered conditions, the fuzzy inference system decides which of the previous positions is more efficient. The proposed approach is implemented using experimental prototype located in Perpignan (France). On a period of 17 months, the results are very satisfactory, with power gains of up to 23 % compared to the collectors oriented by a continuous solar tracking. (paper)
Fuzzy rule-based model for optimum orientation of solar panels using satellite image processing
Zaher, A.; N'goran, Y.; Thiery, F.; Grieu, S.; Traoré, A.
2017-01-01
In solar energy converting systems, a particular attention is paid to the orientation of solar collectors in order to optimize the overall system efficiency. In this context, the collectors can be fixed or oriented by a continuous solar tracking system. The proposed approach is based on METEOSAT images processing in order to detect the cloud coverage and its duration. These two parameters are treated by a fuzzy inference system deciding the optimal position of the solar panel. In fact, three weather cases can be considered: clear, partly covered or overcast sky. In the first case, the direct sunlight is more important than the diffuse radiation, thus the panel is always pointed towards the sun. In the overcast case, the solar beam is close to zero and the panel is placed horizontally to receive the diffuse radiation. Under partly covered conditions, the fuzzy inference system decides which of the previous positions is more efficient. The proposed approach is implemented using experimental prototype located in Perpignan (France). On a period of 17 months, the results are very satisfactory, with power gains of up to 23 % compared to the collectors oriented by a continuous solar tracking.
Fuzzy rule-based expert system for assessment severity of asthma.
Zolnoori, Maryam; Zarandi, Mohammad Hossein Fazel; Moin, Mostafa; Teimorian, Shahram
2012-06-01
Prescription medicine for asthma at primary stages is based on asthma severity level. Despite major progress in discovering various variables affecting asthma severity levels, disregarding some of these variables by physicians, variables' inherent uncertainty, and assigning patients to limited categories of decision making are the major causes of underestimating asthma severity, and as a result low quality of life in asthmatic patients. In this paper, we provide a solution of intelligence fuzzy system for this problem. Inputs of this system are organized in five modules of respiratory symptoms, bronchial obstruction, asthma instability, quality of life, and asthma severity. Output of this system is degree of asthma severity in score (0-10). Evaluating performance of this system by 28 asthmatic patients reinforces that the system's results not only correspond with evaluations of physicians, but represent the slight differences of asthmatic patients placed in specific category introduced by guidelines.
Directory of Open Access Journals (Sweden)
Hamid Reza Marateb
2015-01-01
Full Text Available Background: Coronary heart diseases/coronary artery diseases (CHDs/CAD, the most common form of cardiovascular disease (CVD, are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. Materials and Methods: In this study, the Cleveland CAD dataset from the University of California UCI (Irvine was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS methods, multiple logistic regression (MLR and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. Results: In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard for 272 subjects (68% male were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were "age and ST/heart rate slope categories," "exercise-induced angina status," fluoroscopy, and thallium-201 stress scintigraphy results. Conclusion: The proposed method showed "substantial agreement" with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.
A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories.
Vadivel, A; Surendiran, B
2013-05-01
We present new geometric shape and margin features for classifying mammogram mass lesions into BI-RADS shape categories: round, oval, lobular and irregular. According to Breast Imaging Reporting and Data System (BIRADS), masses can be differentiated using its shape, size and density, which is how radiologist visualizes the mammograms. Measuring regular and irregular shapes mathematically is found to be a difficult task, since there is no single measure available to differentiate various shapes. It is known that for mammograms, shape features are superior to Haralick and wavelet based features. Various geometrical shape and margin features have been introduced based on maximum and minimum radius of mass to classify the morphology of masses. These geometric features are found to be good in discriminating regular shapes from irregular shapes. In this paper, each mass is described by shape feature vector consists of 17 shape and margin properties. The masses are classified into 4 categories such as round, oval, lobular and irregular. Classifying masses into 4 categories is a very difficult task compared to classifying masses as benign, malignant or normal vs. abnormal. Only shape and margin characteristics can be used to discriminate these 4 categories effectively. Experiments have been conducted on mammogram images from the Digital Database for Screening Mammography (DDSM) and classified using C5.0 decision tree classifier. Total of 224 DDSM mammogram masses are considered for experiment. The C5.0 decision tree algorithm is used to generate simple rules, which can be easily implemented and used in fuzzy inference system as if…then…else statements. The rules are used to construct the generalized fuzzy membership function for classifying the masses as round, oval, lobular or irregular. Proposed approach is twice effective than existing Beamlet based features for classifying the mass as round, oval, lobular or irregular. Copyright © 2013 Elsevier Ltd. All rights
Marateb, Hamid Reza; Goudarzi, Sobhan
2015-03-01
Coronary heart diseases/coronary artery diseases (CHDs/CAD), the most common form of cardiovascular disease (CVD), are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. In this study, the Cleveland CAD dataset from the University of California UCI (Irvine) was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC) whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS) methods, multiple logistic regression (MLR) and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS) was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard) for 272 subjects (68% male) were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α) and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were "age and ST/heart rate slope categories," "exercise-induced angina status," fluoroscopy, and thallium-201 stress scintigraphy results. The proposed method showed "substantial agreement" with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.
Kim, Y J; Bae, H; Ko, J H; Poo, K M; Kim, S; Kim, C W; Woo, H J
2006-01-01
A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable.
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Ozen Dilek Nur
2016-01-01
Full Text Available Frost formation brings about insulating effects over the surface of a heat exchanger and thereby deteriorating total heat transfer of the heat exchanger. In this study, a fin-tube evaporator is modeled by making use of Rule-based Mamdani-Type Fuzzy (RBMTF logic where total heat transfer, air inlet temperature of 2 °C to 7 °C and four different fluid speed groups (ua1=1; 1.44; 1.88 m s-1, ua2=2.32; 2.76 m s-1, ua3=3.2; 3.64 m s-1, ua4=4.08; 4.52; 4.96 m s-1 for the evaporator were taken into consideration. In the developed RBMTF system, outlet parameter UA was determined using inlet parameters Ta and ua. The RBMTF was trained and tested by using MATLAB® fuzzy logic toolbox. R2 (% for the training data and test data were found to be 99.91%. With this study, it has been shown that RBMTF model can be reliably used in determination of a total heat transfer of a fin-tube evaporator.
Shieh, Jiann-Shing; Fu, Mu; Huang, Sheng-Jean; Kao, Ming-Chien
2006-08-01
This paper assesses the controller performance of a self-organizing fuzzy logic controller (SOFLC) in comparison with a routine clinical rule-base controller (RBC) for sedation control of intracranial pressure (ICP) pattern. Eleven patients with severe head injury undergoing different neurosurgeries in a neurosurgical intensive care unit (NICU) were divided into two groups. In all cases the sedation control periods lasted 1 h and assessments of propofol infusion rates were made at a frequency of once per 30 s. In the control group of 10 cases selected from 5 patients, a RBC was used, and in the experimental group of 10 cases selected from 6 patients, a self-organizing fuzzy logic controller was used. A SOFLC was derived from a fuzzy logic controller and allowed to generate new rules via self-learning beyond the initial fuzzy rule-base obtained from experts (i.e., neurosurgeons). The performance of the controllers was analyzed using the ICP pattern of sedation for 1 h of control. The results show that a SOFLC can provide a more stable ICP pattern by administering more propofol and changing the rate of delivery more often when rule-base modifications have been considered.
Using fuzzy association rule mining in cancer classification
International Nuclear Information System (INIS)
Mahmoodian, Hamid; Marhaban, M.H.; Abdulrahim, Raha; Rosli, Rozita; Saripan, Iqbal
2011-01-01
Full text: The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selec tion and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables
Using fuzzy association rule mining in cancer classification.
Mahmoodian, Hamid; Hamiruce Marhaban, M; Abdulrahim, Raha; Rosli, Rozita; Saripan, Iqbal
2011-04-01
The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selection and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables.
Directory of Open Access Journals (Sweden)
F.
2012-04-01
Full Text Available In this paper we propose a new approach for laser-based environment device control systems based on the automatic design of a Fuzzy Rule-Based System for laser pointer detection. The idea is to improve the success rate of the previous approaches decreasing as much as possible the false offs and increasing the success rate in images with laser spot, i.e., the detection of a false laser spot (since this could lead to dangerous situations. To this end, we propose to analyze both, the morphology and color of a laser spot image together, thus developing a new robust algorithm. Genetic Fuzzy Systems have also been employed to improve the laser spot system detection by means of a fine tuning of the involved membership functions thus reducing the system false offs, which is the main objective in this problem. The system presented in this paper, makes use of a Fuzzy Rule-Based System adjusted by a Genetic Algorithm, which, based on laser morphology and color analysis, shows a better success rate than previous approaches.
D-FLER - A Distributed Fuzzy Logic Engine for Rule-Based Wireless Sensor Networks
Marin Perianu, Mihai; Havinga, Paul J.M.
2007-01-01
We propose D-FLER, a distributed, general-purpose reasoning engine for WSN. D-FLER uses fuzzy logic for fusing individual and neighborhood observations, in order to produce a more accurate and reliable result. Thorough simulation, we evaluate D-FLER in a fire-detection scenario, using both fire and
D-FLER: A Distributed Fuzzy Logic Engine for Rule-based Wireless Sensor Networks
Marin Perianu, Mihai; Havinga, Paul J.M.
2007-01-01
We propose D-FLER, a distributed, general-purpose reasoning engine for WSN. D-FLER uses fuzzy logic for fusing individual and neighborhood observations, in order to produce a more accurate and reliable result. Thorough simulation, we evaluate D-FLER in a fire-detection scenario, using both fire and
Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models
Janssen, Judith; Krol, Martinus S.; Schielen, Ralph Mathias Johannes; Hoekstra, Arjen Ysbert; de Kok, Jean-Luc
2010-01-01
The coherence between different aspects in the environmental system leads to a demand for comprehensive models of this system to explore the effects of different management alternatives. Fuzzy logic has been suggested as a means to extend the application domain of environmental modelling from
Dynamic compensatory pattern matching in a fuzzy rule-based control system
Sun, Chuen-Tsai
1991-01-01
A dynamic compensatory matching procedure is suggested as a method to generate an aggregated measure for evaluating the appropriateness of rules for control systems. It is a dynamic weighted matching technique which takes into account incomplete information under real-time requirements. The initial weights of importance of variables are generated with a generalized neural network architecture and a gradient descent algorithm. An intuitive compensatory scheme based on correlations among input variables of training data is adopted so that the system is coherent to a noisy environment.
Design and Implementation an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
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Mohsen Taheri
2010-04-01
Full Text Available Research on humanoid robotics in Mechatronics and Automation Laboratory, Electrical and Computer Engineering, Islamic Azad University Khorasgan branch (Isfahan of Iran was started at
the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. This paper describes the hardware and software design of the kid size humanoid robot systems of the PERSIA Team in 2009. The robot has 20 actuated degrees of freedom based on Hitec HSR898. In this paper we have tried to focus on areas such as mechanical structure, Image processing unit, robot controller, Robot AI and behavior
learning. In 2009, our developments for the Kid size humanoid robot include: (1 the design and construction of our new humanoid robots (2 the design and construction of a new hardware and software controller to be used in our robots. The project is described in two main parts: Hardware and Software. The software is developed a robot application which consists walking controller, autonomous motion robot, self localization base on vision and Particle Filter, local AI, Trajectory Planning, Motion Controller and Network. The hardware consists of the mechanical structure and the driver circuit board. Each robot is able to walk, fast walk, pass, kick and dribble when it catches
the ball. These humanoids have been successfully participating in various robotic soccer competitions. This project is still in progress and some new interesting methods are described in the current report.
Z Number Based Fuzzy Inference System for Dynamic Plant Control
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Rahib H. Abiyev
2016-01-01
Full Text Available Frequently the reliabilities of the linguistic values of the variables in the rule base are becoming important in the modeling of fuzzy systems. Taking into consideration the reliability degree of the fuzzy values of variables of the rules the design of inference mechanism acquires importance. For this purpose, Z number based fuzzy rules that include constraint and reliability degrees of information are constructed. Fuzzy rule interpolation is presented for designing of an inference engine of fuzzy rule-based system. The mathematical background of the fuzzy inference system based on interpolative mechanism is developed. Based on interpolative inference process Z number based fuzzy controller for control of dynamic plant has been designed. The transient response characteristic of designed controller is compared with the transient response characteristic of the conventional fuzzy controller. The obtained comparative results demonstrate the suitability of designed system in control of dynamic plants.
An Intelligent Trading System with Fuzzy Rules and Fuzzy Capital Management
Naranjo, Rodrigo; Meco, Albert; Arroyo Gallardo, Javier; Santos Peñas, Matilde
2015-01-01
In this work we are proposing a trading system where fuzzy logic is applied not only for defining the trading rules, but also for managing the capital to invest. In fact, two fuzzy decision support systems are developed. The first one uses fuzzy logic to design the trading rules and to apply the stock market technical indicators. The second one enhances this fuzzy trading system adding a fuzzy strategy to manage the capital to trade. Additionally, a new technical market indicator that produce...
FuzzySRI-II A fuzzy rule induction algorithm for numerical output prediction
Afifi, A.
2014-01-01
Current inductive learning algorithms have difficulties handling attributes with numerical output values. This paper presents FuzzySRI-II, a new fuzzy rule induction algorithm for the prediction of numerical outputs. FuzzySRI-II integrates the comprehensibility and ease of application of rule induction algorithms with the uncertainty handling and approximate reasoning capabilities of fuzzy sets. The performance of the proposed FuzzySRI-II algorithm in two simulated control applications involv...
On the fusion of tuning parameters of fuzzy rules and neural network
Mamuda, Mamman; Sathasivam, Saratha
2017-08-01
Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.
Puhan, Pratap Sekhar; Ray, Pravat Kumar; Panda, Gayadhar
2016-12-01
This paper presents the effectiveness of 5/5 Fuzzy rule implementation in Fuzzy Logic Controller conjunction with indirect control technique to enhance the power quality in single phase system, An indirect current controller in conjunction with Fuzzy Logic Controller is applied to the proposed shunt active power filter to estimate the peak reference current and capacitor voltage. Current Controller based pulse width modulation (CCPWM) is used to generate the switching signals of voltage source inverter. Various simulation results are presented to verify the good behaviour of the Shunt active Power Filter (SAPF) with proposed two levels Hysteresis Current Controller (HCC). For verification of Shunt Active Power Filter in real time, the proposed control algorithm has been implemented in laboratory developed setup in dSPACE platform.
Developing a fuzzy rule based cognitive map for total system safety assessment
International Nuclear Information System (INIS)
Lemos, Francisco Luiz de; Sullivan, Terry
2007-01-01
Total System Performance Assessment, TSPA, for radioactive waste disposal is a multi and interdisciplinary task that is characterized by complex interactions between parameters and processes; lack of data; and ignorance regarding natural processes and conditions. The vagueness in the determination of ranges of values of parameters and identification of interacting processes pose further difficulties to the analysts with regard to the establishment of the relations between processes and parameters. More specifically the vagueness makes uncertainty propagation and sensitivity analysis challenging to analyze. To cope with these difficulties experts often use simplifications and linguistic terms to express their state of knowledge about a certain situation. For example, experts use terms such as 'low pH', 'very unlikely', etc to describe their perception about natural processes or conditions. In this work we propose the use of Fuzzy Cognitive Maps, FCM, for representation of interrelation between processes and parameters as well as to promote a better understanding of the system performance. Fuzzy cognitive maps are suited for the case where the causal relations are not clearly defined and, therefore, can not be represented by crisp values. In other words, instead of representing the quality of the interactions by crisp values, they are assigned degrees of truth. For example, we can assign values to the effect of one process on another such that (+) 1 corresponds to positive, (-) 1 to negative and 0 to neutral effects respectively. In this case the effect of a process A, on a process, B, can be depicted as function of the membership to the fuzzy set 'causal effect' of the cause process to the target one. One of the main advantages of this methodology would be that it allows one to aggregate the linguistic expressions as descriptions of processes. For example, a process can be known to have a 'very strong' positive effect on another one, or using fuzzy sets terminology
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Ajay Khunteta
2016-01-01
Full Text Available Active contour models, colloquially known as snakes, are quite popular for several applications such as object boundary detection, image segmentation, object tracking, and classification via energy minimization. While energy minimization may be accomplished using traditional optimization methods, approaches based on nature-inspired evolutionary algorithms have been developed in recent years. One such evolutionary algorithm that has been used extensively in active contours is the particle swarm optimization (PSO. However, conventional PSO converges slowly and gets trapped in local minimum easily which results in inaccurate detection of concavities in the object boundary. This is taken care of by using proposed multiswarm PSO in which a swarm is set for every control point in the snake and then all the swarms search for their best points simultaneously through information sharing among them. The performance of the multiswarm PSO-based search process is further enhanced by using dynamic adaptation of the inertia factor. In this paper, we propose using a set of fuzzy rules to adjust the inertia weight on the basis of the current normalized snake energy and the current value of inertia. Experimental results demonstrate the effectiveness of the proposed method compared to conventional approaches.
A fuzzy rule based remedial priority ranking system for contaminated sites.
Polat, Sener; Aksoy, Aysegul; Unlu, Kahraman
2015-01-01
Contaminated site remediation is generally difficult, time consuming, and expensive. As a result ranking may aid in efficient allocation of resources. In order to rank the priorities of contaminated sites, input parameters relevant to contaminant fate and transport, and exposure assessment should be as accurate as possible. Yet, in most cases these parameters are vague or not precise. Most of the current remediation priority ranking methodologies overlook the vagueness in parameter values or do not go beyond assigning a contaminated site to a risk class. The main objective of this study is to develop an alternative remedial priority ranking system (RPRS) for contaminated sites in which vagueness in parameter values is considered. RPRS aims to evaluate potential human health risks due to contamination using sufficiently comprehensive and readily available parameters in describing the fate and transport of contaminants in air, soil, and groundwater. Vagueness in parameter values is considered by means of fuzzy set theory. A fuzzy expert system is proposed for the evaluation of contaminated sites and a software (ConSiteRPRS) is developed in Microsoft Office Excel 2007 platform. Rankings are employed for hypothetical and real sites. Results show that RPRS is successful in distinguishing between the higher and lower risk cases. © 2014, National Ground Water Association.
A fuzzy rule-based expert system for diagnosing cystic fibrosis.
Hassanzad, Maryam; Orooji, Azam; Valinejadi, Ali; Velayati, Aliakbar
2017-12-01
Finding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients. The objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF). Defining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment. Results showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface. The system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.
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Nurul Haqimin Mohd Salleh
2017-07-01
Full Text Available One of the biggest concerns in liner operations is punctuality of containerships. Managing the time factor has become a crucial issue in today's liner shipping operations. A statistic in 2015 showed that the overall punctuality for containerships only reached an on-time performance of 73%. However, vessel punctuality is affected by many factors such as the port and vessel conditions and knock-on effects of delays. As a result, this paper develops a model for analyzing and predicting the arrival punctuality of a liner vessel at ports of call under uncertain environments by using a hybrid decision-making technique, the Fuzzy Rule-Based Bayesian Network (FRBBN. In order to ensure the practicability of the model, two container vessels have been tested by using the proposed model. The results have shown that the differences between prediction values and real arrival times are only 4.2% and 6.6%, which can be considered as reasonable. This model is capable of helping liner shipping operators (LSOs to predict the arrival punctuality of their vessel at a particular port of call.
Fuzzy rule-based expert system for evaluating level of asthma control.
Zolnoori, Maryam; Fazel Zarandi, Mohammad Hosain; Moin, Mostafa; Taherian, Mehran
2012-10-01
Asthma control is a final goal of asthma therapy process. Despite outstanding progress in discovering various variables affecting asthma control levels, disregarding some of them by physicians and variables' inherent uncertainty are the major causes of underestimating of asthma control levels and as a result asthma morbidity and mortality. In this paper, we provide an intelligent fuzzy system as a solution for this problem. Inputs of this system are composed of 14 variables organized in five modules of respiratory symptoms severity, bronchial obstruction, asthma instability, current treatment, and quality of life. Output of this system is degree of asthma control defined in the score (0-10). Evaluation of performance of this system by 42 asthmatic patients at asthma, allergy, immunology research center of Emam Khomeini hospital, Tehran, Iran reinforces that the system's results not only correspond with the evaluations of experienced asthma physicians, but represents slight differences in the levels of asthma control between asthmatic patients.
FPGA Based Modified Fuzzy PID Controller for Pitch Angle of Bench-top Helicopter
A.A. Aldair
2012-01-01
Fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. To reduce the huge number of fuzzy rules required in the normal design for fuzzy PID controller, the fuzzy PID controller is represented as Proportional-Derivative Fuzzy (PDF) controller and Proportional-Integral Fuzzy (PIF) controller connected in parallel through a summer. The PIF controller design has been simplified by replacing the PIF controller by ...
Implementasi Rule Base System dan Fuzzy Logic Artifical Intelligence pada Game Kartu Capsa
Pangkatodi, Edo; Liliana, Liliana; Budhi, Gregorius Satia
2016-01-01
In the era of globalization today, science and technology is developing very fast, particularly in entertainment media, specifically in the gaming world. Today, games are not only used as an entertainment, but also can be used as an alternative in the world of work, education, and even sports. In the world of gaming, artificial intelligence, or AI is a factor that cannot be separated. With the right methods and the specific rules of the AI can walk like a human being doing a job. So it is not...
Exarchos, Themis P; Tsipouras, Markos G; Exarchos, Costas P; Papaloukas, Costas; Fotiadis, Dimitrios I; Michalis, Lampros K
2007-07-01
In the current work we propose a methodology for the automated creation of fuzzy expert systems, applied in ischaemic and arrhythmic beat classification. The proposed methodology automatically creates a fuzzy expert system from an initial training dataset. The approach consists of three stages: (a) extraction of a crisp set of rules from a decision tree induced from the training dataset, (b) transformation of the crisp set of rules into a fuzzy model and (c) optimization of the fuzzy model's parameters using global optimization. The above methodology is employed in order to create fuzzy expert systems for ischaemic and arrhythmic beat classification in ECG recordings. The fuzzy expert system for ischaemic beat detection is evaluated in a cardiac beat dataset that was constructed using recordings from the European Society of Cardiology ST-T database. The arrhythmic beat classification fuzzy expert system is evaluated using the MIT-BIH arrhythmia database. The fuzzy expert system for ischaemic beat classification reported 91% sensitivity and 92% specificity. The arrhythmic beat classification fuzzy expert system reported 96% average sensitivity and 99% average specificity for all categories. The proposed methodology provides high accuracy and the ability to interpret the decisions made. The fuzzy expert systems for ischaemic and arrhythmic beat classification compare well with previously reported results, indicating that they could be part of an overall clinical system for ECG analysis and diagnosis.
International Nuclear Information System (INIS)
Kim, Han Gon
1993-02-01
In pressurized water reactors, the fuel reloading problem has significant meaning in terms of both safety and economic aspects. Therefore the general problem of incore fuel management for a PWR consists of determining the fuel reloading policy for each cycle that minimize unit energy cost under the constraints imposed on various core parameters, e.g., a local power peaking factor and an assembly burnup. This is equivalent that a cycle length is maximized for a given energy cost under the various constraints. Existing optimization methods do not ensure the global optimum solution because of the essential limitation of their searching algorithms. They only find near optimal solutions. To solve this limitation, a hybrid artificial neural network system is developed for the optimal fuel loading pattern design using a fuzzy rule based system and an artificial neural networks. This system finds the patterns that P max is lower than the predetermined value and K eff is larger than the reference value. The back-propagation networks are developed to predict PWR core parameters. Reference PWR is an 121-assembly typical PWR. The local power peaking factor and the effective multiplication factor at BOC condition are predicted. To obtain target values of these two parameters, the QCC code are used. Using this code, 1000 training patterns are obtained, randomly. Two networks are constructed, one for P max and another for K eff Both of two networks have 21 input layer neurons, 18 output layer neurons, and 120 and 393 hidden layer neurons, respectively. A new learning algorithm is proposed. This is called the advanced adaptive learning algorithm. The weight change step size of this algorithm is optimally varied inversely proportional to the average difference between an actual output value and an ideal target value. This algorithm greatly enhances the convergence speed of a BPN. In case of P max prediction, 98% of the untrained patterns are predicted within 6% error, and in case
Adaptive Measurement-Based Policy-Driven QoS Management with Fuzzy-Rule-based Resource Allocation
Directory of Open Access Journals (Sweden)
Philip J. Morrow
2012-07-01
Full Text Available Fixed and wireless networks are increasingly converging towards common connectivity with IP-based core networks. Providing effective end-to-end resource and QoS management in such complex heterogeneous converged network scenarios requires unified, adaptive and scalable solutions to integrate and co-ordinate diverse QoS mechanisms of different access technologies with IP-based QoS. Policy-Based Network Management (PBNM is one approach that could be employed to address this challenge. Hence, a policy-based framework for end-to-end QoS management in converged networks, CNQF (Converged Networks QoS Management Framework has been proposed within our project. In this paper, the CNQF architecture, a Java implementation of its prototype and experimental validation of key elements are discussed. We then present a fuzzy-based CNQF resource management approach and study the performance of our implementation with real traffic flows on an experimental testbed. The results demonstrate the efficacy of our resource-adaptive approach for practical PBNM systems.
Chen, Shyi-Ming; Hsin, Wen-Chyuan
2015-07-01
In this paper, we propose a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the slopes of fuzzy sets. We also propose a particle swarm optimization (PSO)-based weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of fuzzy rules for weighted fuzzy interpolative reasoning. We apply the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm to deal with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm outperforms the existing methods for dealing with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems.
On structuring the rules of a fuzzy controller
Zhou, Jun; Raju, G. V. S.
1993-01-01
Since the pioneering work of Zadeh and Mamdani and Assilian, fuzzy logic control has emerged as one of the most active and fruitful research areas. The applications of fuzzy logic control can be found in many fields such as control of stream generators, automatic train operation systems, elevator control, nuclear reactor control, automobile transmission control, etc. In this paper, two new structures of hierarchical fuzzy rule-based controller are proposed to reduce the number of rules in a complete rule set of a controller. In one approach, the overall system is split into sub-systems which are treated independently in parallel. A coordinator is then used to take into account the interactions. This is done via an iterating information exchange between the lower level and the coordinator level. From the point of view of information used, this structure is very similar to central structure in that the coordinator can have at least in principle, all the information that the local controllers have.
Fuzzy Logic Based Automatic Door Control System
Directory of Open Access Journals (Sweden)
Harun SUMBUL
2017-12-01
Full Text Available In this paper, fuzzy logic based an automatic door control system is designed to provide for heat energy savings. The heat energy loss usually occurs in where outomotic doors are used. Designed fuzzy logic system’s Input statuses (WS: Walking Speed and DD: Distance Door and the output status (DOS: Door Opening Speed is determined. According to these cases, rule base (25 rules is created; the rules are processed by a fuzzy logic and by appyled to control of an automatic door. An interface program is prepared by using Matlab Graphical User Interface (GUI programming language and some sample results are checked on Matlab using fuzzy logic toolbox. Designed fuzzy logic controller is tested at different speed cases and the results are plotted. As a result; in this study, we have obtained very good results in control of an automatic door with fuzzy logic. The results of analyses have indicated that the controls performed with fuzzy logic provided heat energy savings, less heat energy loss and reliable, consistent controls and that are feasible to in real.
A hierarchical structure for representing and learning fuzzy rules
Yager, Ronald R.
1993-01-01
Yager provides an example in which the flat representation of fuzzy if-then rules leads to unsatisfactory results. Consider a rule base consisting to two rules: if U is 12 the V is 29; if U is (10-15) the V is (25-30). If U = 12 we would get V is G where G = (25-30). The application of the defuzzification process leads to a selection of V = 27.5. Thus we see that the very specific instruction was not followed. The problem with the technique used is that the most specific information was swamped by the less specific information. In this paper we shall provide for a new structure for the representation of fuzzy if-then rules. The representational form introduced here is called a Hierarchical Prioritized Structure (HPS) representation. Most importantly in addition to overcoming the problem illustrated in the previous example this HPS representation has an inherent capability to emulate the learning of general rules and provides a reasonable accurate cognitive mapping of how human beings store information.
Measuring uncertainty by extracting fuzzy rules using rough sets
Worm, Jeffrey A.
1991-01-01
Despite the advancements in the computer industry in the past 30 years, there is still one major deficiency. Computers are not designed to handle terms where uncertainty is present. To deal with uncertainty, techniques other than classical logic must be developed. The methods are examined of statistical analysis, the Dempster-Shafer theory, rough set theory, and fuzzy set theory to solve this problem. The fundamentals of these theories are combined to possibly provide the optimal solution. By incorporating principles from these theories, a decision making process may be simulated by extracting two sets of fuzzy rules: certain rules and possible rules. From these rules a corresponding measure of how much these rules is believed is constructed. From this, the idea of how much a fuzzy diagnosis is definable in terms of a set of fuzzy attributes is studied.
Influence of fuzzy norms and other heuristics on “Mixed fuzzy rule formation”
Gabriel, Thomas R.; Berthold, Michael R.
2004-01-01
In Mixed Fuzzy Rule Formation [Int. J. Approx. Reason. 32 (2003) 67] a method to extract mixed fuzzy rules from data was introduced. The underlying algorithm s performance is influenced by the choice of fuzzy t-norm and t-conorm, and a heuristic to avoid conflicts between patterns and rules of different classes throughout training. In the following addendum to [Int. J. Approx. Reason. 32 (2003) 67], we discuss in more depth how these parameters affect the generalization performance of the res...
DecisionMaker software and extracting fuzzy rules under uncertainty
Walker, Kevin B.
1992-01-01
Knowledge acquisition under uncertainty is examined. Theories proposed in deKorvin's paper 'Extracting Fuzzy Rules Under Uncertainty and Measuring Definability Using Rough Sets' are discussed as they relate to rule calculation algorithms. A data structure for holding an arbitrary number of data fields is described. Limitations of Pascal for loops in the generation of combinations are also discussed. Finally, recursive algorithms for generating all possible combination of attributes and for calculating the intersection of an arbitrary number of fuzzy sets are presented.
Gorai, A. K.; Hasni, S. A.; Iqbal, Jawed
2016-11-01
Groundwater is the most important natural resource for drinking water to many people around the world, especially in rural areas where the supply of treated water is not available. Drinking water resources cannot be optimally used and sustained unless the quality of water is properly assessed. To this end, an attempt has been made to develop a suitable methodology for the assessment of drinking water quality on the basis of 11 physico-chemical parameters. The present study aims to select the fuzzy aggregation approach for estimation of the water quality index of a sample to check the suitability for drinking purposes. Based on expert's opinion and author's judgement, 11 water quality (pollutant) variables (Alkalinity, Dissolved Solids (DS), Hardness, pH, Ca, Mg, Fe, Fluoride, As, Sulphate, Nitrates) are selected for the quality assessment. The output results of proposed methodology are compared with the output obtained from widely used deterministic method (weighted arithmetic mean aggregation) for the suitability of the developed methodology.
Fuzzy logic based robotic controller
Attia, F.; Upadhyaya, M.
1994-01-01
Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.
International Nuclear Information System (INIS)
Kang, Ki Sig
1995-02-01
The on - line Operator Aid SYStem (OASYS) has been developed to support operator's decision making process and to ensure the safety of nuclear power plants (NPPs) by timely providing operators with proper guidelines according to the plant operation mode. The OASYS consists of four systems such as the signal validation and management system (SVMS), the plant monitoring system (PMS), the alarm filtering and diagnostic system (AFDS), and the dynamic emergency procedure tracking system (DEPTS). The SVMS and the PMS help operators to maintain a plant as a normal operation condition. The AFDS covers the abnormal events until they result in exceeding the limit range of reactor trip signals, while after a reactor trip, the DEPTS aids operators with proper guidelines so as to shutdown safely. The OASYS uses a rule based expert system and a fuzzy logic. The rule based expert system is used to classify the pre-defined events and track the emergency operating procedures (EOPs) through data processing. The fuzzy logic is used to generate the conceptual high level alarms for the prognostic diagnosis and to evaluate the qualitative fuzzy criteria used in EOPs. Performance assessment of the OASYS demonstrates that it is capable of diagnosing plant abnormal conditions and providing operators appropriate guidelines with fast response time and consistency. The developed technology for OASYS will be used to design the Integrated Advanced Control Room in which a plant can be operated by one operator during normal operation. The advanced EOP for emergency operation has been developed by focusing attention on the importance of the operators' role in emergency conditions. To overcome the complexity of current EOPs and maintain the consistency of operators' action according to plant emergency conditions, operator's tasks were allocated according to their duties in the advanced EOP and the computerized operator aid system (COAS) has been developed as an alternative to reduce operator
Zolnoori, Maryam; Zarandi, Mohammad Hossein Fazel; Moin, Mostafa
2012-08-01
This paper discusses the capacities of artificial intelligence in the process of asthma diagnosing and asthma treatment. Developed intelligent systems for asthma disease have been classified in five categories including diagnosing, evaluating, management, communicative facilities, and prediction. Considering inputs, results, and methodologies of the systems show that by focusing on meticulous analysis of quality of life as an input variable and developing patient-based systems, under-diagnosing and asthma morbidity and mortality would decrease significantly. Regard to the importance of accurate evaluation in accurate prescription and expeditious treatment, the methodology of developing a fuzzy expert system for evaluating level of asthma exacerbation is presented in this paper too. The performance of this system has been tested in Asthma, Allergy, and Immunology Center of Iran using 25 asthmatic patients. Comparison between system's results and physicians' evaluations using Kappa coefficient (K) reinforces the value of K = 1. In addition this system assigns a degree in gradation (0-10) to every patient representing the slight differences between patients assigned to a specific category.
A Robustness Study of Fuzzy Control Rules
DEFF Research Database (Denmark)
Jantzen, Jan
1997-01-01
This simulation study investigates how different types of rule bases affect the control of different types of plant. In Simulink three nonlinear control surfaces have been tested and compared to a linear surface. It is recommended to be aware of the shape of the control surface, and carefully sel...
Adding dynamic rules to self-organizing fuzzy systems
Buhusi, Catalin V.
1992-01-01
This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.
Postmodern Fuzzy System Theory: A Deconstruction Approach Based on Kabbalah
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Gabriel Burstein
2014-11-01
Full Text Available Modern general system theory proposed a holistic integrative approach based on input-state-output dynamics as opposed to the traditional reductionist detail based approach. Information complexity and uncertainty required a fuzzy system theory, based on fuzzy sets and fuzzy logic. While successful in dealing with analysis, synthesis and control of technical engineering systems, general system theory and fuzzy system theory could not fully deal with humanistic and human-like intelligent systems which combine technical engineering components with human or human-like components characterized by their cognitive, emotional/motivational and behavioral/action levels of operation. Such humanistic systems are essential in artificial intelligence, cognitive and behavioral science applications, organization management and social systems, man-machine systems or human factor systems, behavioral knowledge based economics and finance applications. We are introducing here a “postmodern fuzzy system theory” for controlled state dynamics and output fuzzy systems and fuzzy rule based systems using our earlier postmodern fuzzy set theory and a Kabbalah possible worlds model of modal logic and semantics type. In order to create a postmodern fuzzy system theory, we “deconstruct” a fuzzy system in order to incorporate in it the cognitive, emotional and behavioral actions and expressions levels characteristic for humanistic systems. Kabbalah offers a structural, fractal and hierarchic model for integrating cognition, emotions and behavior. We obtain a canonic deconstruction for a fuzzy system into its cognitive, emotional and behavioral fuzzy subsystems.
Chen, Wei-Ling; Kan, Chung-Dann; Lin, Chia-Hung; Chen, Tainsong
2014-03-01
This paper proposes a rule-based decision-making diagnosis system to evaluate arteriovenous shunt (AVS) stenosis for long-term hemodialysis treatment of patients using fuzzy petri nets (FPNs). AVS stenoses are often associated with blood sounds, resulting from turbulent flow over the narrowed blood vessel. Phonoangiography provides a noninvasive technique to monitor the sounds of the AVS. Since the power spectra changes in frequency and amplitude with the degree of AVS stenosis, it is difficult to make a human-made decision to judge the degree using a combination of those variances. The Burg autoregressive (AR) method is used to estimate the frequency spectra of a phonoangiographic signal and identify the characteristic frequencies. A rule-based decision-making method, FPNs, is designed as a decision-making system to evaluate the degree of stenosis (DOS) in routine examinations. For 42 long-term follow-up patients, the examination results show the proposed diagnosis system has greater efficiency in evaluating AVS stenosis.
Fuzzy association rules for biological data analysis: a case study on yeast.
Lopez, Francisco J; Blanco, Armando; Garcia, Fernando; Cano, Carlos; Marin, Antonio
2008-02-19
Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data. In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones. An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters.
Fuzzy association rules for biological data analysis: A case study on yeast
Directory of Open Access Journals (Sweden)
Cano Carlos
2008-02-01
Full Text Available Abstract Background Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data. Results In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones. Conclusion An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters.
Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning
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Ya’nan Wang
2016-01-01
Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.
Fuzzy Reasoning with a Rete-OO Rule Engine
Wulff, Nikolaus; Sottara, Davide
Rules and rule engines play an important role in automated decision making processes like business workflows or system monitoring. Classical inference machines evaluate rules until a final "yes" or "no" decision: this crisp classification schema can turn into a deficiency when they have to deal with uncertain or inprecise knowledge. To circumvent some of these limitations we have built the "Java Expert Fuzzy Inference System" (Jefis) and implemented factory methods to deploy the Jefis library as an extension for the classical rule engine JBoss Drools. We outline the new features and give examples of uncertain formulated rules executing within the Jefis Drools extender.
Generation of Fuzzy Rules by Subtractive Clustering
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Hussen Ateya Lafta
2017-12-01
Full Text Available This work depends on two stages. First one, "subtractive method", clustering algorithm, used for identifying the relationships between data points in order to build system, where the data point gathers with other points to make cluster of the same features. These groups will be used in the second part of the work to construct fuzzy IF…THEN rules, which controls how the system works. The number of rules and its parts depend on these clusters. While the Takagi-Sugeno Kang (TSK fuzzy inference modal was used. The scope of this work is applied to heart disease diagnosis.
PENERAPAN FUZZY IF-THEN RULES UNTUK PENINGKATAN KONTRAS PADA CITRA HASIL MAMMOGRAFI
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Helmy Thendean
2008-01-01
Full Text Available In medical area, the quality of an image which is acquired from mammography often has a poor contrast. The poor quality image leads a difficulty for a radiologist to analyze the image. The problem becomes bigger when the image contains a cancer or tumor. There are some methods in image processing technique to increase the contrast quality of an image. This paper presents Fuzzy IF-THEN Rules method which has four knowledge base approaches to increase the contrast quality of the image, especially breast images from mammography. To determine the success rate, this experiment tries to compare this method with a standard contrast improvement such as histogram equalization. The quantity parameters to compare these methods are linier index of fuzziness and fuzzy entropy. The result shows that Fuzzy IF-THEN Rules offers better result to improve the contrast quality than standard method. The result of this experiment is validated by an expert from radiology department from Husada Hospital, Jakarta. Abstract in Bahasa Indonesia : Citra hasil dari mammografi dalam dunia kedokteran sering memiliki kualitas yang buruk dari sisi kontras. Hal ini mengakibatkan kesulitan bagi seorang radiolog untuk menganalisis citra tersebut. Tingkat kesulitan bertambah apabila citra yang harus dianalisis tersebut mengandung kanker atau tumor. Terdapat beberapa metode untuk peningkatan kualitas kontras sebuah citra. Penelitian ini menggunakan metode Fuzzy IF-THEN Rules dengan empat pendekatan basis pengetahuan untuk meningkatkan kualitas kontras citra, khususnya citra payudara yang diperoleh dari hasil mammografi. Untuk menentukan tingkat keberha-silannya, metode tersebut akan dibandingkan dengan metode standar untuk peningkatan kontras seperti Histogram Equalization. Parameter yang digunakan untuk membandingkan setiap metode tersebut adalah linier index of fuzziness dan fuzzy entropy. Hasil percobaan menunjukkan bahwa Fuzzy IF-THEN Rules mampu menghasilkan hasil peningkatan
Reduction of false positives by extracting fuzzy rules from data for polyp detection in CTC scans
Siddique, Musib M.; Zheng, Yalin; Yang, Xiaoyun; Beddoe, Gareth
2008-03-01
This paper presents an adaptive neural network based Fuzzy Inference System (ANFIS) to reduce the false positive (FP) rate of detected colonic polyps in Computed Tomography Colonography (CTC) scans. Extracted fuzzy rules establish linguistically interpretable relationships in the data that are easy to understand, validate, and extend. The system takes several features identified from regions extracted by a segmentation algorithm and decides whether the regions are true polyps. In the training phase, subtractive clustering is used to down-sample the negative regions in order to get balanced data. The rule extraction method is based on estimating clusters in the data using the subtractive clustering algorithm; each cluster obtained corresponds to a fuzzy rule that maps a region in the input space to an output class. After the number of rules and initial rule parameters are obtained by cluster estimation, the rule parameters are optimized using a hybrid learning algorithm which is a combination of least-squares estimation with back propagation. The evolved Sugeno-type FIS has been tested on a total of 129 scans with 99 polyps of sizes 5-15 mm by experienced radiologists. The results indicate that for 93% detection sensitivity (on polyps), the evolved FIS method is able to remove 88% of FPs generated by the segmentation algorithm leaving 7.5 FP per scan. The high sensitivity rate of our results show the promise of neuro-fuzzy classifiers as an aid for interpreting CTC examinations.
Correntropy-Based Evolving Fuzzy Neural System
Bao, Rongjing; Rong, Haijun; Angelov, Plamen Parvanov; Chen, Badong; Wong, Pak Kin
2017-01-01
In this paper, a correntropy-based evolving fuzzy neural system (correntropy-EFNS) is proposed for approximation of nonlinear systems. Different from the commonly used meansquare error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build EFNS based on the correntropy concept to achieve a more stable evolution of the rule base and update of t...
Fuzzy Logic Based Autonomous Traffic Control System
Directory of Open Access Journals (Sweden)
Muhammad ABBAS
2012-01-01
Full Text Available The aim of this paper is to design and implement fuzzy logic based traffic light Control system to solve the traffic congestion issues. In this system four input parameters: Arrival, Queue, Pedestrian and Emergency Vehicle and two output parameters: Extension in Green and Pedestrian Signals are used. Using Fuzzy Rule Base, the system extends or terminates the Green Signal according to the Traffic situation at the junction. On the presence of emergency vehicle, the system decides which signal(s should be red and how much an extension should be given to Green Signal for Emergency Vehicle. The system also monitors the density of people and makes decisions accordingly. In order to verify the proposed design algorithm MATLAB simulation is adopted and results obtained show concurrency to the calculated values according to the Mamdani Model of the Fuzzy Control System.
Evolving fuzzy rules for relaxed-criteria negotiation.
Sim, Kwang Mong
2008-12-01
In the literature on automated negotiation, very few negotiation agents are designed with the flexibility to slightly relax their negotiation criteria to reach a consensus more rapidly and with more certainty. Furthermore, these relaxed-criteria negotiation agents were not equipped with the ability to enhance their performance by learning and evolving their relaxed-criteria negotiation rules. The impetus of this work is designing market-driven negotiation agents (MDAs) that not only have the flexibility of relaxing bargaining criteria using fuzzy rules, but can also evolve their structures by learning new relaxed-criteria fuzzy rules to improve their negotiation outcomes as they participate in negotiations in more e-markets. To this end, an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules was developed. Implementing the idea in a testbed, two kinds of experiments for evaluating and comparing EvEMDAs (MDAs with relaxed-criteria rules that are evolved using the evolutionary algorithm) and EMDAs (MDAs with relaxed-criteria rules that are manually constructed) were carried out through stochastic simulations. Empirical results show that: 1) EvEMDAs generally outperformed EMDAs in different types of e-markets and 2) the negotiation outcomes of EvEMDAs generally improved as they negotiated in more e-markets.
Fuzzy-based HAZOP study for process industry
Energy Technology Data Exchange (ETDEWEB)
Ahn, Junkeon; Chang, Daejun, E-mail: djchang@kaist.edu
2016-11-05
Highlights: • HAZOP is the important technique to evaluate system safety and its risks while process operations. • Fuzzy theory can handle the inherent uncertainties of process systems for the HAZOP. • Fuzzy-based HAZOP considers the aleatory and epistemic uncertainties and provides the risk level with less uncertainty. • Risk acceptance criteria should be considered regarding the transition region for each risk. - Abstract: This study proposed a fuzzy-based HAZOP for analyzing process hazards. Fuzzy theory was used to express uncertain states. This theory was found to be a useful approach to overcome the inherent uncertainty in HAZOP analyses. Fuzzy logic sharply contrasted with classical logic and provided diverse risk values according to its membership degree. Appropriate process parameters and guidewords were selected to describe the frequency and consequence of an accident. Fuzzy modeling calculated risks based on the relationship between the variables of an accident. The modeling was based on the mean expected value, trapezoidal fuzzy number, IF-THEN rules, and the center of gravity method. A cryogenic LNG (liquefied natural gas) testing facility was the objective process for the fuzzy-based and conventional HAZOPs. The most significant index is the frequency to determine risks. The comparison results showed that the fuzzy-based HAZOP provides better sophisticated risks than the conventional HAZOP. The fuzzy risk matrix presents the significance of risks, negligible risks, and necessity of risk reduction.
Assessment of the Degree of Consistency of the System of Fuzzy Rules
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Pospelova Lyudmila Yakovlevna
2013-12-01
Full Text Available The article analyses recent achievements and publications and shows that difficulties of explaining the nature of fuzziness and equivocation arise in socio-economic models that use the traditional paradigm of classical rationalism (computational, agent and econometric models. The accumulated collective experience of development of optimal models confirms prospectiveness of application of the fuzzy set approach in modelling the society. The article justifies the necessity of study of the nature of inconsistency in fuzzy knowledge bases both on the generalised ontology level and on pragmatic functional level of the logical inference. The article offers the method of search for logical and conceptual contradictions in the form of a combination of the abduction and modus ponens. It discusses the key issue of the proposed method: what properties should have the membership function of the secondary fuzzy set, which describes in fuzzy inference models such a resulting state of the object of management, which combines empirically incompatible properties with high probability. The degree of membership of the object of management in several incompatible classes with respect to the fuzzy output variable is the degree of fuzziness of the “Intersection of all results of the fuzzy inference of the set, applied at some input of rules, is an empty set” statement. The article describes an algorithm of assessment of the degree of consistency. It provides an example of the step-by-step detection of contradictions in statistical fuzzy knowledge bases at the pragmatic functional level of the logical output. The obtained results of testing in the form of sets of incompatible facts, output chains, sets of non-crossing intervals and computed degrees of inconsistency allow experts timely elimination of inadmissible contradictions and, at the same time, increase of quality of recommendations and assessment of fuzzy expert systems.
Simultaneous structure identification and fuzzy rule generation for Takagi-Sugeno models.
Pal, Nikhil R; Saha, Seemanti
2008-12-01
One of the main attractions of a fuzzy rule-based system is its interpretability which is hindered severely with an increase in the dimensionality of the data. For high-dimensional data, the identification of fuzzy rules also possesses a big challenge. Feature selection methods often ignore the subtle nonlinear interaction that the features and the learning system can have. To address this problem of structure identification, we propose an integrated method that can find the bad features simultaneously when finding the rules from data for Takagi-Sugeno-type fuzzy systems. It is an integrated learning mechanism that can take into account the nonlinear interactions that may be present between features and between features and fuzzy rule-based systems. Hence, it can pick up a small set of useful features and generate useful rules for the problem at hand. Such an approach is computationally very attractive because it is not iterative in nature like the forward or backward selection approaches. The effectiveness of the proposed approach is demonstrated on four function-approximation-type well-studied problems.
Combining Fuzzy AHP with GIS and Decision Rules for Industrial Site Selection
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Aissa Taibi
2017-12-01
Full Text Available This study combines Fuzzy Analytic Hierarchy Process (FAHP, Geographic Information System (GIS and Decision rules to provide decision makers with a ranking model for industrial sites in Algeria. A ranking of the suitable industrial areas is a crucial multi-criteria decision problem based on socio-economical and technical criteria as on environmental considerations. Fuzzy AHP is used for assessment of the candidate industrial sites by combining fuzzy set theory and analytic hierarchy process (AHP. The decision rule base serves as a filter that performs criteria pre-treatment involving a reduction of their numbers. GIS is used to overlay, generate criteria maps and for visualizing ranked zones on the map. The rank of a zone so obtained is an index that guides decision-makers to the best utilization of the zone in future.
IMPLEMENTATION OF FUZZY LOGIC BASED TEMPERATURE ...
African Journals Online (AJOL)
The “center of gravity” or the “centroid” method of defuzzification was chosen, since it weighs the effect of each input variable towards the calculation of the output [5]. Input fuzzy sets and rules are converted into an output fuzzy set, and then into a crisp output for controlling the steam control valve. All the rules that have any ...
Adaptive Neuro-Fuzzy Inference System based DVR Controller Design
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Brahim FERDI
2011-06-01
Full Text Available PI controller is very common in the control of DVRs. However, one disadvantage of this conventional controller is its inability to still working well under a wider range of operating conditions. So, as a solution fuzzy controller is proposed in literature. But, the main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. To overcome this problem, Adaptive Neuro-Fuzzy Inference System (ANFIS based controller design is proposed. The resulted controller is composed of Sugeno fuzzy controller with two inputs and one output. According to the error and error rate of the control system and the output data, ANFIS generates the appropriate fuzzy controller. The simulation results have proved that the proposed design method gives reliable powerful fuzzy controller with a minimum number of membership functions.
Determining rules for closing customer service centers: A public utility company's fuzzy decision
Dekorvin, Andre; Shipley, Margaret F.; Lea, Robert N.
1992-01-01
In the present work, we consider the general problem of knowledge acquisition under uncertainty. Simply stated, the problem reduces to the following: how can we capture the knowledge of an expert when the expert is unable to clearly formulate how he or she arrives at a decision? A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision may have been made. Unique to our work is the fuzzy set representation of the conditions or attributes upon which the expert may possibly base his fuzzy decision. From our examples, we infer certain and possible fuzzy rules for closing a customer service center and illustrate the importance of having the decision closely relate to the conditions under consideration.
Detection of Stator Winding Fault in Induction Motor Using Fuzzy Logic with Optimal Rules
Directory of Open Access Journals (Sweden)
Hamid Fekri Azgomi
2013-04-01
Full Text Available Induction motors are critical components in many industrial processes. Therefore, swift, precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.
A fuzzy behaviorist approach to sensor-based robot control
Energy Technology Data Exchange (ETDEWEB)
Pin, F.G.
1996-05-01
Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-based approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.
International Nuclear Information System (INIS)
Ozekes, Serhat; Osman, Onur; Ucan, N.
2008-01-01
The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer aided detection of lung nodules
Saravanan, Vijayakumar; Lakshmi, P T V
2014-09-01
The path to personalized medicine demands the use of new and customized biopharmaceutical products containing modified proteins. Hence, assessment of these products for allergenicity becomes mandatory before they are introduced as therapeutics. Despite the availability of different tools to predict the allergenicity of proteins, it remains challenging to predict the allergens and nonallergens, when they share significant sequence similarity with known nonallergens and allergens, respectively. Hence, we propose "FuzzyApp," a novel fuzzy rule based system to evaluate the quality of the query protein to be an allergen. It measures the allergenicity of the protein based on the fuzzy IF-THEN rules derived from five different modules. On various datasets, FuzzyApp outperformed other existing methods and retained balance between sensitivity and specificity, with positive Mathew's correlation coefficient. The high specificity of allergen-like putative nonallergens (APN) revealed the FuzzyApp's capability in distinguishing the APN from allergens. In addition, the error analysis and whole proteome dataset analysis suggest the efficiency and consistency of the proposed method. Further, FuzzyApp predicted the Tropomyosin from various allergenic and nonallergenic sources accurately. The web service created allows batch sequence submission, and outputs the result as readable sentences rather than values alone, which assists the user in understanding why and what features are responsible for the prediction. FuzzyApp is implemented using PERL CGI and is freely accessible at http://fuzzyapp.bicpu.edu.in/predict.php . We suggest the use of Fuzzy logic has much potential in biomarker and personalized medicine research to enhance predictive capabilities of post-genomics diagnostics.
A solution to the rule explosion in the fuzzy inverted pendulum
Directory of Open Access Journals (Sweden)
Peng Ye
2017-08-01
Full Text Available Granulated thought is introduced in this paper,which considers the fuzzy rules as fuzzy grain point and the simulation experiment is carried out.The results show that this method can not only archive the desired control effect,but also reduce the complexity of the system effectively,thereby solve the fuzzy controller rule explosion problem due to rules excessive.
Image segmentation based on scaled fuzzy membership functions
DEFF Research Database (Denmark)
Jantzen, Jan; Ring,, P.; Christiansen, Pernille
1993-01-01
As a basis for an automated interpretation of magnetic resonance images, the authors propose a fuzzy segmentation method. The method uses five standard fuzzy membership functions: small, small medium, medium, large medium, and large. The method fits these membership functions to the modes...... of interest in the image histogram by means of a piecewise-linear transformation. A test example is given concerning a human head image, including a sensitivity analysis based on the fuzzy area measure. The method provides a rule-based interface to the physician...
Development of quantum-based adaptive neuro-fuzzy networks.
Kim, Sung-Suk; Kwak, Keun-Chang
2010-02-01
In this study, we are concerned with a method for constructing quantum-based adaptive neuro-fuzzy networks (QANFNs) with a Takagi-Sugeno-Kang (TSK) fuzzy type based on the fuzzy granulation from a given input-output data set. For this purpose, we developed a systematic approach in producing automatic fuzzy rules based on fuzzy subtractive quantum clustering. This clustering technique is not only an extension of ideas inherent to scale-space and support-vector clustering but also represents an effective prototype that exhibits certain characteristics of the target system to be modeled from the fuzzy subtractive method. Furthermore, we developed linear-regression QANFN (LR-QANFN) as an incremental model to deal with localized nonlinearities of the system, so that all modeling discrepancies can be compensated. After adopting the construction of the linear regression as the first global model, we refined it through a series of local fuzzy if-then rules in order to capture the remaining localized characteristics. The experimental results revealed that the proposed QANFN and LR-QANFN yielded a better performance in comparison with radial basis function networks and the linguistic model obtained in previous literature for an automobile mile-per-gallon prediction, Boston Housing data, and a coagulant dosing process in a water purification plant.
Worm, Jeffrey A.; Culas, Donald E.
1991-01-01
Computers are not designed to handle terms where uncertainty is present. To deal with uncertainty, techniques other than classical logic must be developed. This paper examines the concepts of statistical analysis, the Dempster-Shafer theory, rough set theory, and fuzzy set theory to solve this problem. The fundamentals of these theories are combined to provide the possible optimal solution. By incorporating principles from these theories, a decision-making process may be simulated by extracting two sets of fuzzy rules: certain rules and possible rules. From these rules a corresponding measure of how much we believe these rules is constructed. From this, the idea of how much a fuzzy diagnosis is definable in terms of its fuzzy attributes is studied.
Fuzzy modeling based on generalized neural networks and fuzzy clustering objective functions
Sun, Chuen-Tsai; Jang, Jyh-Shing
1991-01-01
An approach to the formulation of fuzzy if-then rules based on clustering objective functions is proposed. The membership functions are then calibrated with the generalized neural networks technique to achieve a desired input-output mapping. The learning procedure is basically a gradient-descent algorithm. A Kalman filter algorithm is used to improve the overall performance.
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.
Directory of Open Access Journals (Sweden)
Zhijia Chen
2015-01-01
Full Text Available In IaaS (infrastructure as a service cloud environment, users are provisioned with virtual machines (VMs. To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN. We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
Soil quality assessment using weighted fuzzy association rules
Xue, Yue-Ju; Liu, Shu-Guang; Hu, Yue-Ming; Yang, Jing-Feng
2010-01-01
Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the importance variability of the rules, can be redundant and far from optimal. In this study, we developed a method applying different weights to traditional FARs to improve accuracy of soil quality assessment. After the FARs for soil quality assessment were mined, redundant rules were eliminated according to whether the rules were significant or not in reducing the complexity of the soil quality assessment models and in improving the comprehensibility of FARs. The global weights, each representing the importance of a FAR in soil quality assessment, were then introduced and refined using a gradient descent optimization method. This method was applied to the assessment of soil resources conditions in Guangdong Province, China. The new approach had an accuracy of 87%, when 15 rules were mined, as compared with 76% from the traditional approach. The accuracy increased to 96% when 32 rules were mined, in contrast to 88% from the traditional approach. These results demonstrated an improved comprehensibility of FARs and a high accuracy of the proposed method.
Sugisaka, Masanori; Mbaïtiga, Zacharie
There exist several problems in the control of vehicle brake including the development of control logic for anti-lock braking system (ABS), base-braking and intelligent braking. Here we study the intelligent braking control where we seek to develop a controller that can ensure that the braking torque commended by the driver will be achieved. In particular, we develop, a new PID Fuzzy controller (PIDFC) based on parallel operation of PI Fuzzy and PD Fuzzy control. Two fuzzy rule bases are constructed by separating the linguistic control rule for PID Fuzzy control into two parts: The first part is e-Δe and the second part is Δ2e-Δe respectively. Then two Fuzzy controls employing these rules bases individually are synthesized and run in parallel. The incremental control input is determined by taking weighted mean of the outputs of two Fuzzy controls. The result, which proves the merit of the proposed method are compared to those found in the previous research.
Determination of interrill soil erodibility coefficient based on Fuzzy and Fuzzy-Genetic Systems
Directory of Open Access Journals (Sweden)
Habib Palizvan Zand
2017-02-01
Full Text Available Introduction: Although the fuzzy logic science has been used successfully in various sudies of hydrology and soil erosion, but in literature review no article was found about its performance for estimating of interrill erodibility. On the other hand, studies indicate that genetic algorithm techniques can be used in fuzzy models and finding the appropriate membership functions for linguistic variables and fuzzy rules. So this study was conducted to develop the fuzzy and fuzzy–genetics models and investigation of their performance in the estimation of soil interrill erodibility factor (Ki. Materials and Methods: For this reason 36 soil samples with different physical and chemical properties were collected from west of Azerbaijan province . soilsamples were also taken from the Ap or A horizon of each soil profile. The samples were air-dried , sieved and Some soil characteristics such as soil texture, organic matter (OM, cation exchange capacity (CEC, sodium adsorption ratio (SAR, EC and pH were determined by the standard laboratory methods. Aggregates size distributions (ASD were determined by the wet-sieving method and fractal dimension of soil aggregates (Dn was also calculated. In order to determination of soil interrill erodibility, the flume experiment performed by packing soil a depth of 0.09-m in 0.5 × 1.0 m. soil was saturated from the base and adjusted to 9% slope and was subjected to at least 90 min rainfall . Rainfall intensity treatments were 20, 37 and 47 mm h-1. During each rainfall event, runoff was collected manually in different time intervals, being less than 60 s at the beginning, up to 15 min near the end of the test. At the end of the experiment, the volumes of runoff samples and the mass of sediment load at each time interval were measured. Finally interrill erodibility values were calculated using Kinnell (11 Equation. Then by statistical analyses Dn and sand percent of the soils were selected as input variables and Ki as
A Genetic Based Neuro Fuzzy Technique for Process Grain Sized Scheduling of Parallel Jobs
Keppanagowder Thanushkodi; Sadasivam V. Sudha
2012-01-01
Problem statement: In this study, we present the development of genetic algorithm based neuro fuzzy technique for process grain sized in scheduling of parallel jobs with the help of real lIfe workload data. Approach: The study uses the rule based scheduling strategy for the scheduling and classIfies all possible scheduling strategies. The rule bases are developed with the help of the neuro fuzzy system and with the genetic fuzzy system. From the comparison of the two classIfiers of the fuzzy ...
Juang, Chia-Feng; Hsu, Chia-Hung
2009-12-01
This paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise. There are initially no fuzzy rules in the IT2FS. The ORGQACO concurrently designs both the structure and parameters of an IT2FS. We propose an online interval type-2 rule generation method for the evolution of system structure and flexible partitioning of the input space. Consequent part parameters in an IT2FS are designed using Q -values and the reinforcement local-global ant colony optimization algorithm. This algorithm selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of which are updated using reinforcement signals. The ORGQACO design method is applied to the following three control problems: 1) truck-backing control; 2) magnetic-levitation control; and 3) chaotic-system control. The ORGQACO is compared with other reinforcement-learning methods to verify its efficiency and effectiveness. Comparisons with type-1 fuzzy systems verify the noise robustness property of using an IT2FS.
Schmitt, Emmanuel; Bombardier, Vincent; Wendling, Laurent
2008-10-01
In this paper, an iterative method to select suitable features in an industrial pattern recognition context is proposed. It combines a global method of feature selection and a fuzzy linguistic rule classifier. It is applied to an industrial fabric textile context. The aim of the global vision system is to identify textile fabric defects. From the related industrial process, the training data sets are small, and some are incomplete. Moreover, the recognition step must be compatible with the time constant of the system, which generally imposes low complexity for the system. The choice of the most relevant features and the reduction of their number are important to respect these constraints. The feature selection method is based on the analysis of indexes extracted on the lattice defined from training in relation with the Choquet integral. This selection step is embedded in an iterative algorithm to discard weaker features in order to decrease the number of rules while keeping good recognition rates. The recognition step is done with a fuzzy reasoning classifier that is well adapted for this application case. The proposed method is quite efficient with small learning data sets because of the generalization capacity of both the feature selection and recognition steps. The experimental study shows the wanted behavior of this approach: the feature number decreases, whereas the recognition rate increases. Thus, the total number of generated fuzzy rules is reduced.
Abdelfatah, Nasri; Brahim, Gasbaoui
2011-01-01
The Reactive power flow’s is one of the most electrical distribution systems problem wich have great of interset of the electrical network researchers, it’s cause’s active power transmission reduction, power losses decreasing, and the drop voltage’s increase. In this research we described the efficiency of the FLC-GAO approach to solve the optimal power flow (OPF) combinatorial problem. The proposed approach employ tow algorithms, Fuzzy logic controller (FLC) algorithm for critical nodal de...
Design of a fuzzy logic based controller for neutron power regulation
International Nuclear Information System (INIS)
Velez D, D.
2000-01-01
This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)
WARP: Weight Associative Rule Processor. A dedicated VLSI fuzzy logic megacell
Pagni, A.; Poluzzi, R.; Rizzotto, G. G.
1992-01-01
During the last five years Fuzzy Logic has gained enormous popularity in the academic and industrial worlds. The success of this new methodology has led the microelectronics industry to create a new class of machines, called Fuzzy Machines, to overcome the limitations of traditional computing systems when utilized as Fuzzy Systems. This paper gives an overview of the methods by which Fuzzy Logic data structures are represented in the machines (each with its own advantages and inefficiencies). Next, the paper introduces WARP (Weight Associative Rule Processor) which is a dedicated VLSI megacell allowing the realization of a fuzzy controller suitable for a wide range of applications. WARP represents an innovative approach to VLSI Fuzzy controllers by utilizing different types of data structures for characterizing the membership functions during the various stages of the Fuzzy processing. WARP dedicated architecture has been designed in order to achieve high performance by exploiting the computational advantages offered by the different data representations.
GA-Based Fuzzy Sliding Mode Controller for Nonlinear Systems
Directory of Open Access Journals (Sweden)
P. C. Chen
2008-01-01
Full Text Available Generally, the greatest difficulty encountered when designing a fuzzy sliding mode controller (FSMC or an adaptive fuzzy sliding mode controller (AFSMC capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. In this paper, we describe a method of stability analysis for a GA-based reference adaptive fuzzy sliding model controller capable of handling these types of problems for a nonlinear system. First, we approximate and describe an uncertain and nonlinear plant for the tracking of a reference trajectory via a fuzzy model incorporating fuzzy logic control rules. Next, the initial values of the consequent parameter vector are decided via a genetic algorithm. After this, an adaptive fuzzy sliding model controller, designed to simultaneously stabilize and control the system, is derived. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov's direct method. Finally, an example, a numerical simulation, is provided to demonstrate the control methodology.
Directory of Open Access Journals (Sweden)
Nasri Abdelfatah
2011-01-01
Full Text Available The Reactive power flow’s is one of the most electrical distribution systems problem wich have great of interset of the electrical network researchers, it’s cause’s active power transmission reduction, power losses decreasing, and the drop voltage’s increase. In this research we described the efficiency of the FLC-GAO approach to solve the optimal power flow (OPF combinatorial problem. The proposed approach employ tow algorithms, Fuzzy logic controller (FLC algorithm for critical nodal detection and gentic algorithm optimization (GAO algorithm for optimal seizing capacitor.GAO method is more efficient in combinatory problem solutions. The proposed approach has been examined and tested on the standard IEEE 57-bus the resulats show the power loss minimization denhancement, voltage profile, and stability improvement. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach.
Dhruba Das; Hemanta K. Baruah
2015-01-01
In this article, based on Zadeh’s extension principle we have apply the parametric programming approach to construct the membership functions of the performance measures when the interarrival time and the service time are fuzzy numbers based on the Baruah’s Randomness- Fuzziness Consistency Principle. The Randomness-Fuzziness Consistency Principle leads to defining a normal law of fuzziness using two different laws of randomness. In this article, two fuzzy queues FM...
Aggregation Operator Based Fuzzy Pattern Classifier Design
DEFF Research Database (Denmark)
Mönks, Uwe; Larsen, Henrik Legind; Lohweg, Volker
2009-01-01
This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable. The perfor...
Fuzzy Modelling for Human Dynamics Based on Online Social Networks.
Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F
2017-08-24
Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.
Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty
International Nuclear Information System (INIS)
Purba, Julwan Hendry; Sony Tjahyani, D.T.; Ekariansyah, Andi Sofrany; Tjahjono, Hendro
2015-01-01
Highlights: • Fuzzy probability based fault tree analysis is to evaluate epistemic uncertainty in fuzzy fault tree analysis. • Fuzzy probabilities represent likelihood occurrences of all events in a fault tree. • A fuzzy multiplication rule quantifies epistemic uncertainty of minimal cut sets. • A fuzzy complement rule estimate epistemic uncertainty of the top event. • The proposed FPFTA has successfully evaluated the U.S. Combustion Engineering RPS. - Abstract: A number of fuzzy fault tree analysis approaches, which integrate fuzzy concepts into the quantitative phase of conventional fault tree analysis, have been proposed to study reliabilities of engineering systems. Those new approaches apply expert judgments to overcome the limitation of the conventional fault tree analysis when basic events do not have probability distributions. Since expert judgments might come with epistemic uncertainty, it is important to quantify the overall uncertainties of the fuzzy fault tree analysis. Monte Carlo simulation is commonly used to quantify the overall uncertainties of conventional fault tree analysis. However, since Monte Carlo simulation is based on probability distribution, this technique is not appropriate for fuzzy fault tree analysis, which is based on fuzzy probabilities. The objective of this study is to develop a fuzzy probability based fault tree analysis to overcome the limitation of fuzzy fault tree analysis. To demonstrate the applicability of the proposed approach, a case study is performed and its results are then compared to the results analyzed by a conventional fault tree analysis. The results confirm that the proposed fuzzy probability based fault tree analysis is feasible to propagate and quantify epistemic uncertainties in fault tree analysis
Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach
Directory of Open Access Journals (Sweden)
Rana Dinesh Singh
2015-01-01
Full Text Available Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.
Macian-Sorribes, Hector; Pulido-Velazquez, Manuel
2016-04-01
This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to
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Bloch Isabelle
2007-01-01
Full Text Available This paper describes a system for optical music recognition (OMR in case of monophonic typeset scores. After clarifying the difficulties specific to this domain, we propose appropriate solutions at both image analysis level and high-level interpretation. Thus, a recognition and segmentation method is designed, that allows dealing with common printing defects and numerous symbol interconnections. Then, musical rules are modeled and integrated, in order to make a consistent decision. This high-level interpretation step relies on the fuzzy sets and possibility framework, since it allows dealing with symbol variability, flexibility, and imprecision of music rules, and merging all these heterogeneous pieces of information. Other innovative features are the indication of potential errors and the possibility of applying learning procedures, in order to gain in robustness. Experiments conducted on a large data base show that the proposed method constitutes an interesting contribution to OMR.
Extracting fuzzy rules under uncertainty and measuring definability using rough sets
Culas, Donald E.
1991-01-01
Although computers have come a long way since their invention, they are basically able to handle only crisp values at the hardware level. Unfortunately, the world we live in consists of problems which fail to fall into this category, i.e., uncertainty is all too common. A problem is looked at which involves uncertainty. To be specific, attributes are dealt with which are fuzzy sets. Under this condition, knowledge is acquired by looking at examples. In each example, a condition as well as a decision is made available. Based on the examples given, two sets of rules are extracted, certain and possible. Furthermore, measures are constructed of how much these rules are believed in, and finally, the decisions are defined as a function of the terms used in the conditions.
Chen, Min; Yin, Xuezhi
2011-07-01
This paper descries a new non-invasive method for diagnosis of breathing disorders based on adaptive-network-based fuzzy inference system (ANFIS). In this method, PetCO2, SpO2 and HR are chosen as inputs, and the breathing condition is selected as output ofANFIS. The inputs and output are then classified into fuzzy subsets by experts' knowledge. After, the fuzzy IF-THEN rules are built up according to the corresponding membership functions by set up of fuzzy subsets. The neural network was finally established and the membership functions and fuzzy rules were optimized by training. The results of experiment shows that ANFIS is more effective than BP Network regarding the diagnosis of breathing disorders.
Outdoor altitude stabilization of QuadRotor based on type-2 fuzzy and fuzzy PID
Wicaksono, H.; Yusuf, Y. G.; Kristanto, C.; Haryanto, L.
2017-11-01
This paper presents a design of altitude stabilization of QuadRotor based on type-2 fuzzy and fuzzy PID. This practical design is implemented outdoor. Barometric and sonar sensor were used in this experiment as an input for the controller YoHe. The throttle signal as a control input was provided by the controller to leveling QuadRotor in particular altitude and known well as altitude stabilization. The parameter of type-2 fuzzy and fuzzy PID was tuned in several heights to get the best control parameter for any height. Type-2 fuzzy produced better result than fuzzy PID but had a slow response in the beginning.
Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms
Siddique, Nazmul
2014-01-01
Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...
Transportation optimization with fuzzy trapezoidal numbers based on possibility theory.
He, Dayi; Li, Ran; Huang, Qi; Lei, Ping
2014-01-01
In this paper, a parametric method is introduced to solve fuzzy transportation problem. Considering that parameters of transportation problem have uncertainties, this paper develops a generalized fuzzy transportation problem with fuzzy supply, demand and cost. For simplicity, these parameters are assumed to be fuzzy trapezoidal numbers. Based on possibility theory and consistent with decision-makers' subjectiveness and practical requirements, the fuzzy transportation problem is transformed to a crisp linear transportation problem by defuzzifying fuzzy constraints and objectives with application of fractile and modality approach. Finally, a numerical example is provided to exemplify the application of fuzzy transportation programming and to verify the validity of the proposed methods.
An Integrated Risk Index Model Based on Hierarchical Fuzzy Logic for Underground Risk Assessment
Directory of Open Access Journals (Sweden)
Muhammad Fayaz
2017-10-01
Full Text Available Available space in congested cities is getting scarce due to growing urbanization in the recent past. The utilization of underground space is considered as a solution to the limited space in smart cities. The numbers of underground facilities are growing day by day in the developing world. Typical underground facilities include the transit subway, parking lots, electric lines, water supply and sewer lines. The likelihood of the occurrence of accidents due to underground facilities is a random phenomenon. To avoid any accidental loss, a risk assessment method is required to conduct the continuous risk assessment and report any abnormality before it happens. In this paper, we have proposed a hierarchical fuzzy inference based model for under-ground risk assessment. The proposed hierarchical fuzzy inference architecture reduces the total number of rules from the rule base. Rule reduction is important because the curse of dimensionality damages the transparency and interpretation as it is very tough to understand and justify hundreds or thousands of fuzzy rules. The computation time also increases as rules increase. The proposed model takes 175 rules having eight input parameters to compute the risk index, and the conventional fuzzy logic requires 390,625 rules, having the same number of input parameters to compute risk index. Hence, the proposed model significantly reduces the curse of dimensionality. Rule design for fuzzy logic is also a tedious task. In this paper, we have also introduced new rule schemes, namely maximum rule-based and average rule-based; both schemes can be used interchangeably according to the logic needed for rule design. The experimental results show that the proposed method is a virtuous choice for risk index calculation where the numbers of variables are greater.
Meta Modelling of Submerged-Arc Welding Design based on Fuzzy Algorithm
Song, Chang-Yong; Park, Jonghwan; Goh, Dugab; Park, Woo-Chang; Lee, Chang-Ha; Kim, Mun Yong; Kang, Jinseo
2017-12-01
Fuzzy algorithm based meta-model is proposed for approximating submerged-arc weld design factors such as weld speed and weld output. Orthogonal array design based on the submerged-arc weld numerical analysis is applied to the proposed approach. The nonlinear finite element analysis is carried out to simulate the submerged-arc weld numerical analysis using thermo-mechanical and temperature-dependent material properties for general mild steel. The proposed meta-model based on fuzzy algorithm design is generated with triangle membership functions and fuzzy if-then rules using training data obtained from the Taguchi orthogonal array design data. The aim of proposed approach is to develop a fuzzy meta-model to effectively approximate the optimized submerged-arc weld factors. To validate the meta-model, the results obtained from the fuzzy meta-model are compared to the best cases from the Taguchi orthogonal array.
Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering
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Jean Marie Vianney Kinani
2017-01-01
Full Text Available We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR and fluid-attenuated inversion recovery (FLAIR images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.
A reinforcement learning-based architecture for fuzzy logic control
Berenji, Hamid R.
1992-01-01
This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.
Enhancement of SAR images using fuzzy shrinkage technique in ...
Indian Academy of Sciences (India)
Shivakumara Swamy Puranik Math
2017-08-03
Aug 3, 2017 ... fuzzy techniques, such as fuzzy clustering, fuzzy rule-based approach, and fuzzy integration approach. In the proposed work, the fuzzy membership is modified using Eq. (12). After the membership value is modified defuzzification process is applied with the help of Eq. (13). Denoised coefficients are.
Research on Fault Diagnosis Method Based on Rule Base Neural Network
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Zheng Ni
2017-01-01
Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.
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Dhruba Das
2015-04-01
Full Text Available In this article, based on Zadeh’s extension principle we have apply the parametric programming approach to construct the membership functions of the performance measures when the interarrival time and the service time are fuzzy numbers based on the Baruah’s Randomness- Fuzziness Consistency Principle. The Randomness-Fuzziness Consistency Principle leads to defining a normal law of fuzziness using two different laws of randomness. In this article, two fuzzy queues FM/M/1 and M/FM/1 has been studied and constructed their membership functions of the system characteristics based on the aforesaid principle. The former represents a queue with fuzzy exponential arrivals and exponential service rate while the latter represents a queue with exponential arrival rate and fuzzy exponential service rate.
Santos, Sandra A; de Lima, Helano Póvoas; Massruhá, Silvia M F S; de Abreu, Urbano G P; Tomás, Walfrido M; Salis, Suzana M; Cardoso, Evaldo L; de Oliveira, Márcia Divina; Soares, Márcia Toffani S; Dos Santos, Antônio; de Oliveira, Luiz Orcírio F; Calheiros, Débora F; Crispim, Sandra M A; Soriano, Balbina M A; Amâncio, Christiane O G; Nunes, Alessandro Pacheco; Pellegrin, Luiz Alberto
2017-08-01
One of the most relevant issues in discussion worldwide nowadays is the concept of sustainability. However, sustainability assessment is a difficult task due to the complexity of factors involved in the natural world added to the human interference. In order to assess the sustainability of beef ranching in complex and uncertain tropical environment systems this paper describes a decision support system based on fuzzy rule-approach, the Sustainable Pantanal Ranch (SPR). This tool was built by a set of measurements and indicators integrated by fuzzy logic to evaluate the attributes of the three dimensions of sustainability. Indicators and decision rules, as well as scenario evaluations, were obtained from workshops involving multi-disciplinary team of experts. A Fuzzy Rule-Based System (FRBS) was developed to each attribute, dimension and general index. The essential parts of the FRBS are the knowledge database, rules and the inference engine. The FuzzyGen and WebFuzzy tools were developed to support the FRBS and both showed efficiency and low cost for digital applications. The results of each attribute, dimension and index were presented as radar graphs, showing the individual value (0-10) of each indicator. In the validation process using the WebFuzzy, different combinations of indicators were made for each attribute index to show the corresponding output, and which confirm the feasibility and usability of the tool. Copyright © 2017 Elsevier Ltd. All rights reserved.
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2017-08-11
This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.
Directory of Open Access Journals (Sweden)
Hsin-Chieh Wu
2013-01-01
Full Text Available In this study, the fuzzy-neural ensemble and geometric rule fusion approach is presented to optimize the performance of job dispatching in a wafer fabrication factory with an intelligent rule. The proposed methodology is a modification of a previous study by fusing two dispatching rules and diversifying the job slacks in novel ways. To this end, the geometric mean of the neighboring distances of slacks is maximized. In addition, the fuzzy c-means (FCM and backpropagation network (BPN ensemble approach was also proposed to estimate the remaining cycle time of a job, which is an important input to the new rule. A new aggregation mechanism was also designed to enhance the robustness of the FCM-BPN ensemble approach. To validate the effectiveness of the proposed methodology, some experiments have been conducted. The experimental results did support the effectiveness of the proposed methodology.
Approximation properties of the neuro-fuzzy minimum function
Gottschling, Andreas; Kreuter, Christof
1999-01-01
The integration of fuzzy logic systems and neural networks in data driven nonlinear modeling applications has generally been limited to functions based upon the multiplicative fuzzy implication rule for theoretical and computational reasons. We derive a universal approximation result for the minimum fuzzy implication rule as well as a differentiable substitute function that allows fast optimization and function approximation with neuro-fuzzy networks.
Robust modified GA based multi-stage fuzzy LFC
International Nuclear Information System (INIS)
Shayeghi, H.; Jalili, A.; Shayanfar, H.A.
2007-01-01
In this paper, a robust genetic algorithm (GA) based multi-stage fuzzy (MSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operates under deregulation based on the bilateral policy scheme. In this strategy, the control signal is tuned online from the knowledge base and the fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of the membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by modified genetic algorithms. The classical genetic algorithms are powerful search techniques to find the global optimal area. However, the global optimum value is not guaranteed using this method, and the speed of the algorithm's convergence is extremely reduced too. To overcome this drawback, a modified genetic algorithm is being used to tune the membership functions of the proposed MSF controller. The effectiveness of the proposed method is demonstrated on a three area restructured power system with possible contracted scenarios under large load demand and area disturbances in comparison with the multi-stage fuzzy and classical fuzzy PID controllers through FD and ITAE performance indices. The results evaluation shows that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers. Moreover, this newly developed control strategy has a simple structure, does not require an accurate model of the plant and is fairly easy to implement, which can be useful for the real world complex power systems
Abrasive slurry jet cutting model based on fuzzy relations
Qiang, C. H.; Guo, C. W.
2017-12-01
The cutting process of pre-mixed abrasive slurry or suspension jet (ASJ) is a complex process affected by many factors, and there is a highly nonlinear relationship between the cutting parameters and cutting quality. In this paper, guided by fuzzy theory, the fuzzy cutting model of ASJ was developed. In the modeling of surface roughness, the upper surface roughness prediction model and the lower surface roughness prediction model were established respectively. The adaptive fuzzy inference system combines the learning mechanism of neural networks and the linguistic reasoning ability of the fuzzy system, membership functions, and fuzzy rules are obtained by adaptive adjustment. Therefore, the modeling process is fast and effective. In this paper, the ANFIS module of MATLAB fuzzy logic toolbox was used to establish the fuzzy cutting model of ASJ, which is found to be quite instrumental to ASJ cutting applications.
Fuzzy-logic-based active vibration control of beams using piezoelectric patches
Sharma, Manu; Singh, S. P.; Sachdeva, B. L.
2003-10-01
The present work presents a fuzzy logic based controller with a compact rule base, for active vibration control of beams. The controller was implemented experimentally on a test beam and the results were found satisfactory. The test system consists of a cantilevered beam with two piezoelectric patches mounted near its root in collocated fashion. This piezo-beam system was modelled using Finite Element Method. To derive the equations of motion, Hamilton's principle was used. Electro-mechanical interaction of the piezoelectric patch with the beam was modelled using linear constitutive equations for piezoceramics, which relate strain and electric displacement to stress and electric field. The fuzzy logic controller is based on modal velocity of the beam. The basis for generating the fuzzy logic rule base of this controller is obtained from negative velocity feedback control. Modal velocity of the beam acts as an input to the fuzzy controller and actuation force is the output from the inference engine. Linear decay of vibratory amplitude is observed in case of fuzzy logic controller as opposed to logarithmic decay in case of negative velocity feedback control Present controller has just three rules. This is an important achievement because bulky fuzzy logic controllers for active vibration control require fast processors for real time implementation (Kwak and Sciulli and Mayhan and Washington).
Data-Based Fuzzy TOPSIS for Alternative Ranking
Directory of Open Access Journals (Sweden)
Victor Utomo
2016-01-01
Full Text Available Technique for Order Preference by Similarity (TOPSIS solves multi-criteria decision making (MCDM by ranking the alternatives. When the attributes are not deterministic, a Fuzzy TOPSIS method is applied. The traditional fuzzy TOPSIS depends on decision makers to determine alternative’s value which considered subjective. A new method named data-based fuzzy TOPSIS proposed to diminish the dependency to decision maker. The proposed algorithm use data to determine alternative’s values objectively. Subtractive Clustering (SC and Fuzzy C-Mean (FCM selected to transform crisp value data to fuzzy value data. Some modification applied to SC and FCM to obtain fuzzy triangular value needed by fuzzy TOPSIS. Keyword : Index Terms—Decision support systems, fuzzy TOPSIS, fuzzy C-mean, subtractive clustering
Fuzzy Logic Based Autonomous Parallel Parking System with Kalman Filtering
Panomruttanarug, Benjamas; Higuchi, Kohji
This paper presents an emulation of fuzzy logic control schemes for an autonomous parallel parking system in a backward maneuver. There are four infrared sensors sending the distance data to a microcontroller for generating an obstacle-free parking path. Two of them mounted on the front and rear wheels on the parking side are used as the inputs to the fuzzy rules to calculate a proper steering angle while backing. The other two attached to the front and rear ends serve for avoiding collision with other cars along the parking space. At the end of parking processes, the vehicle will be in line with other parked cars and positioned in the middle of the free space. Fuzzy rules are designed based upon a wall following process. Performance of the infrared sensors is improved using Kalman filtering. The design method needs extra information from ultrasonic sensors. Starting from modeling the ultrasonic sensor in 1-D state space forms, one makes use of the infrared sensor as a measurement to update the predicted values. Experimental results demonstrate the effectiveness of sensor improvement.
A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables
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Jaw-Kuen Shiau
2015-04-01
Full Text Available Maximum power point tracking (MPPT is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i slope (of solar power-versus-solar voltage and changes of the slope; (ii slope and variation of the power; (iii variation of power and variation of voltage; (iv variation of power and variation of current; (v sum of conductance and increment of the conductance; and (vi sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i–(iv have two input variables each while algorithms (v and (vi use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV cell properties. The range of the input variable of Algorithm (vi is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.
Juang, Chia-Feng; Jeng, Tian-Lu; Chang, Yu-Cheng
2016-12-01
This paper proposes a new multiobjective optimization approach to designing a fuzzy logic system (FLS) using process data and applies it to the wall-following control of a mobile robot. The objectives considered include both the interpretability and control performance of the FLS. It is assumed that no off-line training data are available in advance, and the rule base is initially empty. All rules are generated through an online clustering and fuzzy set merging (OCFM) algorithm using data generated online during the FLS evaluation process. The OCFM builds a reference rule base that flexibly partitions the input space with distinguishable fuzzy sets (FSs). Based on the reference rule base, a new multiobjective front-guided continuous ant-colony optimization (MO-FCACO) algorithm is proposed to optimize the FLS structure and parameters. In addition to the objective functions defined to evaluate the FLS control performance, a transparency-oriented objective function is defined with constraints imposed on the FS parameters to obtain an interpretable FLS with transparent FSs. The MO-FCACO solves the constrained multiobjective optimization problem by optimizing all of the free parameters in an FLS through ant-path selection, sampling operation, and front-guided optimization processes. The multiobjective FLS design approach is applied to control the orientation and moving speed of a mobile robot in performing the wall-following task. Optimization performance of the MO-FCACO is verified through comparisons with various multiobjective population-based optimization algorithms. Experimental results verify the effectiveness of the designed FLSs in controlling a real robot.
Using fuzzy logic to integrate neural networks and knowledge-based systems
Yen, John
1991-01-01
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.
Igrejas, Getúlio; Salgado, Paulo
2007-01-01
This paper presents a fuzzy c-means clustering method for partitioning symbolic interval data, namely the T-S fuzzy rules. The proposed method furnish a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable squared Euclidean distances between vectors of intervals. This methodology leads to a fuzzy partition of the TS-fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of TS-fuzzy ...
Fuzzy-logic-based resource allocation for isolated and multiple platforms
Smith, James F., III; Rhyne, Robert D., II
2000-08-01
Modern naval battle forces generally include many different platforms each with its own sensors, radar, ESM, and communications. The sharing of information measured by local sensors via communication links across the battle group should allow for optimal or near optimal decision. The survival of the battle group or members of the group depends on the automatic real-time allocation of various resources. A fuzzy logic algorithm has been developed that automatically allocates electronic attack resources in real- time. The particular approach to fuzzy logic that is used is the fuzzy decision tree, a generalization of the standard artificial intelligence technique of decision trees. The controller must be able to make decisions based on rules provided by experts. The fuzzy logic approach allows the direct incorporation of expertise forming a fuzzy linguistic description, i.e. a formal representation of the system in terms of fuzzy if-then rules. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the fuzzy root concepts. The isolated platform and multi platform resource manager models are discussed as well as the underlying multi-platform communication model. The resource manager is shown to exhibit excellent performance under many demanding scenarios.
Fuzzy Reasoning Based on First-Order Modal Logic,
Zhang, Xiaoru; Zhang, Z.; Sui, Y.; Huang, Z.
2008-01-01
As an extension of traditional modal logics, this paper proposes a fuzzy first-order modal logic based on believable degree, and gives out a description of the fuzzy first-order modal logic based on constant domain semantics. In order to make the reasoning procedure between the fuzzy assertions
Fuzzy model-based control of a nuclear reactor
International Nuclear Information System (INIS)
Van Den Durpel, L.; Ruan, D.
1994-01-01
The fuzzy model-based control of a nuclear power reactor is an emerging research topic world-wide. SCK-CEN is dealing with this research in a preliminary stage, including two aspects, namely fuzzy control and fuzzy modelling. The aim is to combine both methodologies in contrast to conventional model-based PID control techniques, and to state advantages of including fuzzy parameters as safety and operator feedback. This paper summarizes the general scheme of this new research project
Directory of Open Access Journals (Sweden)
Peide Liu
2017-11-01
Full Text Available Normal intuitionistic fuzzy numbers (NIFNs, which combine the normal fuzzy number (NFN with intuitionistic number, can easily express the stochastic fuzzy information existing in real decision making, and power-average (PA operator can consider the relationships of different attributes by assigned weighting vectors which depend upon the input arguments. In this paper, we extended PA operator to process the NIFNs. Firstly, we defined some basic operational rules of NIFNs by considering the interaction operations of intuitionistic fuzzy sets (IFSs, established the distance between two NIFNs, and introduced the comparison method of NIFNs. Then, we proposed some new aggregation operators, including normal intuitionistic fuzzy weighted interaction averaging (NIFWIA operator, normal intuitionistic fuzzy power interaction averaging (NIFPIA operator, normal intuitionistic fuzzy weighted power interaction averaging (NIFWPIA operator, normal intuitionistic fuzzy generalized power interaction averaging (NIFGPIA operator, and normal intuitionistic fuzzy generalized weighted power interaction averaging (NIFGWPIA operator, and studied some properties and some special cases of them. Based on these operators, we developed a decision approach for multiple attribute decision-making (MADM problems with NIFNs. The significant characteristics of the proposed method are that: (1 it is easier to describe the uncertain information than the existing fuzzy sets and stochastic variables; (2 it used the interaction operations in part of IFSs which could overcome the existing weaknesses in operational rules of NIFNs; (3 it adopted PA operator which could relieve the influence of unreasonable data given by biased decision makers; and (4 it made the decision-making results more flexible and reliable because it was with generalized parameter which could be regard as the risk attitude value of decision makers. Finally, an illustrative example is given to verify its feasibility
Similarity based approximate reasoning: fuzzy control
Raha, S.; Hossain, A.; Ghosh, S.
2008-01-01
This paper presents an approach to similarity based approximate reasoning that elucidates the connection between similarity and existing approaches to inference in approximate reasoning methodology. A set of axioms is proposed to get a reasonable measure of similarity between two fuzzy sets. The
Fuzzy model-based observers for fault detection in CSTR.
Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan
2015-11-01
Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Fuzzy knowledge management for the semantic web
Ma, Zongmin; Yan, Li; Cheng, Jingwei
2014-01-01
This book goes to great depth concerning the fast growing topic of technologies and approaches of fuzzy logic in the Semantic Web. The topics of this book include fuzzy description logics and fuzzy ontologies, queries of fuzzy description logics and fuzzy ontology knowledge bases, extraction of fuzzy description logics and ontologies from fuzzy data models, storage of fuzzy ontology knowledge bases in fuzzy databases, fuzzy Semantic Web ontology mapping, and fuzzy rules and their interchange in the Semantic Web. The book aims to provide a single record of current research in the fuzzy knowledge representation and reasoning for the Semantic Web. The objective of the book is to provide the state of the art information to researchers, practitioners and graduate students of the Web intelligence and at the same time serve the knowledge and data engineering professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.
The Compositional Rule of Inference and Zadeh’s Extension Principle for Non-normal Fuzzy Sets
van den Broek, P.M.; Noppen, J.A.R.; Castillo, Oscar
2007-01-01
Defining the standard Boolean operations on fuzzy Booleans with the compositional rule of inference (CRI) or Zadeh's extension principle gives counter-intuitive results. We introduce and motivate a slight adaptation of the CRI, which only effects the results for non-normal fuzzy sets. It is shown
Self-learning fuzzy controllers based on temporal back propagation
Jang, Jyh-Shing R.
1992-01-01
This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.
Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure
Directory of Open Access Journals (Sweden)
Ashraf Ahmed Fahmy
2014-03-01
Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation. Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.
Nonlinear adaptive control based on fuzzy sliding mode technique and fuzzy-based compensator.
Nguyen, Sy Dzung; Vo, Hoang Duy; Seo, Tae-Il
2017-09-01
It is difficult to efficiently control nonlinear systems in the presence of uncertainty and disturbance (UAD). One of the main reasons derives from the negative impact of the unknown features of UAD as well as the response delay of the control system on the accuracy rate in the real time of the control signal. In order to deal with this, we propose a new controller named CO-FSMC for a class of nonlinear control systems subjected to UAD, which is constituted of a fuzzy sliding mode controller (FSMC) and a fuzzy-based compensator (CO). Firstly, the FSMC and CO are designed independently, and then an adaptive fuzzy structure is discovered to combine them. Solutions for avoiding the singular cases of the fuzzy-based function approximation and reducing the calculating cost are proposed. Based on the solutions, fuzzy sliding mode technique, lumped disturbance observer and Lyapunov stability analysis, a closed-loop adaptive control law is formulated. Simulations along with a real application based on a semi-active train-car suspension are performed to fully evaluate the method. The obtained results reflected that vibration of the chassis mass is insensitive to UAD. Compared with the other fuzzy sliding mode control strategies, the CO-FSMC can provide the best control ability to reduce unwanted vibrations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Design of uav robust autopilot based on adaptive neuro-fuzzy inference system
Directory of Open Access Journals (Sweden)
Mohand Achour Touat
2008-04-01
Full Text Available This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.
Intelligent control-II: review of fuzzy systems and theory of approximate reasoning
International Nuclear Information System (INIS)
Nagrial, M.H.
2004-01-01
Fuzzy systems are knowledge-based or rule-based systems. The heart of a fuzzy systems knowledge base consisting of the so-called fuzzy IF -THEN rules. This paper reviews various aspects of fuzzy IF-THEN rules. The theory of approximate reasoning, which provides a powerful framework for reasoning the imprecise and uncertain information, , is also reviewed. Additional properties of fuzzy systems are also discussed. (author)
Characterization of the Equilibrium Strategy of Fuzzy Bimatrix Games Based on L-R Fuzzy Variables
Directory of Open Access Journals (Sweden)
Cun-lin Li
2012-01-01
variable. In this paper, we generalized Maeda’s model to the non-symmetrical environment. In other words, we investigated the fuzzy bimatrix games based on nonsymmetrical L-R fuzzy variables. Then the pseudoinverse of a nonconstant monotone function was given and the concept of crisp parametric bimatrix games was introduced. At last, the existence condition of Nash equilibrium strategies of the fuzzy bimatrix games is proposed and (weak Pareto equilibrium of the fuzzy bimatrix games was obtained through the Nash equilibrium of the crisp parametric bimatrix.
Forest fire autonomous decision system based on fuzzy logic
Lei, Z.; Lu, Jianhua
2010-11-01
The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.
Directory of Open Access Journals (Sweden)
Jing Lu
2014-11-01
Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.
Myravyova, E. A.; Sharipov, M. I.; Radakina, D. S.
2017-10-01
During writing this work, the fuzzy controller with a double base of rules was studied, which was applied for the synthesis of the automated control system. A method for fuzzy controller adaptation has been developed. The adaptation allows the fuzzy controller to automatically compensate for parametric interferences that occur at the control object. Specifically, the fuzzy controller controlled the outlet steam temperature in the boiler unit BKZ-75-39 GMA. The software code was written in the programming support environment Unity Pro XL designed for fuzzy controller adaptation.
A Multitarget Tracking Video System Based on Fuzzy and Neuro-Fuzzy Techniques
Directory of Open Access Journals (Sweden)
Javier I. Portillo
2005-08-01
Full Text Available Automatic surveillance of airport surface is one of the core components of advanced surface movement, guidance, and control systems (A-SMGCS. This function is in charge of the automatic detection, identification, and tracking of all interesting targets (aircraft and relevant ground vehicles in the airport movement area. This paper presents a novel approach for object tracking based on sequences of video images. A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets from the image regions extracted in the images. The advantages of this approach are robustness, flexibility in the design to adapt to different situations, and efficiency for operation in real time, avoiding combinatorial enumeration. Results obtained in representative ground operations show the system capabilities to solve complex scenarios and improve tracking accuracy. Finally, an automatic procedure, based on neuro-fuzzy techniques, has been applied in order to obtain a set of rules from representative examples. Validation of learned system shows the capability to learn the suitable tracker decisions.
Image-based Fuzzy Parking Control of a Car-like Mobile Robot
Directory of Open Access Journals (Sweden)
Yin Yin Aye
2017-03-01
Full Text Available This paper develops a novel automatic parking system using an image-based fuzzy controller, where in the reasoning the slope and intercept of the desired target line are used for the inputs, and the steering angle of the robot is generated for the output. The objective of this study is that a robot equipped with a camera detects a rectangular parking frame, which is drawn on the floor, based on image processing. The desired target line to be followed by the robot is generated by using Hough transform from a captured image. The fuzzy controller is designed according to experiments of skilled driver, and the fuzzy rules are tuned and the fuzzy membership functions are optimized by experimentally for output. The effectiveness of the proposed method is demonstrated through some experimental results with an actual mobile robot
The Temperature Fuzzy Control System of Barleythe Malt Drying Based on Microcontroller
Gao, Xiaoyang; Bi, Yang; Zhang, Lili; Chen, Jingjing; Yun, Jianmin
The control strategy of temperature and humidity in the beer barley malt drying chamber based on fuzzy logic control was implemented.Expounded in this paper was the selection of parameters for the structure of the regulatory device, as well as the essential design from control rules based on the existing experience. A temperature fuzzy controller was thus constructed using relevantfuzzy logic, and humidity control was achieved by relay, ensured the situation of the humidity to control the temperature. The temperature's fuzzy control and the humidity real-time control were all processed by single chip microcomputer with assembly program. The experimental results showed that the temperature control performance of this fuzzy regulatory system,especially in the ways of working stability and responding speed and so on,was better than normal used PID control. The cost of real-time system was inquite competitive position. It was demonstrated that the system have a promising prospect of extensive application.
Fuzzy-set based contingency ranking
International Nuclear Information System (INIS)
Hsu, Y.Y.; Kuo, H.C.
1992-01-01
In this paper, a new approach based on fuzzy set theory is developed for contingency ranking of Taiwan power system. To examine whether a power system can remain in a secure and reliable operating state under contingency conditions, those contingency cases that will result in loss-of-load, loss-of generation, or islanding are first identified. Then 1P-1Q iteration of fast decoupled load flow is preformed to estimate post-contingent quantities (line flows, bus voltages) for other contingency cases. Based on system operators' past experience, each post-contingent quantity is assigned a degree of severity according to the potential damage that could be imposed on the power system by the quantity, should the contingency occurs. An approach based on fuzzy set theory is developed to deal with the imprecision of linguistic terms
Classifying Cervical Spondylosis Based on Fuzzy Calculation
Directory of Open Access Journals (Sweden)
Xinghu Yu
2014-01-01
Full Text Available Conventional evaluation of X-ray radiographs aiming at diagnosing cervical spondylosis (CS often depends on the clinic experiences, visual reading of radiography, and analysis of certain regions of interest (ROIs about clinician himself or herself. These steps are not only time consuming and subjective, but also prone to error for inexperienced clinicians due to low resolution of X-ray. This paper proposed an approach based on fuzzy calculation to classify CS. From the X-ray of CS manifestations, we extracted 10 effective ROIs to establish X-ray symptom-disease table of CS. Fuzzy calculation model based on the table can be carried out to classify CS and improve the diagnosis accuracy. The proposed model yields approximately 80.33% accuracy in classifying CS.
Gain Scheduling of PID Controller Based on Fuzzy Systems
Directory of Open Access Journals (Sweden)
Singh Sandeep
2016-01-01
Full Text Available This paper aims to utilize fuzzy rules and reasoning to determine the controller parameters and the PID controller generates the control signal. The objective of this study is to simulate the proposed scheme on various processes and arrive at results providing better response of the system when compared with best industrial auto-tuning technique: Ziegler-Nichols. The proposed scheme is based upon the Ultimate Gain (Ku and the Period (Tu of the system. The error and rate of change in error gains are tuned manually to get the desired response using LabVIEW. This can also be done with various optimization techniques. A thumb rule for choosing the ranges for Kc, Kd and Ki has been obtained experimentally.
Luo, Yi; Zhang, Tao; Li, Xiao-song
2016-05-01
To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.
Multi Groups Cooperation based Symbiotic Evolution for TSK-type Neuro-Fuzzy Systems Design.
Cheng, Yi-Chang; Hsu, Yung-Chi; Lin, Sheng-Fuu
2010-07-01
In this paper, a TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution method (TNFS-MGCSE) is proposed. The TNFS-MGCSE is developed from symbiotic evolution. The symbiotic evolution is different from traditional GAs (genetic algorithms) that each chromosome in symbiotic evolution represents a rule of fuzzy model. The MGCSE is different from the traditional symbiotic evolution; with a population in MGCSE is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperate with other groups to generate the better chromosomes by using the proposed cooperation based crossover strategy (CCS). In this paper, the proposed TNFS-MGCSE is used to evaluate by numerical examples (Mackey-Glass chaotic time series and sunspot number forecasting). The performance of the TNFS-MGCSE achieves excellently with other existing models in the simulations.
Design of the Fuzzy Control Systems Based on Genetic Algorithm for Intelligent Robots
Directory of Open Access Journals (Sweden)
Gyula Mester
2014-07-01
Full Text Available This paper gives the structure optimization of fuzzy control systems based on genetic algorithm in the MATLAB environment. The genetic algorithm is a powerful tool for structure optimization of the fuzzy controllers, therefore, in this paper, integration and synthesis of fuzzy logic and genetic algorithm has been proposed. The genetic algorithms are applied for fuzzy rules set, scaling factors and membership functions optimization. The fuzzy control structure initial consist of the 3 membership functions and 9 rules and after the optimization it is enough for the 4 DOF SCARA Robot control to compensate for structured and unstructured uncertainty. Fuzzy controller with the generalized bell membership functions can provide better dynamic performance of the robot then with the triangular membership functions. The proposed joint-space controller is computationally simple and had adaptability to a sudden change in the dynamics of the robot. Results of the computer simulation applied to the 4 DOF SCARA Robot show the validity of the proposed method.
A Fuzzy Logic-Based Video Subtitle and Caption Coloring System
Directory of Open Access Journals (Sweden)
Mohsen Davoudi
2012-01-01
Full Text Available An approach has been proposed for automatic adaptive subtitle coloring using fuzzy logic-based algorithm. This system changes the color of the video subtitle/caption to “pleasant” color according to color harmony and the visual perception of the image background colors. In the fuzzy analyzer unit, using RGB histograms of background image, the R, G, and B values for the color of the subtitle/caption are computed using fixed fuzzy IF-THEN rules fully driven from the color harmony theories to satisfy complementary color and subtitle-background color harmony conditions. A real-time hardware structure has been proposed for implementation of the front-end processing unit as well as the fuzzy analyzer unit.
International Nuclear Information System (INIS)
Peng Yafu; Hsu, C.-F.
2009-01-01
This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.
Fuzzy based attitude controller for flexible spacecraft with on/off thrusters
Knapp, Roger Glenn
1993-05-01
A fuzzy-based attitude controller is designed for attitude control of a generic spacecraft with on/off thrusters. The controller is comprised of packages of rules dedicated to addressing different objectives (e.g., disturbance rejection, low fuel consumption, avoiding the excitation of flexible appendages, etc.). These rule packages can be inserted or removed depending on the requirements of the particular spacecraft and are parameterized based on vehicle parameters such as inertia or operational parameters such as the maneuvering rate. Individual rule packages can be 'weighted' relative to each other to emphasize the importance of one objective relative to another. Finally, the fuzzy controller and rule packages are demonstrated using the high-fidelity Space Shuttle Interactive On-Orbit Simulator (IOS) while performing typical on-orbit operations and are subsequently compared with the existing shuttle flight control system performance.
a novel two – factor high order fuzzy time series with applications to ...
African Journals Online (AJOL)
HOD
objectively with multiple – factor fuzzy time series, recurrent number of fuzzy relationships, and assigning weights to elements of fuzzy forecasting rules. In this paper, a novel two – factor high – order fuzzy time series forecasting method based on fuzzy C-means clustering and particle swarm optimization is proposed to ...
Comparison of Fuzzy-Based Models in Landslide Hazard Mapping
Mijani, N.; Neysani Samani, N.
2017-09-01
Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR) and Quality Sum (QS). The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P) and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzy-gamma method with a minor difference is in the second order.
Energy Technology Data Exchange (ETDEWEB)
Velez D, D
2000-07-01
This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)
Certain and possible rules for decision making using rough set theory extended to fuzzy sets
Dekorvin, Andre; Shipley, Margaret F.
1993-01-01
Uncertainty may be caused by the ambiguity in the terms used to describe a specific situation. It may also be caused by skepticism of rules used to describe a course of action or by missing and/or erroneous data. To deal with uncertainty, techniques other than classical logic need to be developed. Although, statistics may be the best tool available for handling likelihood, it is not always adequate for dealing with knowledge acquisition under uncertainty. Inadequacies caused by estimating probabilities in statistical processes can be alleviated through use of the Dempster-Shafer theory of evidence. Fuzzy set theory is another tool used to deal with uncertainty where ambiguous terms are present. Other methods include rough sets, the theory of endorsements and nonmonotonic logic. J. Grzymala-Busse has defined the concept of lower and upper approximation of a (crisp) set and has used that concept to extract rules from a set of examples. We will define the fuzzy analogs of lower and upper approximations and use these to obtain certain and possible rules from a set of examples where the data is fuzzy. Central to these concepts will be the idea of the degree to which a fuzzy set A is contained in another fuzzy set B, and the degree of intersection of a set A with set B. These concepts will also give meaning to the statement; A implies B. The two meanings will be: (1) if x is certainly in A then it is certainly in B, and (2) if x is possibly in A then it is possibly in B. Next, classification will be looked at and it will be shown that if a classification will be looked at and it will be shown that if a classification is well externally definable then it is well internally definable, and if it is poorly externally definable then it is poorly internally definable, thus generalizing a result of Grzymala-Busse. Finally, some ideas of how to define consensus and group options to form clusters of rules will be given.
DEFF Research Database (Denmark)
Dotoli, M.; Jantzen, Jan
1999-01-01
The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....
Fuzzy logic based classification and assessment of pathological voice signals.
Aghazadeh, Babak Seyed; Heris, Hossein Khadivi
2009-01-01
In this paper an efficient fuzzy wavelet packet (WP) based feature extraction method and fuzzy logic based disorder assessment technique were used to investigate voice signals of patients suffering from unilateral vocal fold paralysis (UVFP). Mother wavelet function of tenth order Daubechies (d10) was employed to decompose signals in 5 levels. Next, WP coefficients were used to measure energy and Shannon entropy features at different spectral sub-bands. Consequently, using fuzzy c-means method, signals were clustered into 2 classes. The amount of fuzzy membership of pathological and normal signals in their corresponding clusters was considered as a measure to quantify the discrimination ability of features. A classification accuracy of 100 percent was achieved using an artificial neural network classifier. Finally, fuzzy c-means clustering method was used as a way of voice pathology assessment. Accordingly, fuzzy membership function based health index is proposed.
A fuzzy MCDM framework based on fuzzy measure and fuzzy integral for agile supplier evaluation
Dursun, Mehtap
2017-06-01
Supply chains need to be agile in order to response quickly to the changes in today's competitive environment. The success of an agile supply chain depends on the firm's ability to select the most appropriate suppliers. This study proposes a multi-criteria decision making technique for conducting an analysis based on multi-level hierarchical structure and fuzzy logic for the evaluation of agile suppliers. The ideal and anti-ideal solutions are taken into consideration simultaneously in the developed approach. The proposed decision approach enables the decision-makers to use linguistic terms, and thus, reduce their cognitive burden in the evaluation process. Furthermore, a hierarchy of evaluation criteria and their related sub-criteria is employed in the presented approach in order to conduct a more effective analysis.
Esophageal cancer prediction based on qualitative features using adaptive fuzzy reasoning method
Directory of Open Access Journals (Sweden)
Raed I. Hamed
2015-04-01
Full Text Available Esophageal cancer is one of the most common cancers world-wide and also the most common cause of cancer death. In this paper, we present an adaptive fuzzy reasoning algorithm for rule-based systems using fuzzy Petri nets (FPNs, where the fuzzy production rules are represented by FPN. We developed an adaptive fuzzy Petri net (AFPN reasoning algorithm as a prognostic system to predict the outcome for esophageal cancer based on the serum concentrations of C-reactive protein and albumin as a set of input variables. The system can perform fuzzy reasoning automatically to evaluate the degree of truth of the proposition representing the risk degree value with a weight value to be optimally tuned based on the observed data. In addition, the implementation process for esophageal cancer prediction is fuzzily deducted by the AFPN algorithm. Performance of the composite model is evaluated through a set of experiments. Simulations and experimental results demonstrate the effectiveness and performance of the proposed algorithms. A comparison of the predictive performance of AFPN models with other methods and the analysis of the curve showed the same results with an intuitive behavior of AFPN models.
Personnel Selection Based on Fuzzy Methods
Directory of Open Access Journals (Sweden)
Lourdes Cañós
2011-03-01
Full Text Available The decisions of managers regarding the selection of staff strongly determine the success of the company. A correct choice of employees is a source of competitive advantage. We propose a fuzzy method for staff selection, based on competence management and the comparison with the valuation that the company considers the best in each competence (ideal candidate. Our method is based on the Hamming distance and a Matching Level Index. The algorithms, implemented in the software StaffDesigner, allow us to rank the candidates, even when the competences of the ideal candidate have been evaluated only in part. Our approach is applied in a numerical example.
A fuzzy logic based PROMETHEE method for material selection problems
Directory of Open Access Journals (Sweden)
Muhammet Gul
2018-03-01
Full Text Available Material selection is a complex problem in the design and development of products for diverse engineering applications. This paper presents a fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation method based on trapezoidal fuzzy interval numbers that can be applied to the selection of materials for an automotive instrument panel. Also, it presents uniqueness in making a significant contribution to the literature in terms of the application of fuzzy decision-making approach to material selection problems. The method is illustrated, validated, and compared against three different fuzzy MCDM methods (fuzzy VIKOR, fuzzy TOPSIS, and fuzzy ELECTRE in terms of its ranking performance. Also, the relationships between the compared methods and the proposed scenarios for fuzzy PROMETHEE are evaluated via the Spearman’s correlation coefficient. Styrene Maleic Anhydride and Polypropylene are determined optionally as suitable materials for the automotive instrument panel case. We propose a generic fuzzy MCDM methodology that can be practically implemented to material selection problem. The main advantages of the methodology are consideration of the vagueness, uncertainty, and fuzziness to decision making environment.
Howard, Ayanna
2005-01-01
The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.
Use Of A Rule-Based System For Process Control
Bernard, John A.
1987-10-01
A rule-based, digital, closed-loop controller that incorporates 'fuzzy' logic has been designed and implemented for the control of power on the 5 MWt MIT Research Reactor under both steady-state and transient conditions. Based on this experience and having designed several other controllers for the same purpose, a comparison is made of the rule-based and analytic approaches. Differences in the division of labor between plant engineers and control specialists, the type of knowledge required and its acquisition, the use of performance criteria, and controller testing are discussed. The design, implementation, and calibration of rule-based controllers are reviewed with specific examples taken from the completed work at the MIT Research Reactor. An evaluation is then made of the possible role of rule-based technology in process control. It is noted that there are no comprehensive guidelines for the design of rule-based controllers and that such systems are quite difficult to calibrate. The advantage of rule-based systems is that they are generally more robust than their analy-tic counterparts. The rule-based and analytic technologies should therefore be used to complement each other with rule-based systems being employed both as backups to analytic controllers and as a means of improving the man-machine interface by providing human operators with the rationale for automatic control actions.
Fuzzy CMAC With incremental Bayesian Ying-Yang learning and dynamic rule construction.
Nguyen, M N
2010-04-01
Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.
A Belief Rule Based Expert System to Assess Mental Disorder under Uncertainty
DEFF Research Database (Denmark)
Hossain, Mohammad Shahadat; Afif Monrat, Ahmed; Hasan, Mamun
2016-01-01
to ignorance, incompleteness, and randomness. So, a belief rule-based expert system (BRBES) has been designed and developed with the capability of handling the uncertainties mentioned. Evidential reasoning works as the inference engine and the belief rule base as the knowledge representation schema...... in this BRBES. The study shows that the results generated by BRBES are more reliable than that of Fuzzy Rule-based expert system and from a human expert....
Fuzzy Genetic Algorithm Based on Principal Operation and Inequity Degree
Li, Fachao; Jin, Chenxia
In this paper, starting from the structure of fuzzy information, by distinguishing principal indexes and assistant indexes, give comparison of fuzzy information on synthesizing effect and operation of fuzzy optimization on principal indexes transformation, further, propose axiom system of fuzzy inequity degree from essence of constraint, and give an instructive metric method; Then, combining genetic algorithm, give fuzzy optimization methods based on principal operation and inequity degree (denoted by BPO&ID-FGA, for short); Finally, consider its convergence using Markov chain theory and analyze its performance through an example. All these indicate, BPO&ID-FGA can not only effectively merge decision consciousness into the optimization process, but possess better global convergence, so it can be applied to many fuzzy optimization problems.
International Nuclear Information System (INIS)
Zhang, Xu; Huang, Hong Zhong; Yu, Lanfeng
2006-01-01
Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer
Fuzzy association rule mining and classification for the prediction of malaria in South Korea.
Buczak, Anna L; Baugher, Benjamin; Guven, Erhan; Ramac-Thomas, Liane C; Elbert, Yevgeniy; Babin, Steven M; Lewis, Sheri H
2015-06-18
Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as Low, Medium or High, where these classes are defined as a total of 0-2, 3-16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak. Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7-8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the Medium class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict Low, Medium or High cases 7-8 weeks in the future. This paper
The Assessment of Ramp Metering Based on Fuzzy Logic
Taale, H.; Slager, Jan; Rosloot, Jeroen
1996-01-01
This paper deals with an assessment project and its results of an experiment with ramp metering based on fuzzy logic. In industrial processes and home appliances the control method based on fuzzy logic is being used more and
more. In traffic control however the use of this method is still in a
Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith
2015-01-01
Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.
Hongesombut, Komsan; Mitani, Yasunori; Tsuji, Kiichiro
Fuzzy logic control has been applied to various applications in power systems. Its control rules and membership functions are typically obtained by trial and error methods or experience knowledge. Proposed here is the application of a micro-genetic algorithm (micro-GA) to simultaneously design optimal membership functions and control rules for STATCOM. First, we propose a simple approach to extract membership functions and fuzzy logic control rules based on observed signals. Then a proposed GA will be applied to optimize membership functions and its control rules. To validate the effectiveness of the proposed approach, several simulation studies have been performed on a multimachine power system. Simulation results show that the proposed fuzzy logic controller with STATCOM can effectively and robustly enhance the damping of oscillations.
DEFF Research Database (Denmark)
Jantzen, Jan
1998-01-01
Design of a fuzzy controller requires more design decisions than usual, for example regarding rule base, inference engine, defuzzification, and data pre- and post processing. This tutorial paper identifies and describes the design choices related to single-loop fuzzy control, based on an internat...... on an international standard which is underway. The paper contains also a design approach, which uses a PID controller as a starting point. A design engineer can view the paper as an introduction to fuzzy controller design.......Design of a fuzzy controller requires more design decisions than usual, for example regarding rule base, inference engine, defuzzification, and data pre- and post processing. This tutorial paper identifies and describes the design choices related to single-loop fuzzy control, based...
A neural fuzzy controller learning by fuzzy error propagation
Nauck, Detlef; Kruse, Rudolf
1992-01-01
In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.
Valle, Marcos Eduardo; Sussner, Peter
2011-01-01
We recently employed concepts of mathematical morphology to introduce fuzzy morphological associative memories (FMAMs), a broad class of fuzzy associative memories (FAMs). We observed that many well-known FAM models can be classified as belonging to the class of FMAMs. Moreover, we developed a general learning strategy for FMAMs using the concept of adjunction of mathematical morphology. In this paper, we describe the properties of FMAMs with adjunction-based learning. In particular, we characterize the recall phase of these models. Furthermore, we prove several theorems concerning the storage capacity, noise tolerance, fixed points, and convergence of auto-associative FMAMs. These theorems are corroborated by experimental results concerning the reconstruction of noisy images. Finally, we successfully employ FMAMs with adjunction-based learning in order to implement fuzzy rule-based systems in an application to a time-series prediction problem in industry. Copyright © 2010 Elsevier Ltd. All rights reserved.
Fuzzy-logic-based safety verification framework for nuclear power plants.
Rastogi, Achint; Gabbar, Hossam A
2013-06-01
This article presents a practical implementation of a safety verification framework for nuclear power plants (NPPs) based on fuzzy logic where hazard scenarios are identified in view of safety and control limits in different plant process values. Risk is estimated quantitatively and compared with safety limits in real time so that safety verification can be achieved. Fuzzy logic is used to define safety rules that map hazard condition with required safety protection in view of risk estimate. Case studies are analyzed from NPP to realize the proposed real-time safety verification framework. An automated system is developed to demonstrate the safety limit for different hazard scenarios. © 2012 Society for Risk Analysis.
Designing boosting ensemble of relational fuzzy systems.
Scherer, Rafał
2010-10-01
A method frequently used in classification systems for improving classification accuracy is to combine outputs of several classifiers. Among various types of classifiers, fuzzy ones are tempting because of using intelligible fuzzy if-then rules. In the paper we build an AdaBoost ensemble of relational neuro-fuzzy classifiers. Relational fuzzy systems bond input and output fuzzy linguistic values by a binary relation; thus, fuzzy rules have additional, comparing to traditional fuzzy systems, weights - elements of a fuzzy relation matrix. Thanks to this the system is better adjustable to data during learning. In the paper an ensemble of relational fuzzy systems is proposed. The problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. The method described in the paper is tested on several known benchmarks and compared with other machine learning solutions from the literature.
Implementation of Fuzzy Logic Based Temperature-Controlled Heat ...
African Journals Online (AJOL)
This research then compares the control performance of PID (Proportional Integral and Derivative) and Fuzzy logic controllers. Conclusions are made based on these control performances. The results show that the control performance for a Fuzzy controller is quite similar to PID controller but comparatively gives a better ...
A fuzzy art neural network based color image processing and ...
African Journals Online (AJOL)
A fuzzy art neural network based color image processing and recognition scheme. ... color image pixels, which enables a Fuzzy ART neural network to process the RGB color images. The application of the algorithm was implemented and tested on a set of RGB color face images. Keywords: Color image processing, RGB, ...
Hybrid fuzzy charged system search algorithm based state estimation in distribution networks
Directory of Open Access Journals (Sweden)
Sachidananda Prasad
2017-06-01
Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.
Rule-Based Runtime Verification
Barringer, Howard; Goldberg, Allen; Havelund, Klaus; Sen, Koushik
2003-01-01
We present a rule-based framework for defining and implementing finite trace monitoring logics, including future and past time temporal logic, extended regular expressions, real-time logics, interval logics, forms of quantified temporal logics, and so on. Our logic, EAGLE, is implemented as a Java library and involves novel techniques for rule definition, manipulation and execution. Monitoring is done on a state-by-state basis, without storing the execution trace.
Kim, Kwang Soo; Beresford, Robert M
2012-02-01
A rule-based model was developed to assess climatic risk of European canker (Neonectria galligena), which is a major disease of apple in some temperate zones. A descriptive rule was derived from published observations on climatic conditions favorable for European canker development. Fuzzy set theory was used to evaluate the descriptive rule quantitatively. The amount and frequency of rainfall and the average number of hours between 11 and 16°C/day were used as input variables whose values were matched with terms in the rule, e.g., 'high' or 'low'. The degree of a term, e.g., the state of being high or low, to a given input value was determined using a membership function that converts an input value to a number between 0 and 1. The rule was evaluated by combining the degree of the terms associated with monthly climate data. Monthly risk index values derived using the rule were combined for pairs of consecutive months over 12 months. The annual risk of European canker development was represented by the maximum risk index value for 2 months combined. The membership function parameters were adjusted iteratively to achieve a specified level of risk at Talca (Chile), Loughgall (Northern Ireland), East Malling (UK), and Sebastopol (USA), where European canker risk was known. The rule-based model was validated with data collected from Canada, Ecuador, Denmark, Germany, Norway, Poland, Sweden, the Netherlands, New Zealand, and the Pacific Northwest (USA), where European canker has been reported to occur. In these validation areas, the model's risk prediction agreed with reports of disease occurrence. The rule-based model also predicted high risk areas more reliably than the climate matching model, CLIMEX, which relies on correlations between the spatial distribution of a species and climatic conditions. The combination of a climatic rule and fuzzy sets could be used for other applications where prediction of the geographic distribution of organisms is required for
Train Repathing in Emergencies Based on Fuzzy Linear Programming
Directory of Open Access Journals (Sweden)
Xuelei Meng
2014-01-01
Full Text Available Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.
Train repathing in emergencies based on fuzzy linear programming.
Meng, Xuelei; Cui, Bingmou
2014-01-01
Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model) to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.
A fuzzy classifier system for process control
Karr, C. L.; Phillips, J. C.
1994-01-01
A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.
Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators.
Bárdossy, András; Blinowska, Aleksandra; Kuzmicz, Wieslaw; Ollitrault, Jacky; Lewandowski, Michał; Przybylski, Andrzej; Jaworski, Zbigniew
2014-02-01
The paper presents a diagnostic algorithm for classifying cardiac tachyarrhythmias for implantable cardioverter defibrillators (ICDs). The main aim was to develop an algorithm that could reduce the rate of occurrence of inappropriate therapies, which are often observed in existing ICDs. To achieve low energy consumption, which is a critical factor for implantable medical devices, very low computational complexity of the algorithm was crucial. The study describes and validates such an algorithm and estimates its clinical value. The algorithm was based on the heart rate variability (HRV) analysis. The input data for our algorithm were: RR-interval (I), as extracted from raw intracardiac electrogram (EGM), and in addition two other features of HRV called here onset (ONS) and instability (INST). 6 diagnostic categories were considered: ventricular fibrillation (VF), ventricular tachycardia (VT), sinus tachycardia (ST), detection artifacts and irregularities (including extrasystoles) (DAI), atrial tachyarrhythmias (ATF) and no tachycardia (i.e. normal sinus rhythm) (NT). The initial set of fuzzy rules based on the distributions of I, ONS and INST in the 6 categories was optimized by means of a software tool for automatic rule assessment using simulated annealing. A training data set with 74 EGM recordings was used during optimization, and the algorithm was validated with a validation data set with 58 EGM recordings. Real life recordings stored in defibrillator memories were used. Additionally the algorithm was tested on 2 sets of recordings from the PhysioBank databases: MIT-BIH Arrhythmia Database and MIT-BIH Supraventricular Arrhythmia Database. A custom CMOS integrated circuit implementing the diagnostic algorithm was designed in order to estimate the power consumption. A dedicated Web site, which provides public online access to the algorithm, has been created and is available for testing it. The total number of events in our training and validation sets was 132. In
A fuzzy-logic-based approach to qualitative safety modelling for marine systems
International Nuclear Information System (INIS)
Sii, H.S.; Ruxton, Tom; Wang Jin
2001-01-01
Safety assessment based on conventional tools (e.g. probability risk assessment (PRA)) may not be well suited for dealing with systems having a high level of uncertainty, particularly in the feasibility and concept design stages of a maritime or offshore system. By contrast, a safety model using fuzzy logic approach employing fuzzy IF-THEN rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. A fuzzy-logic-based approach may be more appropriately used to carry out risk analysis in the initial design stages. This provides a tool for working directly with the linguistic terms commonly used in carrying out safety assessment. This research focuses on the development and representation of linguistic variables to model risk levels subjectively. These variables are then quantified using fuzzy sets. In this paper, the development of a safety model using fuzzy logic approach for modelling various design variables for maritime and offshore safety based decision making in the concept design stage is presented. An example is used to illustrate the proposed approach
Biomedical application of fuzzy association rules for identifying breast cancer biomarkers.
Lopez, F J; Cuadros, M; Cano, C; Concha, A; Blanco, A
2012-09-01
Current breast cancer research involves the study of many different prognosis factors: primary tumor size, lymph node status, tumor grade, tumor receptor status, p53, and ki67 levels, among others. High-throughput microarray technologies are allowing to better understand and identify prognostic factors in breast cancer. But the massive amounts of data derived from these technologies require the use of efficient computational techniques to unveil new and relevant biomedical knowledge. Furthermore, integrative tools are needed that effectively combine heterogeneous types of biomedical data, such as prognosis factors and expression data. The objective of this study was to integrate information from the main prognostic factors in breast cancer with whole-genome microarray data to identify potential associations among them. We propose the application of a data mining approach, called fuzzy association rule mining, to automatically unveil these associations. This paper describes the proposed methodology and illustrates how it can be applied to different breast cancer datasets. The obtained results support known associations involving the number of copies of chromosome-17, HER2 amplification, or the expression level of estrogen and progesterone receptors in breast cancer patients. They also confirm the correspondence between the HER2 status predicted by different testing methodologies (immunohistochemistry and fluorescence in situ hybridization). In addition, other interesting rules involving CDC6, SOX11, and EFEMP1 genes are identified, although further detailed studies are needed to statistically confirm these findings. As part of this study, a web platform implementing the fuzzy association rule mining approach has been made freely available at: http://www.genome2.ugr.es/biofar .
Ying, Hao
1993-01-01
The fuzzy controllers studied in this paper are the ones that employ N trapezoidal-shaped members for input fuzzy sets, Zadeh fuzzy logic and a centroid defuzzification algorithm for output fuzzy set. The author analytically proves that the structure of the fuzzy controllers is the sum of a global nonlinear controller and a local nonlinear proportional-integral-like controller. If N approaches infinity, the global controller becomes a nonlinear controller while the local controller disappears. If linear control rules are used, the global controller becomes a global two-dimensional multilevel relay which approaches a global linear proportional-integral (PI) controller as N approaches infinity.
Fuzzy logic-based tumor-marker profiles improved sensitivity in the diagnosis of lung cancer.
Schneider, Joachim; Bitterlich, Norman; Velcovsky, Hans-Georg; Morr, Harald; Katz, Norbert; Eigenbrodt, Erich
2002-06-01
The aim of this study was to improve the diagnostic efficiency of tumor markers in the diagnosis of lung cancer, by the mathematical evaluation of a tumor marker profile employing fuzzy logic modelling. A panel of four tumor markers, i.e., carcinoembryonic antigen (CEA), cytokeratin 19 antibody (CYFRA 21-1), neuron-specific enolase (NSE), squamous cell carcinoma-related antigen (SCC) and, additionally, C-reactive protein (CRP), was measured in 175 newly diagnosed lung cancer patients with different histological types and stages. Results were compared with those in 120 control subjects, including 27 with chronic obstructive pulmonary diseases (COPD), 65 with pneumoconiosis, and 11 persons with acute inflammatory lung diseases. A classificator was developed using a fuzzy-logic rule-based system. Application of the fuzzy-logic rule-based system to the tumor marker values of CYFRA 21-1, NSE, and CRP yielded an increase in sensitivity of approximately 20%, i.e., 92%, compared with that of the best single marker, CYFRA 21-1(sensitivity, 72%). The corresponding specificity was 95%. The fuzzy classificator significantly improved the sensitivity of the tumor marker panel in stages I and IIIa for non-small-cell lung cancer, as well as in "limited disease" status for small-cell lung cancer. Also, the diagnosis of other stages of lung cancer was enhanced. Fuzzy-logic analysis was proven to be more powerful than the measurement of single markers alone or combinations using multiple logistic regression analysis of all markers. Therefore, fuzzy logic offers a promising diagnostic tool to improve tumor marker efficiency.
A Sarsa(λ Algorithm Based on Double-Layer Fuzzy Reasoning
Directory of Open Access Journals (Sweden)
Quan Liu
2013-01-01
Full Text Available Solving reinforcement learning problems in continuous space with function approximation is currently a research hotspot of machine learning. When dealing with the continuous space problems, the classic Q-iteration algorithms based on lookup table or function approximation converge slowly and are difficult to derive a continuous policy. To overcome the above weaknesses, we propose an algorithm named DFR-Sarsa(λ based on double-layer fuzzy reasoning and prove its convergence. In this algorithm, the first reasoning layer uses fuzzy sets of state to compute continuous actions; the second reasoning layer uses fuzzy sets of action to compute the components of Q-value. Then, these two fuzzy layers are combined to compute the Q-value function of continuous action space. Besides, this algorithm utilizes the membership degrees of activation rules in the two fuzzy reasoning layers to update the eligibility traces. Applying DFR-Sarsa(λ to the Mountain Car and Cart-pole Balancing problems, experimental results show that the algorithm not only can be used to get a continuous action policy, but also has a better convergence performance.
Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.
Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios
2013-10-01
In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not
Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor
Energy Technology Data Exchange (ETDEWEB)
Ondrej Linda; Todd Vollmer; Jason Wright; Milos Manic
2011-04-01
Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.
Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV
Directory of Open Access Journals (Sweden)
Zain Anwar Ali
2016-05-01
Full Text Available In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV. The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST controller with model reference adaptive control (MRAC, in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.
Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.
Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad
2016-05-09
In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.
Approximate Reasoning with Fuzzy Booleans
van den Broek, P.M.; Noppen, J.A.R.
This paper introduces, in analogy to the concept of fuzzy numbers, the concept of fuzzy booleans, and examines approximate reasoning with the compositional rule of inference using fuzzy booleans. It is shown that each set of fuzzy rules is equivalent to a set of fuzzy rules with singleton crisp
Fuzzy logic based ELF magnetic field estimation in substations
International Nuclear Information System (INIS)
Kosalay, I.
2008-01-01
This paper examines estimation of the extremely low frequency magnetic fields (MF) in the power substation. First, the results of the previous relevant research studies and the MF measurements in a sample power substation are presented. Then, a fuzzy logic model based on the geometric definitions in order to estimate the MF distribution is explained. Visual software, which has a three-dimensional screening unit, based on the fuzzy logic technique, has been developed. (authors)
Fuzzy logic based ELF magnetic field estimation in substations.
Kosalay, Ilhan
2008-01-01
This paper examines estimation of the extremely low frequency magnetic fields (MF) in the power substation. First, the results of the previous relevant research studies and the MF measurements in a sample power substation are presented. Then, a fuzzy logic model based on the geometric definitions in order to estimate the MF distribution is explained. Visual software, which has a three-dimensional screening unit, based on the fuzzy logic technique, has been developed.
An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data.
Directory of Open Access Journals (Sweden)
Jinjun Tang
Full Text Available An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.
AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING
Directory of Open Access Journals (Sweden)
Liang Tang
2011-05-01
Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.
A Neuro-Control Design Based on Fuzzy Reinforcement Learning
DEFF Research Database (Denmark)
Katebi, S.D.; Blanke, M.
This paper describes a neuro-control fuzzy critic design procedure based on reinforcement learning. An important component of the proposed intelligent control configuration is the fuzzy credit assignment unit which acts as a critic, and through fuzzy implications provides adjustment mechanisms...... ones instruct the neuro-control unit to adjust its weights and are simultaneously stored in the memory unit during the training phase. In response to the internal reinforcement signal (set point threshold deviation), the stored information is retrieved by the action applier unit and utilized for re...
A New Fuzzy System Based on Rectangular Pyramid
Jiang, Mingzuo; Yuan, Xuehai; Li, Hongxing; Wang, Jiaxia
2015-01-01
A new fuzzy system is proposed in this paper. The novelty of the proposed system is mainly in the compound of the antecedents, which is based on the proposed rectangular pyramid membership function instead of t-norm. It is proved that the system is capable of approximating any continuous function of two variables to arbitrary degree on a compact domain. Moreover, this paper provides one sufficient condition of approximating function so that the new fuzzy system can approximate any continuous function of two variables with bounded partial derivatives. Finally, simulation examples are given to show how the proposed fuzzy system can be effectively used for function approximation. PMID:25874253
Fuzzy Critical Path Method Based on Lexicographic Ordering
Directory of Open Access Journals (Sweden)
Phani Bushan Rao P
2012-01-01
Full Text Available The Critical Path Method (CPM is useful for planning and control of complex projects. The CPM identifies the critical activities in the critical path of an activity network. The successful implementation of CPM requires the availability of clear determined time duration for each activity. However, in practical situations this requirement is usually hard to fulfil since many of activities will be executed for the first time. Hence, there is always uncertainty about the time durations of activities in the network planning. This has led to the development of fuzzy CPM. In this paper, we use a Lexicographic ordering method for ranking fuzzy numbers to a critical path method in a fuzzy project network, where the duration time of each activity is represented by a trapezoidal fuzzy number. The proposed method is compared with fuzzy CPM based on different ranking methods of fuzzy numbers. The comparison reveals that the method proposed in this paper is more effective in determining the activity criticalities and finding the critical path. This new method is simple in calculating fuzzy critical path than many methods proposed so far in literature.
Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree.
Kim, Jaekwon; Lee, Jongsik; Lee, Youngho
2015-07-01
The importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans. A model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model. The rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction. The accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models.
Maximum power point tracker based on fuzzy logic
International Nuclear Information System (INIS)
Daoud, A.; Midoun, A.
2006-01-01
The solar energy is used as power source in photovoltaic power systems and the need for an intelligent power management system is important to obtain the maximum power from the limited solar panels. With the changing of the sun illumination due to variation of angle of incidence of sun radiation and of the temperature of the panels, Maximum Power Point Tracker (MPPT) enables optimization of solar power generation. The MPPT is a sub-system designed to extract the maximum power from a power source. In the case of solar panels power source. the maximum power point varies as a result of changes in its electrical characteristics which in turn are functions of radiation dose, temperature, ageing and other effects. The MPPT maximum the power output from panels for a given set of conditions by detecting the best working point of the power characteristic and then controls the current through the panels or the voltage across them. Many MPPT methods have been reported in literature. These techniques of MPPT can be classified into three main categories that include: lookup table methods, hill climbing methods and computational methods. The techniques vary according to the degree of sophistication, processing time and memory requirements. The perturbation and observation algorithm (hill climbing technique) is commonly used due to its ease of implementation, and relative tracking efficiency. However, it has been shown that when the insolation changes rapidly, the perturbation and observation method is slow to track the maximum power point. In recent years, the fuzzy controllers are used for maximum power point tracking. This method only requires the linguistic control rules for maximum power point, the mathematical model is not required and therefore the implementation of this control method is easy to real control system. In this paper, we we present a simple robust MPPT using fuzzy set theory where the hardware consists of the microchip's microcontroller unit control card and
Genetic learning in rule-based and neural systems
Smith, Robert E.
1993-01-01
The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.
Rutkowski, L; Cpalka, K
2003-01-01
In this paper, we derive new neuro-fuzzy structures called flexible neuro-fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents; 2) certainty weights to aggregation of rules and to connectives of antecedents; and 3) parameterized families of T-norms and S-norms to fuzzy implication operators, to aggregation of rules and to connectives of antecedents. Our approach introduces more flexibility to the structure and design of neuro-fuzzy systems. Through computer simulations, we show that Mamdani-type systems are more suitable to approximation problems, whereas logical-type systems may be preferred for classification problems.
Design of interpretable fuzzy systems
Cpałka, Krzysztof
2017-01-01
This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
FUZZY BASED CONTRAST STRETCHING FOR MEDICAL IMAGE ENHANCEMENT
Directory of Open Access Journals (Sweden)
T.C. Raja Kumar
2011-07-01
Full Text Available Contrast Stretching is an important part in medical image processing applications. Contrast is the difference between two adjacent pixels. Fuzzy statistical values are analyzed and better results are produced in the spatial domain of the input image. The histogram mapping produces the resultant image with less impulsive noise and smooth nature. The probabilities of gray values are generated and the fuzzy set is determined from the position of the input image pixel. The result indicates the good performance of the proposed fuzzy based stretching. The inverse transform of the real values are mapped with the input image to generate the fuzzy statistics. This approach gives a flexible image enhancement for medical images in the presence of noises.
Directory of Open Access Journals (Sweden)
Somaye Yeylaghi
2017-06-01
Full Text Available In this paper, a novel hybrid method based on interval-valued fuzzy neural network for approximate of interval-valued fuzzy regression models, is presented. The work of this paper is an expansion of the research of real fuzzy regression models. In this paper interval-valued fuzzy neural network (IVFNN can be trained with crisp and interval-valued fuzzy data. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples and compare this method with existing methods.
A novel rules based approach for estimating software birthmark.
Nazir, Shah; Shahzad, Sara; Khan, Sher Afzal; Alias, Norma Binti; Anwar, Sajid
2015-01-01
Software birthmark is a unique quality of software to detect software theft. Comparing birthmarks of software can tell us whether a program or software is a copy of another. Software theft and piracy are rapidly increasing problems of copying, stealing, and misusing the software without proper permission, as mentioned in the desired license agreement. The estimation of birthmark can play a key role in understanding the effectiveness of a birthmark. In this paper, a new technique is presented to evaluate and estimate software birthmark based on the two most sought-after properties of birthmarks, that is, credibility and resilience. For this purpose, the concept of soft computing such as probabilistic and fuzzy computing has been taken into account and fuzzy logic is used to estimate properties of birthmark. The proposed fuzzy rule based technique is validated through a case study and the results show that the technique is successful in assessing the specified properties of the birthmark, its resilience and credibility. This, in turn, shows how much effort will be required to detect the originality of the software based on its birthmark.
A Novel Rules Based Approach for Estimating Software Birthmark
Directory of Open Access Journals (Sweden)
Shah Nazir
2015-01-01
Full Text Available Software birthmark is a unique quality of software to detect software theft. Comparing birthmarks of software can tell us whether a program or software is a copy of another. Software theft and piracy are rapidly increasing problems of copying, stealing, and misusing the software without proper permission, as mentioned in the desired license agreement. The estimation of birthmark can play a key role in understanding the effectiveness of a birthmark. In this paper, a new technique is presented to evaluate and estimate software birthmark based on the two most sought-after properties of birthmarks, that is, credibility and resilience. For this purpose, the concept of soft computing such as probabilistic and fuzzy computing has been taken into account and fuzzy logic is used to estimate properties of birthmark. The proposed fuzzy rule based technique is validated through a case study and the results show that the technique is successful in assessing the specified properties of the birthmark, its resilience and credibility. This, in turn, shows how much effort will be required to detect the originality of the software based on its birthmark.
Selection of Vendor Based on Intuitionistic Fuzzy Analytical Hierarchy Process
Directory of Open Access Journals (Sweden)
Prabjot Kaur
2014-01-01
Full Text Available Business environment is characterized by greater domestic and international competitive position in the global market. Vendors play a key role in achieving the so-called corporate competition. It is not easy however to identify good vendors because evaluation is based on multiple criteria. In practice, for VSP most of the input information about the criteria is not known precisely. Intuitionistic fuzzy set is an extension of the classical fuzzy set theory (FST, which is a suitable way to deal with impreciseness. In other words, the application of intuitionistic fuzzy sets instead of fuzzy sets means the introduction of another degree of freedom called nonmembership function into the set description. In this paper, we proposed a triangular intuitionistic fuzzy number based approach for the vendor selection problem using analytical hierarchy process. The crisp data of the vendors is represented in the form of triangular intuitionistic fuzzy numbers. By applying AHP which involves decomposition, pairwise comparison, and deriving priorities for the various levels of the hierarchy, an overall crisp priority is obtained for ranking the best vendor. A numerical example illustrates our method. Lastly a sensitivity analysis is performed to find the most critical criterion on the basis of which vendor is selected.
A fuzzy method for improving the functionality of search engines based on user's web interactions
Directory of Open Access Journals (Sweden)
Farzaneh Kabirbeyk
2015-04-01
Full Text Available Web mining has been widely used to discover knowledge from various sources in the web. One of the important tools in web mining is mining of web user’s behavior that is considered as a way to discover the potential knowledge of web user’s interaction. Nowadays, Website personalization is regarded as a popular phenomenon among web users and it plays an important role in facilitating user access and provides information of users’ requirements based on their own interests. Extracting important features about web user behavior plays a significant role in web usage mining. Such features are page visit frequency in each session, visit duration, and dates of visiting a certain pages. This paper presents a method to predict user’s interest and to propose a list of pages based on their interests by identifying user’s behavior based on fuzzy techniques called fuzzy clustering method. Due to the user’s different interests and use of one or more interest at a time, user’s interest may belong to several clusters and fuzzy clustering provide a possible overlap. Using the resulted cluster helps extract fuzzy rules. This helps detecting user’s movement pattern and using neural network a list of suggested pages to the users is provided.
Fuzzy-logic based strategy for validation of multiplex methods: example with qualitative GMO assays.
Bellocchi, Gianni; Bertholet, Vincent; Hamels, Sandrine; Moens, W; Remacle, José; Van den Eede, Guy
2010-02-01
This paper illustrates the advantages that a fuzzy-based aggregation method could bring into the validation of a multiplex method for GMO detection (DualChip GMO kit, Eppendorf). Guidelines for validation of chemical, bio-chemical, pharmaceutical and genetic methods have been developed and ad hoc validation statistics are available and routinely used, for in-house and inter-laboratory testing, and decision-making. Fuzzy logic allows summarising the information obtained by independent validation statistics into one synthetic indicator of overall method performance. The microarray technology, introduced for simultaneous identification of multiple GMOs, poses specific validation issues (patterns of performance for a variety of GMOs at different concentrations). A fuzzy-based indicator for overall evaluation is illustrated in this paper, and applied to validation data for different genetically modified elements. Remarks were drawn on the analytical results. The fuzzy-logic based rules were shown to be applicable to improve interpretation of results and facilitate overall evaluation of the multiplex method.
Real-time fuzzy-knowledge-based control of Baker's yeast production.
Siimes, T; Linko, P; von Numers, C; Nakajima, M; Endo, I
1995-01-20
A real-time fuzzy-knowledge-based system for fault diagnosis and control of bioprocesses was constructed using the object-oriented programming environment Small-talk/V Mac. The basic system was implemented in a Macintosh Quadra 900 computer and built to function connected on line to the process computer. Fuzzy logic was employed in handling uncertainties both in the knowledge and in measurements. The fuzzy sets defined for the process variables could be changed on-line according to process dynamics. Process knowledge was implemented in a graphical two-level hierachical knowledge base. In on-line process control the system first recognizes the current process phase on the basis of top-level rules in the knowledge-base. Then, according to the results of process diagnosis based on measurement data, the appropriate control strategy is subsequently inferred making use of the lower level rules describing the process during the phase in question. (c) 1995 John Wiley & Sons, Inc.
Edge detection methods based on generalized type-2 fuzzy logic
Gonzalez, Claudia I; Castro, Juan R; Castillo, Oscar
2017-01-01
In this book four new methods are proposed. In the first method the generalized type-2 fuzzy logic is combined with the morphological gra-dient technique. The second method combines the general type-2 fuzzy systems (GT2 FSs) and the Sobel operator; in the third approach the me-thodology based on Sobel operator and GT2 FSs is improved to be applied on color images. In the fourth approach, we proposed a novel edge detec-tion method where, a digital image is converted a generalized type-2 fuzzy image. In this book it is also included a comparative study of type-1, inter-val type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy edge detection methods were tested with benchmark images and synthetic images, in a grayscale and color format. Another contribution in this book is that the generalized type-2 fuzzy edge detector method is applied in the preproc...
Solar-Based Fuzzy Intelligent Water Sprinkle System
Directory of Open Access Journals (Sweden)
Riza Muhida
2012-03-01
Full Text Available A solar-based intelligent water sprinkler system project that has been developed to ensure the effectiveness in watering the plant is improved by making the system automated. The control system consists of an electrical capacitance soil moisture sensor installed into the ground which is interfaced to a controller unit of Motorola 68HC11 Handy board microcontroller. The microcontroller was programmed based on the decision rules made using fuzzy logic approach on when to water the lawn. The whole system is powered up by the solar energy which is then interfaced to a particular type of irrigation timer for plant fertilizing schedule and rain detector through a simple design of rain dual-collector tipping bucket. The controller unit automatically disrupted voltage signals sent to the control valves whenever irrigation was not needed. Using this system we combined the logic implementation in the area of irrigation and weather sensing equipment, and more efficient water delivery can be made possible.
Directory of Open Access Journals (Sweden)
Jinjun Tang
Full Text Available Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN, two learning processes are proposed: (1 a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2 a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE, root mean square error (RMSE, and mean absolute relative error (MARE are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR, instantaneous model (IM, linear model (LM, neural network (NN, and cumulative plots (CP.
Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles
Directory of Open Access Journals (Sweden)
Ahcene Farah
2002-06-01
Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles with more autonomy and intelligence is discussed. Second, the system for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.
Study on pattern recognition of Raman spectrum based on fuzzy neural network
Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing
2017-10-01
Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.
A DIFFERENT WEB-BASED GEOCODING SERVICE USING FUZZY TECHNIQUES
Directory of Open Access Journals (Sweden)
P. Pahlavani
2015-12-01
Full Text Available Geocoding – the process of finding position based on descriptive data such as address or postal code - is considered as one of the most commonly used spatial analyses. Many online map providers such as Google Maps, Bing Maps and Yahoo Maps present geocoding as one of their basic capabilities. Despite the diversity of geocoding services, users usually face some limitations when they use available online geocoding services. In existing geocoding services, proximity and nearness concept is not modelled appropriately as well as these services search address only by address matching based on descriptive data. In addition there are also some limitations in display searching results. Resolving these limitations can enhance efficiency of the existing geocoding services. This paper proposes the idea of integrating fuzzy technique with geocoding process to resolve these limitations. In order to implement the proposed method, a web-based system is designed. In proposed method, nearness to places is defined by fuzzy membership functions and multiple fuzzy distance maps are created. Then these fuzzy distance maps are integrated using fuzzy overlay technique for obtain the results. Proposed methods provides different capabilities for users such as ability to search multi-part addresses, searching places based on their location, non-point representation of results as well as displaying search results based on their priority.
A Comparative Analysis of Fuzzy Inference Engines in Context of ...
African Journals Online (AJOL)
PROF. O. E. OSUAGWU
profitability quantification in plastic recycling. [14] designs a neuro-fuzzy linguistic approach in optimizing the flow rate of a plastic extruder process. [15] presents fuzzy rule-base frame work for the management of tropical diseases. [16] proposes a fuzzy-neural network model for effective control of profitability in a paper.
Information Clustering Based on Fuzzy Multisets.
Miyamoto, Sadaaki
2003-01-01
Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…
Qi, Na; Zhang, Zhuoyong; Xiang, Yuhong; Yang, Yuping; Harrington, Peter de B
2015-01-01
Combined with terahertz time-domain spectroscopy, the feasibility of fast and reliable diagnosis of cervical carcinoma by a fuzzy rule-building expert system (FuRES) and a fuzzy optimal associative memory (FOAM) had been studied. The terahertz spectra of 52 specimens of cervix were collected in the work. The original data of samples were preprocessed by Savitzky-Golay first derivative (χderivative), principal component orthogonal signal correction (PC-OSC) and emphatic orthogonal signal correction to improve the performance of FuRES and FOAM models. The effect of the different pretreating methods to improve prediction accuracy was evaluated. The FuRES and FOAM models were validated using bootstrapped Latin-partition method. The obtained results showed that the FuRES and FOAM model optimized with the combination S-G first derivative and PC-OSC method had the better predictive ability with classification rates of 92.9 ± 0.4 and 92.5 ± 0.4 %, respectively. The proposed procedure proved that terahertz spectroscopy combined with fuzzy classifiers could supply a technology which has potential for diagnosis of cancerous tissue.
Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model
Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran
2014-09-01
Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.
Fuzzy subtractive clustering based prediction model for brand association analysis
Directory of Open Access Journals (Sweden)
Widodo Imam Djati
2018-01-01
Full Text Available The brand is one of the crucial elements that determine the success of a product. Consumers in determining the choice of a product will always consider product attributes (such as features, shape, and color, however consumers are also considering the brand. Brand will guide someone to associate a product with specific attributes and qualities. This study was designed to identify the product attributes and predict brand performance with those attributes. A survey was run to obtain the attributes affecting the brand. Subtractive Fuzzy Clustering was used to classify and predict product brand association based aspects of the product under investigation. The result indicates that the five attributes namely shape, ease, image, quality and price can be used to classify and predict the brand. Training step gives best FSC model with radii (ra = 0.1. It develops 70 clusters/rules with MSE (Training is 9.7093e-016. By using 14 data testing, the model can predict brand very well (close to the target with MSE is 0.6005 and its’ accuracy rate is 71%.
Directory of Open Access Journals (Sweden)
Pelin GORGEL
2013-01-01
Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.
Adding memory processing behaviors to the fuzzy behaviorist-based navigation of mobile robots
Energy Technology Data Exchange (ETDEWEB)
Pin, F.G.; Bender, S.R.
1996-05-01
Most fuzzy logic-based reasoning schemes developed for robot control are fully reactive, i.e., the reasoning modules consist of fuzzy rule bases that represent direct mappings from the stimuli provided by the perception systems to the responses implemented by the motion controllers. Due to their totally reactive nature, such reasoning systems can encounter problems such as infinite loops and limit cycles. In this paper, we proposed an approach to remedy these problems by adding a memory and memory-related behaviors to basic reactive systems. Three major types of memory behaviors are addressed: memory creation, memory management, and memory utilization. These are first presented, and examples of their implementation for the recognition of limit cycles during the navigation of an autonomous robot in a priori unknown environments are then discussed.
Directory of Open Access Journals (Sweden)
Xue-wen Chen
2018-01-01
Full Text Available A fuzzy increment controller is designed aimed at the vibration system of automobile active suspension with seven degrees of freedom (DOF. For decreasing vibration, an active control force is acquired by created Proportion-Integration-Differentiation (PID controller. The controller’s parameters are adjusted by a fuzzy increment controller with self-modifying parameters functions, which adopts the deviation and its rate of change of the body’s vertical vibration velocity and the desired value in the position of the front and rear suspension as the input variables based on 49 fuzzy control rules. Adopting Simulink, the fuzzy increment controller is validated under different road excitation, such as the white noise input with four-wheel correlation in time-domain, the sinusoidal input, and the pulse input of C-grade road surface. The simulation results show that the proposed controller can reduce obviously the vehicle vibration compared to other independent control types in performance indexes, such as, the root mean square value of the body’s vertical vibration acceleration, pitching, and rolling angular acceleration.
A clustering-based fuzzy wavelet neural network model for short-term load forecasting.
Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias
2013-10-01
Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.
Influence of the Migration Process on the Learning Performances of Fuzzy Knowledge Bases
DEFF Research Database (Denmark)
Akrout, Khaled; Baron, Luc; Balazinski, Marek
2007-01-01
, binary for the base of rules and real for the data base. This hybrid coding used with a set of specialized operators of reproduction proven to be an effective environment of learning. Simulations were made in this environment by adding a process of migration. While varying the number of populations......This paper presents the influence of the process of migration between populations in GENO-FLOU, which is an environment of learning of fuzzy knowledge bases by genetic algorithms. Initially the algorithm did not use the process of migration. For the learning, the algorithm uses a hybrid coding...
A Novel MADM Approach Based on Fuzzy Cross Entropy with Interval-Valued Intuitionistic Fuzzy Sets
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Xin Tong
2015-01-01
Full Text Available The paper presents a novel multiple attribute decision-making (MADM approach for the problem with completely unknown attribute weights in the framework of interval-valued intuitionistic fuzzy sets (IVIFS. First, the fuzzy cross entropy and discrimination degree of IVIFS are defied. Subsequently, based on the discrimination degree of IVIFS, a nonlinear programming model to minimize the total deviation of discrimination degrees between alternatives and the positive ideal solution PIS as well as the negative ideal solution (NIS is constructed to obtain the attribute weights and, then, the weighted discrimination degree. Finally, all the alternatives are ranked according to the relative closeness coefficients using the extended TOPSIS method, and the most desirable alternative is chosen. The proposed approach extends the research method of MADM based on the IVIF cross entropy. Finally, we illustrate the feasibility and validity of the proposed method by two examples.
Intuitionistic Fuzzy Hyperhomomorphism and Intuitionistic Fuzzy Normal Subhypergroups
Abdulmula, Karema S; Salleh, Abdul Razak
2012-01-01
The purpose of this paper is to introduce some basic concepts of intuitionistic fuzzy hyperalgebra. We continue our study of intuitionistic fuzzy hypergroups, by generalising the concept of fuzzy homomorphism and fuzzy normal subgroup based on fuzzy spaces to intuitionistic fuzzy hyperhomomorphism based on intuitionstic fuzzy spaces. We will introduce the notion of an intuitionistic fuzzy quotient hypergroup induced by an intuitionistic fuzzy normal subhypergroup under intuitionistic fuzzy hy...
An Innovative Fuzzy-Logic-Based Methodology for Trend Identification
International Nuclear Information System (INIS)
Wang Xin; Tsoukalas, Lefteri H.; Wei, Thomas Y.C.; Reifman, Jaques
2001-01-01
A new fuzzy-logic-based methodology for on-line signal trend identification is introduced. The methodology may be used for detecting the onset of nuclear power plant (NPP) transients at the earliest possible time and could be of great benefit to diagnostic, maintenance, and performance-monitoring programs. Although signal trend identification is complicated by the presence of noise, fuzzy methods can help capture important features of on-line signals, integrate the information included in these features, and classify incoming NPP signals into increasing, decreasing, and steady-state trend categories. A computer program named PROTREN is developed and tested for the purpose of verifying this methodology using NPP and simulation data. The results indicate that the new fuzzy-logic-based methodology is capable of detecting transients accurately, it identifies trends reliably and does not misinterpret a steady-state signal as a transient one
Fuzzy Clustering Methods and their Application to Fuzzy Modeling
DEFF Research Database (Denmark)
Kroszynski, Uri; Zhou, Jianjun
1999-01-01
. A method to obtain an optimized number of clusters is outlined. Based upon the cluster's characteristics, a behavioural model is formulated in terms of a rule-base and an inference engine. The article reviews several variants for the model formulation. Some limitations of the methods are listed......Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate...
A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network
Directory of Open Access Journals (Sweden)
Linhui Li
2015-01-01
Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.
Dynamic Optimization for IPS2 Resource Allocation Based on Improved Fuzzy Multiple Linear Regression
Directory of Open Access Journals (Sweden)
Maokuan Zheng
2017-01-01
Full Text Available The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2. The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An approach based on fuzzy multiple linear regression (FMLR is developed, which integrates the strength and concision of multiple linear regression in data fitting and factor analysis and the merit of fuzzy theory in dealing with uncertain or vague problems, which helps reduce those costs caused by unnecessary resource transfer. The iteration mechanism is introduced in the FMLR algorithm to improve forecasting accuracy. A case study of human resource allocation optimization in construction machinery industry is implemented to test and verify the proposed model.
Fuzzy logic based variable speed wind generation system
Energy Technology Data Exchange (ETDEWEB)
Simoes, M.G. [Sao Paulo Univ., SP (Brazil). Escola Politecnica. PMC - Mecatronica; Bose, B.K. [Tennessee Univ., Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, Ronal J. [Environmental Protection Agency, Research Triangle Park, NC (United States). Air and Energy Engineering Research Lab.
1996-12-31
This work demonstrates the successful application of fuzzy logic to enhance the performance and control of a variable speed wind generation system. A maximum power point tracker control is performed with three fuzzy controllers, without wind velocity measurement, and robust to wind vortex and turbine torque ripple. A squirrel cage induction generator feeds the power to a double-sided PWM converter system which pumps the power to a utility grid or supplies to an autonomous system. The fuzzy logic controller FLC-1 searches on-line the generator speed so that the aerodynamic efficiency of the wind turbine is optimized. A second fuzzy controller FLC-2 programs the machine flux by on-line search so as to optimize the machine-converter system wind vortex. Detailed analysis and simulation studies were performed for development of the control strategy and fuzzy algorithms, and a DSP TMS320C30 based hardware with C control software was built for the performance evaluation of a laboratory experimental set-up. The theoretical development was fully validated and the system is ready to be reproduced in a higher power installation. (author) 7 refs., 3 figs., 1 tab.
Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach
Ramjeet Singh Yadav; Vijendra Pratap Singh
2011-01-01
We have proposed a Fuzzy Expert System (FES) for student academic performance evaluation based on fuzzy logic techniques. A suitable fuzzy inference mechanism and associated rule has been discussed. It introduces the principles behind fuzzy logic and illustrates how these principles could be applied by educators to evaluating student academic performance. Several approaches using fuzzy logic techniques have been proposed to provide a practical method for evaluating student academic performanc...
Fuzzy Model-based Pitch Stabilization and Wing Vibration Suppression of Flexible Wing Aircraft.
Ayoubi, Mohammad A.; Swei, Sean Shan-Min; Nguyen, Nhan T.
2014-01-01
This paper presents a fuzzy nonlinear controller to regulate the longitudinal dynamics of an aircraft and suppress the bending and torsional vibrations of its flexible wings. The fuzzy controller utilizes full-state feedback with input constraint. First, the Takagi-Sugeno fuzzy linear model is developed which approximates the coupled aeroelastic aircraft model. Then, based on the fuzzy linear model, a fuzzy controller is developed to utilize a full-state feedback and stabilize the system while it satisfies the control input constraint. Linear matrix inequality (LMI) techniques are employed to solve the fuzzy control problem. Finally, the performance of the proposed controller is demonstrated on the NASA Generic Transport Model (GTM).
Fuzzy logic particle tracking velocimetry
Wernet, Mark P.
1993-01-01
Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time.
Lewandowski, Michał; Przybylski, Andrzej; Kuźmicz, Wiesław; Szwed, Hanna
2013-09-01
The aim of the study was to analyze the value of a completely new fuzzy logic-based detection algorithm (FA) in comparison with arrhythmia classification algorithms used in existing ICDs in order to demonstrate whether the rate of inappropriate therapies can be reduced. On the basis of the RR intervals database containing arrhythmia events and controls recordings from the ICD memory a diagnostic algorithm was developed and tested by a computer program. This algorithm uses the same input signals as existing ICDs: RR interval as the primary input variable and two variables derived from it, onset and stability. However, it uses 15 fuzzy rules instead of fixed thresholds used in existing devices. The algorithm considers 6 diagnostic categories: (1) VF (ventricular fibrillation), (2) VT (ventricular tachycardia), (3) ST (sinus tachycardia), (4) DAI (artifacts and heart rhythm irregularities including extrasystoles and T-wave oversensing-TWOS), (5) ATF (atrial and supraventricular tachycardia or fibrillation), and 96) NT (sinus rhythm). This algorithm was tested on 172 RR recordings from different ICDs in the follow-up of 135 patients. All diagnostic categories of the algorithm were present in the analyzed recordings: VF (n = 35), VT (n = 48), ST (n = 14), DAI (n = 32), ATF (n = 18), NT (n = 25). Thirty-eight patients (31.4%) in the studied group received inappropriate ICD therapies. In all these cases the final diagnosis of the algorithm was correct (19 cases of artifacts, 11 of atrial fibrillation and 8 of ST) and fuzzy rules algorithm implementation would have withheld unnecessary therapies. Incidence of inappropriate therapies: 3 vs. 38 (the proposed algorithm vs. ICD diagnosis, respectively) differed significantly (p fuzzy logic based algorithm seems to be promising and its implementation could diminish ICDs inappropriate therapies. We found FA usefulness in correct diagnosis of sinus tachycardia, atrial fibrillation and artifacts in comparison with tested ICDs.
Nuclear Power Plant Operator Reliability Research Based on Fuzzy Math
Directory of Open Access Journals (Sweden)
Fang Xiang
2011-01-01
Full Text Available This paper makes use of the concept and theory of fuzzy number in fuzzy mathematics, to research for the response time of operator in accident of Chinese nuclear power plant. Through the quantitative analysis for the performance shape factors (PSFs which influence the response time of operators, the formula of the operator response time is obtained based on the possibilistic fuzzy linear regression model which is used for the first time in this kind of research. The research result shows that the correct research method can be achieved through the analysis of the information from a small sample. This method breaks through the traditional research method and can be used not only for the reference to the safe operation of nuclear power plant, but also in other areas.
A Geometric Fuzzy-Based Approach for Airport Clustering
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Maria Nadia Postorino
2014-01-01
Full Text Available Airport classification is a common need in the air transport field due to several purposes—such as resource allocation, identification of crucial nodes, and real-time identification of substitute nodes—which also depend on the involved actors’ expectations. In this paper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance among airports—which corresponds to a measure of similarity—has in fact an intrinsic fuzzy nature due to the airport specific characteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained results are in line with expectations.
Fuzzy Evidence in Identification, Forecasting and Diagnosis
Rotshtein, Alexander P
2012-01-01
The purpose of this book is to present a methodology for designing and tuning fuzzy expert systems in order to identify nonlinear objects; that is, to build input-output models using expert and experimental information. The results of these identifications are used for direct and inverse fuzzy evidence in forecasting and diagnosis problem solving. The book is organised as follows: Chapter 1 presents the basic knowledge about fuzzy sets, genetic algorithms and neural nets necessary for a clear understanding of the rest of this book. Chapter 2 analyzes direct fuzzy inference based on fuzzy if-then rules. Chapter 3 is devoted to the tuning of fuzzy rules for direct inference using genetic algorithms and neural nets. Chapter 4 presents models and algorithms for extracting fuzzy rules from experimental data. Chapter 5 describes a method for solving fuzzy logic equations necessary for the inverse fuzzy inference in diagnostic systems. Chapters 6 and 7 are devoted to inverse fuzzy inference based on fu...
Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic
Lara-Rosano, Felipe
1992-01-01
In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available.
International Nuclear Information System (INIS)
Liu Yongkuo; Xia Hong; Xie Chunli; Chen Zhihui; Chen Hongxia
2007-01-01
Rough set theory and fuzzy neural network are combined, to take full advantages of the two of them. Based on the reduction technology to knowledge of Rough set method, and by drawing the simple rule from a large number of initial data, the fuzzy neural network was set up, which was with better topological structure, improved study speed, accurate judgment, strong fault-tolerant ability, and more practical. In order to test the validity of the method, the inverted U-tubes break accident of Steam Generator and etc are used as examples, and many simulation experiments are performed. The test result shows that it is feasible to incorporate the fault intelligence diagnosis method based on rough set and fuzzy neural network in the nuclear power plant equipment, and the method is simple and convenience, with small calculation amount and reliable result. (authors)
Rule-based Modelling and Tunable Resolution
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Russ Harmer
2009-11-01
Full Text Available We investigate the use of an extension of rule-based modelling for cellular signalling to create a structured space of model variants. This enables the incremental development of rule sets that start from simple mechanisms and which, by a gradual increase in agent and rule resolution, evolve into more detailed descriptions.
Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization.
Miranda, Gisele Helena Barboni; Felipe, Joaquim Cezar
2015-09-01
Fuzzy logic can help reduce the difficulties faced by computational systems to represent and simulate the reasoning and the style adopted by radiologists in the process of medical image analysis. The study described in this paper consists of a new method that applies fuzzy logic concepts to improve the representation of features related to image description in order to make it semantically more consistent. Specifically, we have developed a computer-aided diagnosis tool for automatic BI-RADS categorization of breast lesions. The user provides parameters such as contour, shape and density and the system gives a suggestion about the BI-RADS classification. Initially, values of malignancy were defined for each image descriptor, according to the BI-RADS standard. When analyzing contour, for example, our method considers the matching of features and linguistic variables. Next, we created the fuzzy inference system. The generation of membership functions was carried out by the Fuzzy Omega algorithm, which is based on the statistical analysis of the dataset. This algorithm maps the distribution of different classes in a set. Images were analyzed by a group of physicians and the resulting evaluations were submitted to the Fuzzy Omega algorithm. The results were compared, achieving an accuracy of 76.67% for nodules and 83.34% for calcifications. The fit of definitions and linguistic rules to numerical models provided by our method can lead to a tighter connection between the specialist and the computer system, yielding more effective and reliable results. Copyright © 2014 Elsevier Ltd. All rights reserved.
Declarative and Procedural Semantics of Fuzzy Similarity Based Unification
Czech Academy of Sciences Publication Activity Database
Vojtáš, Peter
2000-01-01
Roč. 36, č. 6 (2000), s. 707-720 ISSN 0023-5954 Grant - others:VEGA(SK) 1/7557/20; VEGA(SK) 1/7555/20 Institutional research plan: AV0Z1030915 Keywords : similarity based unification * soundness and completeness * fuzzy logic programming Subject RIV: BA - General Mathematics http://dml.cz/handle/10338.dmlcz/135382
Directory of Open Access Journals (Sweden)
Qifeng Wang
2015-01-01
Full Text Available Intuitionistic fuzzy information aggregation plays an important role in intuitionistic fuzzy set theory and is widely used in group decision making. In this paper, an induced intuitionistic fuzzy Einstein hybrid aggregation operator (I-IFEHA is investigated for supplier selection group decision making in logistics service value cocreation based on fuzzy measures. We first introduce some aggregation operators and Einstein operations on intuitionistic fuzzy sets and develop a new induced intuitionistic fuzzy Einstein hybrid aggregation operator to accommodate the environment in which the given arguments are intuitionistic fuzzy values. Then, we study the supplier selection group decision model in logistics service value cocreation based on intuitionistic fuzzy sets with the I-IFEHA operator. Finally, an example of 3PL supplier selection in logistics service value cocreation environment is given to verify the developed approach and to demonstrate the effectiveness of the developed approach.
Ahmadianfar, Iman; Adib, Arash; Taghian, Mehrdad
2017-10-01
The reservoir hedging rule curves are used to avoid severe water shortage during drought periods. In this method reservoir storage is divided into several zones, wherein the rationing factors are changed immediately when water storage level moves from one zone to another. In the present study, a hedging rule with fuzzy rationing factors was applied for creating a transition zone in up and down each rule curve, and then the rationing factor will be changed in this zone gradually. For this propose, a monthly simulation model was developed and linked to the non-dominated sorting genetic algorithm for calculation of the modified shortage index of two objective functions involving water supply of minimum flow and agriculture demands in a long-term simulation period. Zohre multi-reservoir system in south Iran has been considered as a case study. The results of the proposed hedging rule have improved the long-term system performance from 10 till 27 percent in comparison with the simple hedging rule, where these results demonstrate that the fuzzification of hedging factors increase the applicability and the efficiency of the new hedging rule in comparison to the conventional rule curve for mitigating the water shortage problem.
A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network.
Mohammed, Mohammed Falah; Lim, Chee Peng
2017-02-01
In this paper, we extend our previous work on the Enhanced Fuzzy Min-Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a new k-nearest hyperbox expansion rule (for selection of a new winning hyperbox) is first introduced to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox. A pruning strategy is then deployed to further reduce the network complexity in the presence of noisy data. The effectiveness of the proposed network is evaluated using a number of benchmark data sets. The results compare favorably with those from other related models. The findings indicate that the newly introduced hyperbox winner selection rule coupled with the pruning strategy are useful for undertaking pattern classification problems. Copyright © 2016 Elsevier Ltd. All rights reserved.
Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement
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V. Magudeeswaran
2013-01-01
Full Text Available Fuzzy logic-based histogram equalization (FHE is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the original image and then equalizes them independently to preserve image brightness. The qualitative and quantitative analyses of proposed FHE algorithm are evaluated using two well-known parameters like average information contents (AIC and natural image quality evaluator (NIQE index for various images. From the qualitative and quantitative measures, it is interesting to see that this proposed method provides optimum results by giving better contrast enhancement and preserving the local information of the original image. Experimental result shows that the proposed method can effectively and significantly eliminate washed-out appearance and adverse artifacts induced by several existing methods. The proposed method has been tested using several images and gives better visual quality as compared to the conventional methods.
A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Barari, Amin
2011-01-01
Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm...... for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over-segmentation...
Sutrisno; Widowati; Sunarsih; Kartono
2018-01-01
In this paper, a mathematical model in quadratic programming with fuzzy parameter is proposed to determine the optimal strategy for integrated inventory control and supplier selection problem with fuzzy demand. To solve the corresponding optimization problem, we use the expected value based fuzzy programming. Numerical examples are performed to evaluate the model. From the results, the optimal amount of each product that have to be purchased from each supplier for each time period and the optimal amount of each product that have to be stored in the inventory for each time period were determined with minimum total cost and the inventory level was sufficiently closed to the reference level.
M. ZANGIABADI; H. R. MALEKI
2007-01-01
In the real-world optimization problems, coefficients of the objective function are not known precisely and can be interpreted as fuzzy numbers. In this paper we define the concepts of optimality for linear programming problems with fuzzy parameters based on those for multiobjective linear programming problems. Then by using the concept of comparison of fuzzy numbers, we transform a linear programming problem with fuzzy parameters to a multiobjective linear programming problem. To this end, w...
Model-based fuzzy control solutions for a laboratory Antilock Braking System
DEFF Research Database (Denmark)
Precup, Radu-Emil; Spataru, Sergiu; Rǎdac, Mircea-Bogdan
2010-01-01
This paper gives two original model-based fuzzy control solutions dedicated to the longitudinal slip control of Antilock Braking System laboratory equipment. The parallel distributed compensation leads to linear matrix inequalities which guarantee the global stability of the fuzzy control systems....... Real-time experimental results validate the new fuzzy control solutions....
DEFF Research Database (Denmark)
Mauricio Iglesias, Miguel; Vangsgaard, Anna Katrine; Gernaey, Krist
Diagnosis and control modules based on fuzzy set theory were tested for novel bioreactor monitoring and control. Two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation...... in diagnosis and control allowed a more intuitive design of the membership functions and the production rules. Hence, the resulting diagnosis-control module is simple to tune, update and maintain while providing a good control performance. In particular the diagnosis-control system was designed for a complete...
WIDE-AREA BASED ON COORDINATED TUNING OF FUZZY PSS AND FACTS CONTROLLER IN MULTI-MACHINE ENVIRONMENT
Directory of Open Access Journals (Sweden)
Homayoun Ebrahimian
2016-03-01
Full Text Available In this paper coordination of fuzzy power system stabilizer (FPSS and flexible ac transmission systems (FACTS have been considered in a multi-machine power system. The proposed model, has been applied for a wide-area power system. The proposed FPSS presented with local, nonlinear feedbacks, and the corresponding control synthesis conditions are given in terms of solutions to a set of linear matrix inequalities (LMIs. For this model, in fuzzy control synthesis, the new proposed control design method is based on fewer fuzzy rules and less computational burden. Also, the parameters of FACTS controller have been evaluated by improved honey bee mating optimization (IHBMO. The effectiveness of the proposed method has been applied over two case studies of single-machine infinite-bus (SMIB and two areas four machine (TAFM Kundur’s power system. The obtained results demonstrate the superiority of proposed strategy.
Directory of Open Access Journals (Sweden)
A.K. Parida
2016-09-01
Full Text Available In this paper Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system is presented for the prediction and analysis of financial and electrical energy market data. The normally used TSK-type feedforward fuzzy neural network is unable to take the full advantage of the use of the linear fuzzy rule base in accurate input–output mapping and hence the consequent part of the rule base is made nonlinear using polynomial or arithmetic basis functions. Further the Chebyshev polynomial functions provide an expanded nonlinear transformation to the input space thereby increasing its dimension for capturing the nonlinearities and chaotic variations in financial or energy market data streams. Also the locally recurrent neuro-fuzzy information system (LRNFIS includes feedback loops both at the firing strength layer and the output layer to allow signal flow both in forward and backward directions, thereby making the LRNFIS mimic a dynamic system that provides fast convergence and accuracy in predicting time series fluctuations. Instead of using forward and backward least mean square (FBLMS learning algorithm, an improved Firefly-Harmony search (IFFHS learning algorithm is used to estimate the parameters of the consequent part and feedback loop parameters for better stability and convergence. Several real world financial and energy market time series databases are used for performance validation of the proposed LRNFIS model.
Multi-fuzzy Rough Sets based on Implicators and Continuous t-norms
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Gayathri Varma
2017-12-01
Full Text Available This paper extends the study of multi-fuzzy rough sets using an implicator and a continuous t-norm and thus introduces multi-fuzzy rough sets based on fuzzy logical connectives. In this constructive approach, a pair of lower and upper approximation operators determined by an implicator and a triangular norm is defined. The fundamental properties of these approximation operators are examined. Connections between multi-fuzzy relations and the newly constructed multi-fuzzy rough approximation operators are also established. The theory of multi-fuzzy rough sets is analysed using an operator oriented view in the later sections. The lower and upper approximation operators are characterized by axioms. Various axiom sets of lower and upper multi-fuzzy set theoretic operators guarantee the existence of different types of multi-fuzzy relations which produce the same operators.
International Nuclear Information System (INIS)
Da-Zhong, Ma; Hua-Guang, Zhang; Zhan-Shan, Wang; Jian, Feng
2010-01-01
In this paper the fault tolerant synchronization of two chaotic systems based on fuzzy model and sample data is investigated. The problem of fault tolerant synchronization is formulated to study the global asymptotical stability of the error system with the fuzzy sampled-data controller which contains a state feedback controller and a fault compensator. The synchronization can be achieved no matter whether the fault occurs or not. To investigate the stability of the error system and facilitate the design of the fuzzy sampled-data controller, a Takagi–Sugeno (T–S) fuzzy model is employed to represent the chaotic system dynamics. To acquire good performance and produce a less conservative analysis result, a new parameter-dependent Lyapunov–Krasovksii functional and a relaxed stabilization technique are considered. The stability conditions based on linear matrix inequality are obtained to achieve the fault tolerant synchronization of the chaotic systems. Finally, a numerical simulation is shown to verify the results. (general)
Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.
Kamesh, Reddi; Rani, K Yamuna
2016-09-01
A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Miltiadis Alamaniotis; Vivek Agarwal
2014-10-01
This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.
Anwar, Farhat; Masud, Mosharrof H.; Latif, Suhaimi A.
2013-12-01
Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6.
International Nuclear Information System (INIS)
Anwar, Farhat; Masud, Mosharrof H; Latif, Suhaimi A
2013-01-01
Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6
Posturography stability score generation for stroke patient using Kinect: Fuzzy based approach.
Mazumder, Oishee; Chakravarty, Kingshuk; Chatterjee, Debatri; Sinha, Aniruddha; Das, Abhijit
2017-07-01
Aim of this paper is to formulate a posturography stability score for stroke patients using fuzzy logic. Postural instability is one of the prominent symptoms of stroke, dementia, parkinsons disease, myopathy, etc. and is the major precursor of fall. Conventional scoring techniques used to assess postural stability require manual intervention and are dependent on live interaction with physiotherapist. We propose a novel scoring technique to calculate static stability of a person using posturography features acquired by Kinect sensor, which do not require any manual intervention or expert guidance, is cost effective and hence are ideal for tele rehabilitation purpose. Stability analysis is done during Single Limb Stance (SLS) exercise. Kinect sensor is used to calculate three features, naming SLS duration, vibration index, calculated from mean vibration of twenty joints and sway area of Centre of Mass (CoM). Based on the variation of these features, a fuzzy rule base is generated which calculates a static stability score. One way analysis of variance (Anova) between a group of stroke population and healthy individuals under study validates the reliability of the proposed scorer. Generated fuzzy score are comparable with standard stability scorer like Berg Balance scale and fall risk assessment tool like Johns Hopkins scale. Stability score, besides providing an index of overall stability can also be used as a fall predictability index.
Energy Technology Data Exchange (ETDEWEB)
Barin, A.; Canha, L.; Abaide, A.; Magnago, K. [Federal University of Santa Maria (UFSM), RS (Brazil)], E-mail: chbarin@gmail.com; Machado, R. [Universidade de Sao Paulo (EESC/USP), Sao Carlos, SP (Brazil). Escola de Engenharia], E-mail: rquadros@sel.eesc.usp.br
2009-07-01
A multicriteria analysis to manage de renewable sources of energy is presented, identifying the most appropriate hybrid system to be used as distributed generation of electric energy using biogas. In this methodology, fuzzy sets and rules are defined simulated in the software MATLAB, where the main characteristics of the operation and application of hybrid systems of electric power generation are considered. The main generation system, that can use the biogas, as micro turbines and fuel cells, are evaluated. Afterwards, the systems of energy storage are analyzed: flywheel, H{sub 2} storage and conventional and redox batteries. For the development of the proposed methodology, it was considered the following criteria: efficiency, costs, technological maturity, environmental impacts, the amplitude of the system action (power range), useful life, co-generation possibility and operation temperature. A classification, by priority order, for the use of the sources and storages associated to the environment and cost scenarios is also presented.
DEFF Research Database (Denmark)
Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan
2000-01-01
and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c...
A novel, fuzzy-based air quality index (FAQI) for air quality assessment
Sowlat, Mohammad Hossein; Gharibi, Hamed; Yunesian, Masud; Tayefeh Mahmoudi, Maryam; Lotfi, Saeedeh
2011-04-01
The ever increasing level of air pollution in most areas of the world has led to development of a variety of air quality indices for estimation of health effects of air pollution, though the indices have their own limitations such as high levels of subjectivity. Present study, therefore, aimed at developing a novel, fuzzy-based air quality index (FAQI ) to handle such limitations. The index developed by present study is based on fuzzy logic that is considered as one of the most common computational methods of artificial intelligence. In addition to criteria air pollutants (i.e. CO, SO 2, PM 10, O 3, NO 2), benzene, toluene, ethylbenzene, xylene, and 1,3-butadiene were also taken into account in the index proposed, because of their considerable health effects. Different weighting factors were then assigned to each pollutant according to its priority. Trapezoidal membership functions were employed for classifications and the final index consisted of 72 inference rules. To assess the performance of the index, a case study was carried out employing air quality data at five different sampling stations in Tehran, Iran, from January 2008 to December 2009, results of which were then compared to the results obtained from USEPA air quality index (AQI). According to the results from present study, fuzzy-based air quality index is a comprehensive tool for classification of air quality and tends to produce accurate results. Therefore, it can be considered useful, reliable, and suitable for consideration by local authorities in air quality assessment and management schemes. Fuzzy-based air quality index (FAQI).
CAC Algorithm Based on Fuzzy Logic
Directory of Open Access Journals (Sweden)
Ľubomír DOBOŠ
2009-05-01
Full Text Available Quality of Service (QoS represent one ofmajor parameters that describe mobile wirelesscommunication systems. Thanks growing popularity ofmobile communication in last years, there is anincreasing expansion of connection admission controlschemes (CAC that plays important role in QoSdelivering in terms of connection blocking probability,connection dropping probability, data loss rate andsignal quality.With expansion of services provided by the mobilenetworks growing the requirements to QoS andtogether growing requirements to CAC schemes.Therefore, still more sophisticated CAC schemes arerequired to guarantee the QoS. This paper containsshort introduction into division of connectionadmission control schemes and presents thresholdoriented CAC scheme with fuzzy logic used foradaptation of the threshold value.
Evaluation of a Multi-Variable Self-Learning Fuzzy Logic Controller ...
African Journals Online (AJOL)
In spite of the usefulness of fuzzy control, its main drawback comes from lack of a systematic control design methodology. The most challenging aspect of the design of a fuzzy logic controller is the elicitation of the control rules for its rule base. In this paper, a scheme capable of elicitation of acceptable rules for multivariable ...
Fuzzy Logic Based MPPT Controller for a PV System
Directory of Open Access Journals (Sweden)
Carlos Robles Algarín
2017-12-01
Full Text Available The output power of a photovoltaic (PV module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.
Intuitionistic fuzzy-based model for failure detection.
Aikhuele, Daniel O; Turan, Faiz B M
2016-01-01
In identifying to-be-improved product component(s), the customer/user requirements which are mainly considered, and achieved through customer surveys using the quality function deployment (QFD) tool, often fail to guarantee or cover aspects of the product reliability. Even when they do, there are always many misunderstandings. To improve the product reliability and quality during product redesigning phase and to create that novel product(s) for the customers, the failure information of the existing product, and its component(s) should ordinarily be analyzed and converted to appropriate design knowledge for the design engineer. In this paper, a new intuitionistic fuzzy multi-criteria decision-making method has been proposed. The new approach which is based on an intuitionistic fuzzy TOPSIS model uses an exponential-related function for the computation of the separation measures from the intuitionistic fuzzy positive ideal solution (IFPIS) and intuitionistic fuzzy negative ideal solution (IFNIS) of alternatives. The proposed method has been applied to two practical case studies, and the result from the different cases has been compared with some similar computational approaches in the literature.
Information bottleneck based incremental fuzzy clustering for large biomedical data.
Liu, Yongli; Wan, Xing
2016-08-01
Incremental fuzzy clustering combines advantages of fuzzy clustering and incremental clustering, and therefore is important in classifying large biomedical literature. Conventional algorithms, suffering from data sparsity and high-dimensionality, often fail to produce reasonable results and may even assign all the objects to a single cluster. In this paper, we propose two incremental algorithms based on information bottleneck, Single-Pass fuzzy c-means (spFCM-IB) and Online fuzzy c-means (oFCM-IB). These two algorithms modify conventional algorithms by considering different weights for each centroid and object and scoring mutual information loss to measure the distance between centroids and objects. spFCM-IB and oFCM-IB are used to group a collection of biomedical text abstracts from Medline database. Experimental results show that clustering performances of our approaches are better than such prominent counterparts as spFCM, spHFCM, oFCM and oHFCM, in terms of accuracy. Copyright © 2016 Elsevier Inc. All rights reserved.
Cluster forest based fuzzy logic for massive data clustering
Lahmar, Ines; Ben Ayed, Abdelkarim; Ben Halima, Mohamed; Alimi, Adel M.
2017-03-01
This article is focused in developing an improved cluster ensemble method based cluster forests. Cluster forests (CF) is considered as a version of clustering inspired from Random Forests (RF) in the context of clustering for massive data. It aggregates intermediate Fuzzy C-Means (FCM) clustering results via spectral clustering since pseudo-clustering results are presented in the spectral space in order to classify these data sets in the multidimensional data space. One of the main advantages is the use of FCM, which allows building fuzzy membership to all partitions of the datasets due to the fuzzy logic whereas the classical algorithms as K-means permitted to build just hard partitions. In the first place, we ameliorate the CF clustering algorithm with the integration of fuzzy FCM and we compare it with other existing clustering methods. In the second place, we compare K-means and FCM clustering methods with the agglomerative hierarchical clustering (HAC) and other theory presented methods using data benchmarks from UCI repository.
Zhijia Chen; Yuanchang Zhu; Yanqiang Di; Shaochong Feng
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is const...
Fault Diagnosis in Deaerator Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
S Srinivasan
2007-01-01
Full Text Available In this paper a fuzzy logic based fault diagnosis system for a deaerator in a power plant unit is presented. The system parameters are obtained using the linearised state space deaerator model. The fuzzy inference system is created and rule base are evaluated relating the parameters to the type and severity of the faults. These rules are fired for specific changes in system parameters and the faults are diagnosed.
Masudin, I.; Saputro, T. E.
2016-02-01
In today's technology, electronic trading transaction via internet has been utilized properly with rapid growth. This paper intends to evaluate related to B2C e-commerce website in order to find out the one which meets the usability factors better than another. The influential factors to B2C e-commerce website are determined for two big retailer websites. The factors are investigated based on the consideration of several studies and conformed to the website characteristics. The evaluation is conducted by using different methods namely fuzzy AHP and hierarchical fuzzy TOPSIS so that the final evaluation can be compared. Fuzzy triangular number is adopted to deal with imprecise judgment under fuzzy environment.
Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury.
Chiu, Yu-Hsien; Chen, Tien-Wen; Chen, Yenming J; Su, Ching-I; Hwang, Kao-Shing; Ho, Wen-Hsien
2018-01-01
Effective neurological rehabilitation requires long term assessment and treatment. The rapid progress of virtual reality-based assistive technologies and tele-rehabilitation has increased the potential for self-rehabilitation of various neurological injuries under clinical supervision. The objective of this study was to develop a fuzzy inference mechanism for a smart mobile computing system designed to support in-home rehabilitation of patients with neurological injury in the hand by providing an objective means of self-assessment. A commercially available tablet computer equipped with a Bluetooth motion sensor was integrated in a splint to obtain a smart assistive device for collecting hand motion data, including writing performance and the corresponding grasp force. A virtual reality game was also embedded in the smart splint to support hand rehabilitation. Quantitative data obtained during the rehabilitation process were modeled by fuzzy logic. Finally, the improvement in hand function was quantified with a fuzzy rule database of expert opinion and experience. Experiments in chronic stroke patients showed that the proposed system is applicable for supporting in-home hand rehabilitation. The proposed virtual reality system can be customized for specific therapeutic purposes. Commercial development of the system could immediately provide stroke patients with an effective in-home rehabilitation therapy for improving hand problems.
A Position Controller Model on Color-Based Object Tracking using Fuzzy Logic
Cahyo Wibowo, Budi; Much Ibnu Subroto, Imam; Arifin, Bustanul
2017-04-01
Robotics vision is applying technology on the camera to view the environmental conditions as well as the function of the human eye. Colour object tracking system is one application of robotics vision technology with the ability to follow the object being detected. Several methods have been used to generate a good response position control, but most are still using conventional control approach. Fuzzy logic which includes several step of which is to determine the value of crisp input must be fuzzification. The output of fuzzification is forwarded to the process of inference in which there are some fuzzy logic rules. The inference output forwarded to the process of defuzzification to be transformed into outputs (crisp output) to drive the servo motors on the X-axis and Y-axis. Fuzzy logic control is applied to the color-based object tracking system, the system is successful to follow a moving object with average speed of 7.35 cm/s in environments with 117 lux light intensity.
A self-learning rule base for command following in dynamical systems
Tsai, Wei K.; Lee, Hon-Mun; Parlos, Alexander
1992-01-01
In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.
Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei
2017-09-01
This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.
Yen, John; Wang, Haojin; Daugherity, Walter C.
1992-01-01
Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.
Selection of representative embankments based on rough set - fuzzy clustering method
Bin, Ou; Lin, Zhi-xiang; Fu, Shu-yan; Gao, Sheng-song
2018-02-01
The premise condition of comprehensive evaluation of embankment safety is selection of representative unit embankment, on the basis of dividing the unit levee the influencing factors and classification of the unit embankment are drafted.Based on the rough set-fuzzy clustering, the influence factors of the unit embankment are measured by quantitative and qualitative indexes.Construct to fuzzy similarity matrix of standard embankment then calculate fuzzy equivalent matrix of fuzzy similarity matrix by square method. By setting the threshold of the fuzzy equivalence matrix, the unit embankment is clustered, and the representative unit embankment is selected from the classification of the embankment.
Rule-based Information Integration
de Keijzer, Ander; van Keulen, Maurice
2005-01-01
In this report, we show the process of information integration. We specifically discuss the language used for integration. We show that integration consists of two phases, the schema mapping phase and the data integration phase. We formally define transformation rules, conversion, evolution and
International Nuclear Information System (INIS)
Pothiya, Saravuth; Ngamroo, Issarachai
2008-01-01
This paper proposes a new optimal fuzzy logic-based-proportional-integral-derivative (FLPID) controller for load frequency control (LFC) including superconducting magnetic energy storage (SMES) units. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and error method or experiences of designers. To overcome this problem, the multiple tabu search (MTS) algorithm is applied to simultaneously tune PID gains, membership functions and control rules of FLPID controller to minimize frequency deviations of the system against load disturbances. The MTS algorithm introduces additional techniques for improvement of search process such as initialization, adaptive search, multiple searches, crossover and restarting process. Simulation results explicitly show that the performance of the optimum FLPID controller is superior to the conventional PID controller and the non-optimum FLPID controller in terms of the overshoot, settling time and robustness against variations of system parameters
Fusion Segmentation Method Based on Fuzzy Theory for Color Images
Zhao, J.; Huang, G.; Zhang, J.
2017-09-01
The image segmentation method based on two-dimensional histogram segments the image according to the thresholds of the intensity of the target pixel and the average intensity of its neighborhood. This method is essentially a hard-decision method. Due to the uncertainties when labeling the pixels around the threshold, the hard-decision method can easily get the wrong segmentation result. Therefore, a fusion segmentation method based on fuzzy theory is proposed in this paper. We use membership function to model the uncertainties on each color channel of the color image. Then, we segment the color image according to the fuzzy reasoning. The experiment results show that our proposed method can get better segmentation results both on the natural scene images and optical remote sensing images compared with the traditional thresholding method. The fusion method in this paper can provide new ideas for the information extraction of optical remote sensing images and polarization SAR images.
Fuzzy axiomatic design approach based green supplier selection
DEFF Research Database (Denmark)
Kannan, Devika; Govindan, Kannan; Rajendran, Sivakumar
2015-01-01
proposes a multi-criteria decision-making (MCDM) approach called Fuzzy Axiomatic Design (FAD) to select the best green supplier for Singapore-based plastic manufacturing company. At first, the environmental criteria was developed along with the traditional criteria based on the literature review...... and company requirements. Next, the FAD methodology evaluates the requirements of both the manufacturer (design needs) and the supplier (functional needs), and because multiple criteria must be considered, a multi-objective optimization model of a fuzzy nature must be developed. The application...... of the proposed approach in the case company has been illustrated and the result of this study helps firm to establish the systematic approach to select the best green supplier within the set of criteria. When the proposed methodology is applied, it allows not only to select the most appropriate green supplier...
Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model
Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun
2014-01-01
Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586
Fuzzy GML Modeling Based on Vague Soft Sets
Directory of Open Access Journals (Sweden)
Bo Wei
2017-01-01
Full Text Available The Open Geospatial Consortium (OGC Geography Markup Language (GML explicitly represents geographical spatial knowledge in text mode. All kinds of fuzzy problems will inevitably be encountered in spatial knowledge expression. Especially for those expressions in text mode, this fuzziness will be broader. Describing and representing fuzziness in GML seems necessary. Three kinds of fuzziness in GML can be found: element fuzziness, chain fuzziness, and attribute fuzziness. Both element fuzziness and chain fuzziness belong to the reflection of the fuzziness between GML elements and, then, the representation of chain fuzziness can be replaced by the representation of element fuzziness in GML. On the basis of vague soft set theory, two kinds of modeling, vague soft set GML Document Type Definition (DTD modeling and vague soft set GML schema modeling, are proposed for fuzzy modeling in GML DTD and GML schema, respectively. Five elements or pairs, associated with vague soft sets, are introduced. Then, the DTDs and the schemas of the five elements are correspondingly designed and presented according to their different chains and different fuzzy data types. While the introduction of the five elements or pairs is the basis of vague soft set GML modeling, the corresponding DTD and schema modifications are key for implementation of modeling. The establishment of vague soft set GML enables GML to represent fuzziness and solves the problem of lack of fuzzy information expression in GML.
Switch Reluctance Motor Control Based on Fuzzy Logic System
Directory of Open Access Journals (Sweden)
S. V. Aleksandrovsky
2012-01-01
Full Text Available Due to its intrinsic simplicity and reliability, the switched reluctance motor (SRM has now become a promising candidate for variable-speed drive applications as an alternative induction motor in various industrial application. However, the SRM has the disadvantage of nonlinear characteristic and control. It is suggested to use controller based on fuzzy logic system. Design of FLS controller and simulation model presented.
Fuzzy-Based XML Knowledge Retrieval Methods in Edaphology
K. Naresh kumar; Ch. Satyanand Reddy; N.V.E.S. Murthy
2016-01-01
In this paper, we propose a proficient method for knowledge management in Edaphology to assist the edaphologists and those related with agriculture in a big way. The proposed method mainly consists two sections of which the first one is to build the knowledge base using XML and the latter part deals with information retrieval by searching using fuzzy. Initially, the relational database is converted to the XML database. The paper discusses two algorithms, one is...
Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model
Directory of Open Access Journals (Sweden)
Bogdan Gliwa
2011-01-01
Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.
A PSO-based rule extractor for medical diagnosis.
Hsieh, Yi-Zeng; Su, Mu-Chun; Wang, Pa-Chun
2014-06-01
One of the major bottlenecks in applying conventional neural networks to the medical field is that it is very difficult to interpret, in a physically meaningful way, because the learned knowledge is numerically encoded in the trained synaptic weights. In one of our previous works, we proposed a class of Hyper-Rectangular Composite Neural Networks (HRCNNs) of which synaptic weights can be interpreted as a set of crisp If-Then rules; however, a trained HRCNN may result in some ineffective If-Then rules which can only justify very few positive examples (i.e., poor generalization). This motivated us to propose a PSO-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) which applies particle swarm optimization (PSO) to trim the rules generated by a trained HRCNN while the recognition performance will not be degraded or even be improved. The performance of the proposed PFHRCNN is demonstrated on three benchmark medical databases including liver disorders data set, the breast cancer data set and the Parkinson's disease data set. Copyright © 2014 Elsevier Inc. All rights reserved.
Classifiers based on optimal decision rules
Amin, Talha
2013-11-25
Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).
A new validity measure for a correlation-based fuzzy c-means clustering algorithm.
Zhang, Mingrui; Zhang, Wei; Sicotte, Hugues; Yang, Ping
2009-01-01
One of the major challenges in unsupervised clustering is the lack of consistent means for assessing the quality of clusters. In this paper, we evaluate several validity measures in fuzzy clustering and develop a new measure for a fuzzy c-means algorithm which uses a Pearson correlation in its distance metrics. The measure is designed with within-cluster sum of square, and makes use of fuzzy memberships. In comparing to the existing fuzzy partition coefficient and a fuzzy validity index, this new measure performs consistently across six microarray datasets. The newly developed measure could be used to assess the validity of fuzzy clusters produced by a correlation-based fuzzy c-means clustering algorithm.
Multiple Attribute Decision Making Based on Hesitant Fuzzy Einstein Geometric Aggregation Operators
Directory of Open Access Journals (Sweden)
Xiaoqiang Zhou
2014-01-01
Full Text Available We first define an accuracy function of hesitant fuzzy elements (HFEs and develop a new method to compare two HFEs. Then, based on Einstein operators, we give some new operational laws on HFEs and some desirable properties of these operations. We also develop several new hesitant fuzzy aggregation operators, including the hesitant fuzzy Einstein weighted geometric (HFEWGε operator and the hesitant fuzzy Einstein ordered weighted geometric (HFEWGε operator, which are the extensions of the weighted geometric operator and the ordered weighted geometric (OWG operator with hesitant fuzzy information, respectively. Furthermore, we establish the connections between the proposed and the existing hesitant fuzzy aggregation operators and discuss various properties of the proposed operators. Finally, we apply the HFEWGε operator to solve the hesitant fuzzy decision making problems.
Fuzzy Risk Analysis for a Production System Based on the Nagel Point of a Triangle
Directory of Open Access Journals (Sweden)
Handan Akyar
2016-01-01
Full Text Available Ordering and ranking fuzzy numbers and their comparisons play a significant role in decision-making problems such as social and economic systems, forecasting, optimization, and risk analysis problems. In this paper, a new method for ordering triangular fuzzy numbers using the Nagel point of a triangle is presented. With the aid of the proposed method, reasonable properties of ordering fuzzy numbers are verified. Certain comparative examples are given to illustrate the advantages of the new method. Many papers have been devoted to studies on fuzzy ranking methods, but some of these studies have certain shortcomings. The proposed method overcomes the drawbacks of the existing methods in the literature. The suggested method can order triangular fuzzy numbers as well as crisp numbers and fuzzy numbers with the same centroid point. An application to the fuzzy risk analysis problem is given, based on the suggested ordering approach.
evaluation of a multi-variable self-learning fuzzy logic controller
African Journals Online (AJOL)
Dr Obe
2003-03-01
Mar 1, 2003 ... the merger of fuzzy logic and other forms of soft computing (principally Neural. Networks and Genetic ... merger of soft computing technologies, but instead is based on a purely fuzzy logic platform, was .... A scheme capable of automatic elicitation of suitable rules for a multivariable fuzzy logic controller has ...
Fuzzy based finger vein recognition with rotation invariant feature matching
Ezhilmaran, D.; Joseph, Rose Bindu
2017-11-01
Finger vein recognition is a promising biometric with commercial applications which is explored widely in the recent years. In this paper, a finger vein recognition system is proposed using rotation invariant feature descriptors for matching after enhancing the finger vein images with an interval type-2 fuzzy method. SIFT features are extracted and matched using a matching score based on Euclidian distance. Rotation invariance of the proposed method is verified in the experiment and the results are compared with SURF matching and minutiae matching. It is seen that rotation invariance is verified and the poor quality issues are solved efficiently with the designed system of finger vein recognition during the analysis. The experiments underlines the robustness and reliability of the interval type-2 fuzzy enhancement and SIFT feature matching.
Permutation based decision making under fuzzy environment using Tabu search
Directory of Open Access Journals (Sweden)
Mahdi Bashiri
2012-04-01
Full Text Available One of the techniques, which are used for Multiple Criteria Decision Making (MCDM is the permutation. In the classical form of permutation, it is assumed that weights and decision matrix components are crisp. However, when group decision making is under consideration and decision makers could not agree on a crisp value for weights and decision matrix components, fuzzy numbers should be used. In this article, the fuzzy permutation technique for MCDM problems has been explained. The main deficiency of permutation is its big computational time, so a Tabu Search (TS based algorithm has been proposed to reduce the computational time. A numerical example has illustrated the proposed approach clearly. Then, some benchmark instances extracted from literature are solved by proposed TS. The analyses of the results show the proper performance of the proposed method.
Research on laser cladding control system based on fuzzy PID
Zhang, Chuanwei; Yu, Zhengyang
2017-12-01
Laser cladding technology has a high demand for control system, and the domestic laser cladding control system mostly uses the traditional PID control algorithm. Therefore, the laser cladding control system has a lot of room for improvement. This feature is suitable for laser cladding technology, Based on fuzzy PID three closed-loop control system, and compared with the conventional PID; At the same time, the laser cladding experiment and friction and wear experiment were carried out under the premise of ensuring the reasonable control system. Experiments show that compared with the conventional PID algorithm in fuzzy the PID algorithm under the surface of the cladding layer is more smooth, the surface roughness increases, and the wear resistance of the cladding layer is also enhanced.
Intelligent control based on fuzzy logic and neural net theory
Lee, Chuen-Chien
1991-01-01
In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.
Evaluation-Function-based Model-free Adaptive Fuzzy Control
Directory of Open Access Journals (Sweden)
Agus Naba
2016-12-01
Full Text Available Designs of adaptive fuzzy controllers (AFC are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark veriﬁed the proposed scheme’s efficacy.
Garibaldi, Jonathan M; Zhou, Shang-Ming; Wang, Xiao-Ying; John, Robert I; Ellis, Ian O
2012-06-01
It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), pfuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain. Copyright © 2012 Elsevier Inc. All rights reserved.
Zhang, Liyuan; Li, Tao; Xu, Xuanhua
2014-01-01
The aim of this paper is to develop a methodology for intuitionistic trapezoidal fuzzy multiple criteria group decision making problems based on binary relation. Firstly, the similarity measure between two vectors based on binary relation is defined, which can be utilized to aggregate preference information. Some desirable properties of the similarity measure based on fuzzy binary relation are also studied. Then, a methodology for fuzzy multiple criteria group decision making is proposed, in ...
Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.
Zheng, Yu-Jun; Sheng, Wei-Guo; Sun, Xing-Ming; Chen, Sheng-Yong
2017-12-01
Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.
Lu, Weiying; Rankin, J Graham; Bondra, Alexandria; Trader, Carolyn; Heeren, Amanda; Harrington, Peter de B
2012-07-10
The gasoline and kerosene collected from different locations in the United States were identified by gas chromatography/mass spectrometry (GC/MS) followed by chemometric analysis. Classifications based on two-way profiles and target component ratios were compared. The projected difference resolution (PDR) mapping was applied to measure the differences among the ignitable liquid (IL) samples by their GC/MS profiles quantitatively. Fuzzy rule-building expert systems (FuRESs) were applied to classify individual ILs. The FuRES models yielded correct classification rates greater than 90% for discriminating between samples. PDR mapping, a new method for characterizing complex data sets was consistent with the FuRES classification result. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Fuzzy expert systems using CLIPS
Le, Thach C.
1994-01-01
This paper describes a CLIPS-based fuzzy expert system development environment called FCLIPS and illustrates its application to the simulated cart-pole balancing problem. FCLIPS is a straightforward extension of CLIPS without any alteration to the CLIPS internal structures. It makes use of the object-oriented and module features in CLIPS version 6.0 for the implementation of fuzzy logic concepts. Systems of varying degrees of mixed Boolean and fuzzy rules can be implemented in CLIPS. Design and implementation issues of FCLIPS will also be discussed.
DCT-Yager FNN: a novel Yager-based fuzzy neural network with the discrete clustering technique.
Singh, A; Quek, C; Cho, S Y
2008-04-01
Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into uniform fuzzy sets (Gaussian, triangular, or trapezoidal), the clustering may not be exact and some amount of information may be lost. In this paper, two clustering techniques using a Kohonen-like self-organizing neural network architecture, namely, the unsupervised discrete clustering technique (UDCT) and the supervised discrete clustering technique (SDCT), are proposed. The UDCT and SDCT algorithms reduce this data loss by introducing nonuniform, normal fuzzy sets that are not necessarily convex. The training data range is divided into discrete points at equal intervals, and the membership value corresponding to each discrete point is generated. Hence, the fuzzy sets obtained contain pairs of values, each pair corresponding to a discrete point and its membership grade. Thus, it can be argued that fuzzy membership functions generated using this kind of a discrete methodology provide a more accurate representation of the actual input data. This fact has been demonstrated by comparing the membership functions generated by the UDCT and SDCT algorithms against those generated by the MLVQ, FKP, and pseudofuzzy Kohonen partitioning (PFKP) algorithms. In addition to these clustering techniques, a novel pattern classifying network called the Yager fuzzy neural network (FNN) is proposed in this paper. This network corresponds completely to the Yager inference rule and exhibits remarkable generalization abilities. A modified version of the pseudo-outer product (POP)-Yager FNN called the modified Yager FNN is introduced that eliminates the drawbacks of the earlier network and yi- elds
A New Multi-Layered Fuzzy Image Filter for Removing Impulse Noise
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Russel J Stonier
2003-08-01
Full Text Available In this paper we develop a fuzzy image .lter which consists of a multi-layered fuzzy structure based on the weighted fuzzy blend filter for the removal of noise from images heavily corrupted by impulse noise, while preserving the intricate details of the image. The introduction of multi-layered fuzzy systems substantially decreases the number of rules to be learnt. We then show how Evolutionary Algorithms (EAs can be used to effectively learn the fuzzy rules in each knowledge base. Results are presented for impulse noise corruption of the well-known 'Lena' image.
Comparison of Four Weighting Methods in Fuzzy-based Land Suitability to Predict Wheat Yield
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Fatemeh Rahmati
2017-06-01
Full Text Available Introduction: Land suitability evaluation is a process to examine the degree of land fitness for specific utilization and also makes it possible to estimate land productivity potential. In 1976, FAO provided a general framework for land suitability classification. It has not been proposed a specific method to perform this classification in the framework. In later years, a collection of methods was presented based on the FAO framework. In parametric method, different land suitability aspects are defined as completely discrete groups and are separated from each other by distinguished and consistent ranges. Therefore, land units that have moderate suitability can only choose one of the characteristics of predefined classes of land suitability. Fuzzy logic is an extension of Boolean logic by LotfiZadeh in 1965 based on the mathematical theory of fuzzy sets, which is a generalization of the classical set theory. By introducing the notion of degree in the verification of a condition, fuzzy method enables a condition to be in a state other than true or false, as well as provides a very valuable flexibility for reasoning, which makes it possible to take into account inaccuracies and uncertainties. One advantage of fuzzy logic in order to formalize human reasoning is that the rules are set in natural language. In evaluation method based on fuzzy logic, the weights are used for land characteristics. The objective of this study was to compare four methods of weight calculation in the fuzzy logic to predict the yield of wheat in the study area covering 1500 ha in Kian town in Shahrekord (Chahrmahal and Bakhtiari province, Iran. Materials and Methods: In such investigations, climatic factors, and soil physical and chemical characteristics are studied. This investigation involves several studies including a lab study, and qualitative and quantitative land suitability evaluation with fuzzy logic for wheat. Factors affecting the wheat production consist of
Motion Control of the Soccer Robot Based on Fuzzy Logic
Coman, Daniela; Ionescu, Adela
2009-08-01
Robot soccer is a challenging platform for multi-agent research, involving topics such as real-time image processing and control, robot path planning, obstacle avoidance and machine learning. The conventional robot control consists of methods for path generation and path following. When a robot moves away the estimated path, it must return immediately, and while doing so, the obstacle avoidance behavior and the effectiveness of such a path are not guaranteed. So, motion control is a difficult task, especially in real time and high speed control. This paper describes the use of fuzzy logic control for the low level motion of a soccer robot. Firstly, the modelling of the soccer robot is presented. The soccer robot based on MiroSoT Small Size league is a differential-drive mobile robot with non-slipping and pure-rolling. Then, the design of fuzzy controller is describes. Finally, the computer simulations in MATLAB Simulink show that proposed fuzzy logic controller works well.
Fuzzy Logic-Based Aerodynamic Modeling with Continuous Differentiability
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Ray C. Chang
2013-01-01
Full Text Available This paper presents a modeling method based on a fuzzy-logic algorithm to establish aerodynamic models by using the datasets from flight data recorder (FDR. The fuzzy-logic aerodynamic models are utilized to estimate more accurately the nonlinear unsteady aerodynamics for a transport aircraft, including the effects of atmospheric turbulence. The main objective in this paper is to present the model development and the resulting models with continuous differentiability. The uncertainty and correlation of the data points are estimated and improved by monitoring a multivariable correlation coefficient in the modeling process. The latter is increased by applying a least square method to a set of data points to train a set of modeling coefficients. A commercial transport aircraft encountered severe atmospheric turbulence twice at transonic flight in descending phase is the study case in the present paper. The robustness and nonlinear interpolation capability of the fuzzy-logic algorithm are demonstrated in predicting the degradation in performance and stability characteristics of this transport in severe atmospheric turbulence with sudden plunging motion.
Anomaly based Intrusion Detection using Modified Fuzzy Clustering
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B.S. Harish
2017-12-01
Full Text Available This paper presents a network anomaly detection method based on fuzzy clustering. Computer security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. As a result, Intrusion Detection System has become an indispensable component of computer security. The proposed method consists of three steps: Pre-Processing, Feature Selection and Clustering. In pre-processing step, the duplicate samples are eliminated from the sample set. Next, principal component analysis is adopted to select the most discriminative features. In clustering step, the network samples are clustered using Robust Spatial Kernel Fuzzy C-Means (RSKFCM algorithm. RSKFCM is a variant of traditional Fuzzy C-Means which considers the neighbourhood membership information and uses kernel distance metric. To evaluate the proposed method, we conducted experiments on standard dataset and compared the results with state-of-the-art methods. We used cluster validity indices, accuracy and false positive rate as performance metrics. Experimental results inferred that, the proposed method achieves better results compared to other methods.
Directory of Open Access Journals (Sweden)
Yuxian Zhang
2015-01-01
Full Text Available The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
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P. Akhavan
2014-10-01
Full Text Available Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
Akhavan, P.; Karimi, M.; Pahlavani, P.
2014-10-01
Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Development of Fuzzy-Logic-Based Self Tuning PI Controller for Servomotor
Saad, Nordin; Wahyunggoro, Oyas
2010-01-01
This work discusses the modeling of a DC servomotor from gray box identification and performance evaluations of real time experiment using a fuzzy-logic-based self tuning PI controller as compared to fuzzy-logic-based self tuning PID controller, fuzzy logic controller, PID controller and PI controller on the DC servomotor system. Here, the s-model transfer function of a DC servomotor is identified as a third order transfer function without
Exploration of SWRL Rule Bases through Visualization, Paraphrasing, and Categorization of Rules
Hassanpour, Saeed; O'Connor, Martin J.; Das, Amar K.
Rule bases are increasingly being used as repositories of knowledge content on the Semantic Web. As the size and complexity of these rule bases increases, developers and end users need methods of rule abstraction to facilitate rule management. In this paper, we describe a rule abstraction method for Semantic Web Rule Language (SWRL) rules that is based on lexical analysis and a set of heuristics. Our method results in a tree data structure that we exploit in creating techniques to visualize, paraphrase, and categorize SWRL rules. We evaluate our approach by applying it to several biomedical ontologies that contain SWRL rules, and show how the results reveal rule patterns within the rule base. We have implemented our method as a plug-in tool for Protégé-OWL, the most widely used ontology modeling software for the Semantic Web. Our tool can allow users to rapidly explore content and patterns in SWRL rule bases, enabling their acquisition and management.
ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster.
Volna, Eva; Kotyrba, Martin; Habiballa, Hashim
2015-01-01
The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.
Fuzzy Lyapunov Reinforcement Learning for Non Linear Systems.
Kumar, Abhishek; Sharma, Rajneesh
2017-03-01
We propose a fuzzy reinforcement learning (RL) based controller that generates a stable control action by lyapunov constraining fuzzy linguistic rules. In particular, we attempt at lyapunov constraining the consequent part of fuzzy rules in a fuzzy RL setup. Ours is a first attempt at designing a linguistic RL controller with lyapunov constrained fuzzy consequents to progressively learn a stable optimal policy. The proposed controller does not need system model or desired response and can effectively handle disturbances in continuous state-action space problems. Proposed controller has been employed on the benchmark Inverted Pendulum (IP) and Rotational/Translational Proof-Mass Actuator (RTAC) control problems (with and without disturbances). Simulation results and comparison against a) baseline fuzzy Q learning, b) Lyapunov theory based Actor-Critic, and c) Lyapunov theory based Markov game controller, elucidate stability and viability of the proposed control scheme. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Wei Huang
2013-01-01
Full Text Available We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA. The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.
Location Discovery Based on Fuzzy Geometry in Passive Sensor Networks
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Rui Wang
2011-01-01
Full Text Available Location discovery with uncertainty using passive sensor networks in the nation's power grid is known to be challenging, due to the massive scale and inherent complexity. For bearings-only target localization in passive sensor networks, the approach of fuzzy geometry is introduced to investigate the fuzzy measurability for a moving target in R2 space. The fuzzy analytical bias expressions and the geometrical constraints are derived for bearings-only target localization. The interplay between fuzzy geometry of target localization and the fuzzy estimation bias for the case of fuzzy linear observer trajectory is analyzed in detail in sensor networks, which can realize the 3-dimensional localization including fuzzy estimate position and velocity of the target by measuring the fuzzy azimuth angles at intervals of fixed time. Simulation results show that the resulting estimate position outperforms the traditional least squares approach for localization with uncertainty.
Mazzucco, M.M.; Bolzan, A.; Barcia, R.M.; Machado, R.A. F.
2000-01-01
The development of control systems based on fuzzy rules facilitates the solving of problems when insufficient phenomenological information is available. The most common way of grouping fuzzy rules to form a controller is known as Mamdani controller. This controller consists of a set of rules with two premises, the error and the error variation, and one conclusion, the control action variation. One of the most delicate phases of the project of fuzzy systems is the definition of the supports (r...
A computationally efficient fuzzy control s
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Abdel Badie Sharkawy
2013-12-01
Full Text Available This paper develops a decentralized fuzzy control scheme for MIMO nonlinear second order systems with application to robot manipulators via a combination of genetic algorithms (GAs and fuzzy systems. The controller for each degree of freedom (DOF consists of a feedforward fuzzy torque computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line using GAs, whereas not only the parameters but also the structure of the fuzzy system is optimized. The feedback fuzzy PD system, on the other hand, is used to keep the closed-loop stable. The rule base consists of only four rules per each DOF. Furthermore, the fuzzy feedback system is decentralized and simplified leading to a computationally efficient control scheme. The proposed control scheme has the following advantages: (1 it needs no exact dynamics of the system and the computation is time-saving because of the simple structure of the fuzzy systems and (2 the controller is robust against various parameters and payload uncertainties. The computational complexity of the proposed control scheme has been analyzed and compared with previous works. Computer simulations show that this controller is effective in achieving the control goals.
System control fuzzy neural sewage pumping stations using genetic algorithms
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Владлен Николаевич Кузнецов
2015-06-01
Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.
Rule-based decision making model
International Nuclear Information System (INIS)
Sirola, Miki
1998-01-01
A rule-based decision making model is designed in G2 environment. A theoretical and methodological frame for the model is composed and motivated. The rule-based decision making model is based on object-oriented modelling, knowledge engineering and decision theory. The idea of safety objective tree is utilized. Advanced rule-based methodologies are applied. A general decision making model 'decision element' is constructed. The strategy planning of the decision element is based on e.g. value theory and utility theory. A hypothetical process model is built to give input data for the decision element. The basic principle of the object model in decision making is division in tasks. Probability models are used in characterizing component availabilities. Bayes' theorem is used to recalculate the probability figures when new information is got. The model includes simple learning features to save the solution path. A decision analytic interpretation is given to the decision making process. (author)
GPU-based relative fuzzy connectedness image segmentation
Zhuge, Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.
2013-01-01
Purpose: Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ∞-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology. PMID:23298094
GPU-based relative fuzzy connectedness image segmentation
International Nuclear Information System (INIS)
Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.
2013-01-01
Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ ∞ -based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.
Simplified interval type-2 fuzzy neural networks.
Lin, Yang-Yin; Liao, Shih-Hui; Chang, Jyh-Yeong; Lin, Chin-Teng
2014-05-01
This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.
Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
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Vasile MAZILESCU
2010-12-01
Full Text Available A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model when it is compared to the second part (the fuzzy facts for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs. The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler.
FUZZY BASED TRUST MANAGEMENT SYSTEM FOR CLOUD ENVIRONMENT
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Sunil Kumar
2016-06-01
Full Text Available Cloud computing is a business model with high degree of flexibility, scalability in providing infrastructure, platform and software as a service over the internet. Cloud promises for easiness and reduced expense to service providers and consumers. However, a lack of trust between these two stakeholders has hindered the universal acceptance of cloud for outsourced services. In this paper, a fuzzy based trust management system is proposed to facilitate cloud consumers in identifying trustworthy providers. The performance of proposed system is validated through a simulation using CloudAnalyst and Simulink.
Assessing the quality of sardine based on biogenic amines using a fuzzy logic model.
Zare, Davood; Ghazali, H M
2017-04-15
There is an increasing concern about the quality and quality assessment procedures of seafood. In the present study, a model to assess fish quality based on biogenic amine contents using fuzzy logic model (FLM) is proposed. The fish used was sardine (Sardinella sp.) where the production of eight biogenic amines was monitored over fifteen days of storage at 0, 3 and 10°C. Based on the results, histamine, putrescine and cadaverine were selected as input variables and twelve quality grades were considered for quality of fish as output variables for the FLM. Input data were processed by rules established in the model and were then defuzzified according to defined output variables. Finally, the quality of fish was evaluated using the designed model and Pearson correlation between storage times with quality of fish showed r=0.97, 0.95 and 1 for fish stored at 0, 3 and 10°C, respectively. Copyright © 2016. Published by Elsevier Ltd.
A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network
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WenBo Zhu
2017-01-01
Full Text Available This study proposes a method based on Dempster-Shafer theory (DST and fuzzy neural network (FNN to improve the reliability of recognizing fatigue driving. This method measures driving states using multifeature fusion. First, FNN is introduced to obtain the basic probability assignment (BPA of each piece of evidence given the lack of a general solution to the definition of BPA function. Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists. Finally, the recognition result is given according to the combination of revised evidence based on Dempster’s rule. Experiment results demonstrate that the recognition method proposed in this paper can obtain reasonable results with the combination of information given by multiple features. The proposed method can also effectively and accurately describe driving states.
A fuzzy logic based network intrusion detection system for predicting the TCP SYN flooding attack
CSIR Research Space (South Africa)
Mkuzangwe, Nenekazi NP
2017-04-01
Full Text Available presents a fuzzy logic based network intrusion detection system to predict neptune which is a type of a Transmission Control Protocol Synchronized (TCP SYN) flooding attack. The performance of the proposed fuzzy logic based system is compared to that of a...
A Method Based on Intuitionistic Fuzzy Dependent Aggregation Operators for Supplier Selection
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Fen Wang
2013-01-01
Full Text Available Recently, resolving the decision making problem of evaluation and ranking the potential suppliers have become as a key strategic factor for business firms. In this paper, two new intuitionistic fuzzy aggregation operators are developed: dependent intuitionistic fuzzy ordered weighed averaging (DIFOWA operator and dependent intuitionistic fuzzy hybrid weighed aggregation (DIFHWA operator. Some of their main properties are studied. A method based on the DIFHWA operator for intuitionistic fuzzy multiple attribute decision making is presented. Finally, an illustrative example concerning supplier selection is given.
Abou, Seraphin C
2012-03-01
In this paper, a new interpretation of intuitionistic fuzzy sets in the advanced framework of the Dempster-Shafer theory of evidence is extended to monitor safety-critical systems' performance. Not only is the proposed approach more effective, but it also takes into account the fuzzy rules that deal with imperfect knowledge/information and, therefore, is different from the classical Takagi-Sugeno fuzzy system, which assumes that the rule (the knowledge) is perfect. We provide an analytical solution to the practical and important problem of the conceptual probabilistic approach for formal ship safety assessment using the fuzzy set theory that involves uncertainties associated with the reliability input data. Thus, the overall safety of the ship engine is investigated as an object of risk analysis using the fuzzy mapping structure, which considers uncertainty and partial truth in the input-output mapping. The proposed method integrates direct evidence of the frame of discernment and is demonstrated through references to examples where fuzzy set models are informative. These simple applications illustrate how to assess the conflict of sensor information fusion for a sufficient cooling power system of vessels under extreme operation conditions. It was found that propulsion engine safety systems are not only a function of many environmental and operation profiles but are also dynamic and complex. Copyright © 2011 Elsevier Ltd. All rights reserved.
The Design of Artificial Intelligence Robot Based on Fuzzy Logic Controller Algorithm
Zuhrie, M. S.; Munoto; Hariadi, E.; Muslim, S.
2018-04-01
Artificial Intelligence Robot is a wheeled robot driven by a DC motor that moves along the wall using an ultrasonic sensor as a detector of obstacles. This study uses ultrasonic sensors HC-SR04 to measure the distance between the robot with the wall based ultrasonic wave. This robot uses Fuzzy Logic Controller to adjust the speed of DC motor. When the ultrasonic sensor detects a certain distance, sensor data is processed on ATmega8 then the data goes to ATmega16. From ATmega16, sensor data is calculated based on Fuzzy rules to drive DC motor speed. The program used to adjust the speed of a DC motor is CVAVR program (Code Vision AVR). The readable distance of ultrasonic sensor is 3 cm to 250 cm with response time 0.5 s. Testing of robots on walls with a setpoint value of 9 cm to 10 cm produce an average error value of -12% on the wall of L, -8% on T walls, -8% on U wall, and -1% in square wall.
Ling, Steve S H; Nguyen, Hung T
2011-03-01
Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity.
Qualitative assessment of environmental impacts through fuzzy logic
International Nuclear Information System (INIS)
Peche G, Roberto
2008-01-01
The vagueness of many concepts usually utilized in environmental impact studies, along with frequent lack of quantitative information, suggests that fuzzy logic can be applied to carry out qualitative assessment of such impacts. This paper proposes a method for valuing environmental impacts caused by projects, based on fuzzy sets theory and methods of approximate reasoning. First, impacts must be described by a set of features. A linguistic variable is assigned to each feature, whose values are fuzzy sets. A fuzzy evaluation of environmental impacts is achieved using rule based fuzzy inference and the estimated fuzzy value of each feature. Generalized modus ponens has been the inference method. Finally, a crisp value of impact is attained by aggregation and defuzzification of all fuzzy results
Fuzzy logic and its application in football team ranking.
Zeng, Wenyi; Li, Junhong
2014-01-01
Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in physical education for tasks such as the selection for athletes, the evaluation for different training approaches, the team ranking, and the real-time monitoring of sports data. In this paper, we use fuzzy set theory and apply fuzzy clustering analysis in football team ranking. Based on some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T 7, T 3, T 1, T 9, T 10, T 8, T 11, T 12, T 2, T 6, T 5, T 4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range.
FUZZY LOGIC BASED ENERGY EFFICIENT PROTOCOL IN WIRELESS SENSOR NETWORKS
Directory of Open Access Journals (Sweden)
Zhan Wei Siew
2012-12-01
Full Text Available Wireless sensor networks (WSNs have been vastly developed due to the advances in microelectromechanical systems (MEMS using WSN to study and monitor the environments towards climates changes. In environmental monitoring, sensors are randomly deployed over the interest area to periodically sense the physical environments for a few months or even a year. Therefore, to prolong the network lifetime with limited battery capacity becomes a challenging issue. Low energy adaptive cluster hierarchical (LEACH is the common clustering protocol that aim to reduce the energy consumption by rotating the heavy workload cluster heads (CHs. The CHs election in LEACH is based on probability model which will lead to inefficient in energy consumption due to least desired CHs location in the network. In WSNs, the CHs location can directly influence the network energy consumption and further affect the network lifetime. In this paper, factors which will affect the network lifetime will be presented and the demonstration of fuzzy logic based CH selection conducted in base station (BS will also be carried out. To select suitable CHs that will prolong the network first node dies (FND round and consistent throughput to the BS, energy level and distance to the BS are selected as fuzzy inputs.
Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data
Directory of Open Access Journals (Sweden)
Peter Hofmann
2016-06-01
Full Text Available The classes in fuzzy classification schemes are defined as fuzzy sets, partitioning the feature space through fuzzy rules, defined by fuzzy membership functions. Applying fuzzy classification schemes in remote sensing allows each pixel or segment to be an incomplete member of more than one class simultaneously, i.e., one that does not fully meet all of the classification criteria for any one of the classes and is member of more than one class simultaneously. This can lead to fuzzy, ambiguous and uncertain class assignation, which is unacceptable for many applications, indicating the need for a reliable defuzzification method. Defuzzification in remote sensing has to date, been performed by “crisp-assigning” each fuzzy-classified pixel or segment to the class for which it best fulfills the fuzzy classification rules, regardless of its classification fuzziness, uncertainty or ambiguity (maximum method. The defuzzification of an uncertain or ambiguous fuzzy classification leads to a more or less reliable crisp classification. In this paper the most common parameters for expressing classification uncertainty, fuzziness and ambiguity are analysed and discussed in terms of their ability to express the reliability of a crisp classification. This is done by means of a typical practical example from Object Based Image Analysis (OBIA.
DEFF Research Database (Denmark)
Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede
2015-01-01
by employing wavelet transform under different fault conditions. Then the fuzzy logic rules are automatically trained based on the fuzzified fault features to diagnose the different faults. Neither additional sensor nor the capacitor voltages are needed in the proposed method. The high accuracy, good...... for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage...
A Fuzzy Petri Nets System for Heart Disease Diagnosis
Directory of Open Access Journals (Sweden)
Hussin Attya Lafta
2017-02-01
Full Text Available In this paper we have proposed a Fuzzy Petri Nets Expert System for heart disease diagnosis. The aim of the proposed system is simulating experience of experts in Diagnosis Heart Disease stage, based on Fuzzy Rule System and modeling reasoning operation by using Fuzzy Petri Nets. The database taken from Machine Learning Repository Center for machine learning and intelligent system. The system has 11 input fields and one output field. The accuracy of proposed system is 75%.
Interval-Valued Model Level Fuzzy Aggregation-Based Background Subtraction.
Chiranjeevi, Pojala; Sengupta, Somnath
2017-09-01
In a recent work, the effectiveness of neighborhood supported model level fuzzy aggregation was shown under dynamic background conditions. The multi-feature fuzzy aggregation used in that approach uses real fuzzy similarity values, and is robust for low and medium-scale dynamic background conditions such as swaying vegetation, sprinkling water, etc. The technique, however, exhibited some limitations under heavily dynamic background conditions, as features have high uncertainty under such noisy conditions and these uncertainties were not captured by real fuzzy similarity values. Our proposed algorithm is particularly focused toward improving the detection under heavy dynamic background conditions by modeling uncertainties in the data by interval-valued fuzzy set. In this paper, real-valued fuzzy aggregation has been extended to interval-valued fuzzy aggregation by considering uncertainties over real similarity values. We build up a procedure to calculate the uncertainty that varies for each feature, at each pixel, and at each time instant. We adaptively determine membership values at each pixel by the Gaussian of uncertainty value instead of fixed membership values used in recent fuzzy approaches, thereby, giving importance to a feature based on its uncertainty. Interval-valued Choquet integral is evaluated using interval similarity values and the membership values in order to calculate interval-valued fuzzy similarity between model and current. Adequate qualitative and quantitative studies are carried out to illustrate the effectiveness of the proposed method in mitigating heavily dynamic background situations as compared to state-of-the-art.
Wavefront Propagation and Fuzzy Based Autonomous Navigation
Directory of Open Access Journals (Sweden)
Adel Al-Jumaily
2005-06-01
Full Text Available Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments.
Knowledge base rule partitioning design for CLIPS
Mainardi, Joseph D.; Szatkowski, G. P.
1990-01-01
This describes a knowledge base (KB) partitioning approach to solve the problem of real-time performance using the CLIPS AI shell when containing large numbers of rules and facts. This work is funded under the joint USAF/NASA Advanced Launch System (ALS) Program as applied research in expert systems to perform vehicle checkout for real-time controller and diagnostic monitoring tasks. The Expert System advanced development project (ADP-2302) main objective is to provide robust systems responding to new data frames of 0.1 to 1.0 second intervals. The intelligent system control must be performed within the specified real-time window, in order to meet the demands of the given application. Partitioning the KB reduces the complexity of the inferencing Rete net at any given time. This reduced complexity improves performance but without undo impacts during load and unload cycles. The second objective is to produce highly reliable intelligent systems. This requires simple and automated approaches to the KB verification & validation task. Partitioning the KB reduces rule interaction complexity overall. Reduced interaction simplifies the V&V testing necessary by focusing attention only on individual areas of interest. Many systems require a robustness that involves a large number of rules, most of which are mutually exclusive under different phases or conditions. The ideal solution is to control the knowledge base by loading rules that directly apply for that condition, while stripping out all rules and facts that are not used during that cycle. The practical approach is to cluster rules and facts into associated 'blocks'. A simple approach has been designed to control the addition and deletion of 'blocks' of rules and facts, while allowing real-time operations to run freely. Timing tests for real-time performance for specific machines under R/T operating systems have not been completed but are planned as part of the analysis process to validate the design.
Pires, O C; Palma, C; Costa, J C; Moita, I; Alves, M M; Ferreira, E C
2006-01-01
A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a laboratory- scale plant comprising anaerobic digestion and anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 h. Recycle flow rate from the aerobic to the anoxic module and bypass flow rate from the influent directly to the anoxic reactor were the output variables of the fuzzy system. They were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognised by the expert system. Denitrification efficiency higher than 85% was achieved 30 h after the disturbance and 15 h after the system response at an HRT as low as 1.5 h. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 h. The system proved to react properly in order to set adequate operating conditions that led to timely and efficient recovery of N and C removal rates.
Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images.
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Izhar Haq
Full Text Available Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images employs a 3 × 3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG, Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270 × 290 pixels having 24 dB 'salt and pepper' noise, it detected very few (22 false edge pixels, compared to Sobel (1931, Prewitt (2741, LOG (3102, Roberts (1451 and Canny (1045 false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.
Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images.
Haq, Izhar; Anwar, Shahzad; Shah, Kamran; Khan, Muhammad Tahir; Shah, Shaukat Ali
2015-01-01
Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3 × 3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270 × 290 pixels having 24 dB 'salt and pepper' noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.
Wang, Zhengfang; Harrington, Peter de B
2013-11-01
A bootstrapped fuzzy rule-building expert system (FuRES) and a bootstrapped t-statistical weight feature selection method were individually used to select informative features from gas chromatography/mass spectrometry (GC/MS) chemical profiles of basil plants cultivated by organic and conventional farming practices. Feature subsets were selected from two-way GC/MS data objects, total ion chromatograms, and total mass spectra, separately. Four economic classifiers based on the bootstrapped FuRES approach, i.e., fuzzy optimal associative memory (e-FOAM), e-FuRES, partial least-squares-discriminant analysis (e-PLS-DA), and soft independent modeling by class analogy (e-SIMCA), and four economic classifiers based on the bootstrapped t-weight approach, i.e., e-PLS-DA-t, e-FOAM-t, e-FuRES-t, and e-SIMCA-t, were constructed thereafter to be compared with full-size classifiers obtained from the entire GC/MS data objects (i.e., FOAM, FuRES, PLS-DA, and SIMCA). By using three features selected from two-way data objects, the average classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 95.3 ± 0.5%, 100%, 100%, and 91.8 ± 0.2%, respectively. The established economic classifiers were used to classify a new validation set collected 2.5 months later with no parametric change to experimental procedure. Classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 96.7%, 100%, 100%, and 96.7%, respectively. Characteristic components in basil extracts corresponding to highest-ranked useful features were putatively identified. The feature subset may prove valuable as a rapid approach for organic basil authentication.
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
Fuzzy delay model based fault simulator for crosstalk delay fault test ...
Indian Academy of Sciences (India)
In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design trends move towards nanometer technologies, more number of new parameters affects the delay of the component. Fuzzy delay models are ideal for modelling the uncertainty found in the design and manufacturing steps.
Structural modeling and fuzzy-logic based diagnosis of a ship propulsion benchmark
DEFF Research Database (Denmark)
Izadi-Zamanabadi, Roozbeh; Blanke, M.; Katebi, S.D.
2000-01-01
An analysis of structural model of a ship propulsion benchmark leads to identifying the subsystems with inherent redundant information. For a nonlinear part of the system, a Fuzzy logic based FD algorithm with adaptive threshold is employed. The results illustrate the applicability of structural...... analysis as well as fuzzy observer....
Structural modeling and fuzzy-logic based diagnosis of a ship propulsion benchmark
DEFF Research Database (Denmark)
Izadi-Zamanabadi, Roozbeh; Blanke, M.; Katebi, S.D.
2000-01-01
An analysis of structural model of a ship propulsion benchmark leads to identifying the subsystems with inherent redundant information. For a nonlinear part of the system, a Fuzzy logic based FD algorithm with adaptive threshold is employed. The results illustrate the applicability of structural...... analysis as well as fuzzy observer...
Directory of Open Access Journals (Sweden)
Majid Moshtagh Khorasani
2009-04-01
Full Text Available
Control of a mechanical gripper with a fuzzy controller
International Nuclear Information System (INIS)
Alberdi, J.; Barcala, J.M.; Gamero, E.; Navarrete, J.J.
1995-01-01
A fuzzy logic system is used to control a mechanical gripper. System is based in a NLX230 fuzzy micro controller. Control rules are programmed by a 68020 microprocessor in the micro controller memory. Stress and its derived are used as feedback signals in the control. This system can adapt its effort to the mechanical resistance of the object between the fingers. (Author)
Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising.
Yang, Chin-Chang; Guo, Shu-Mei; Tsai, Jason Sheng-Hong
2017-09-01
An evolutionary fuzzy block-matching-based image denoising algorithm is proposed to remove noise from a camera raw image. Recently, a variance stabilization transform is widely used to stabilize the noise variance, so that a Gaussian denoising algorithm can be used to remove the signal-dependent noise in camera sensors. However, in the stabilized domain, the existed denoising algorithm may blur too much detail. To provide a better estimate of the noise-free signal, a new block-matching approach is proposed to find similar blocks by the use of a type-2 fuzzy logic system (FLS). Then, these similar blocks are averaged with the weightings which are determined by the FLS. Finally, an efficient differential evolution is used to further improve the performance of the proposed denoising algorithm. The experimental results show that the proposed denoising algorithm effectively improves the performance of image denoising. Furthermore, the average performance of the proposed method is better than those of two state-of-the-art image denoising algorithms in subjective and objective measures.
A Novel Fuzzy Document Based Information Retrieval Model for Forecasting
Directory of Open Access Journals (Sweden)
Partha Roy
2017-06-01
Full Text Available Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1 In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2 A unique approach is followed while modeling the information retrieval (IR system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index of National Stock Exchange of India (NSE is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.
Fuzzy Fireworks Algorithm Based on a Sparks Dispersion Measure
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Juan Barraza
2017-07-01
Full Text Available The main goal of this paper is to improve the performance of the Fireworks Algorithm (FWA. To improve the performance of the FWA we propose three modifications: the first modification is to change the stopping criteria, this is to say, previously, the number of function evaluations was utilized as a stopping criteria, and we decided to change this to specify a particular number of iterations; the second and third modifications consist on introducing a dispersion metric (dispersion percent, and both modifications were made with the goal of achieving dynamic adaptation of the two parameters in the algorithm. The parameters that were controlled are the explosion amplitude and the number of sparks, and it is worth mentioning that the control of these parameters is based on a fuzzy logic approach. To measure the impact of these modifications, we perform experiments with 14 benchmark functions and a comparative study shows the advantage of the proposed approach. We decided to call the proposed algorithms Iterative Fireworks Algorithm (IFWA and two variants of the Dispersion Percent Iterative Fuzzy Fireworks Algorithm (DPIFWA-I and DPIFWA-II, respectively.
Vadiati, M; Asghari-Moghaddam, A; Nakhaei, M; Adamowski, J; Akbarzadeh, A H
2016-12-15
Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013-2014. Input membership functions were defined as "desirable", "acceptable" and "unacceptable" based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy
Redundant sensor validation by using fuzzy logic
International Nuclear Information System (INIS)
Holbert, K.E.; Heger, A.S.; Alang-Rashid, N.K.
1994-01-01
This research is motivated by the need to relax the strict boundary of numeric-based signal validation. To this end, the use of fuzzy logic for redundant sensor validation is introduced. Since signal validation employs both numbers and qualitative statements, fuzzy logic provides a pathway for transforming human abstractions into the numerical domain and thus coupling both sources of information. With this transformation, linguistically expressed analysis principles can be coded into a classification rule-base for signal failure detection and identification
WALL-FOLLOWING BEHAVIOR-BASED MOBILE ROBOT USING PARTICLE SWARM FUZZY CONTROLLER
Directory of Open Access Journals (Sweden)
Andi Adriansyah
2016-02-01
Full Text Available Behavior-based control architecture has been broadly recognized due to their compentence in mobile robot development. Fuzzy logic system characteristics are appropriate to address the behavior design problems. Nevertheless, there are problems encountered when setting fuzzy variables manually. Consequently, most of the efforts in the field, produce certain works for the study of fuzzy systems with added learning abilities. This paper presents the improvement of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO. A wall-following behaviors used on Particle Swarm Fuzzy Controller (PSFC are developed using the modified PSO with two stages of the PSFC process. Several simulations have been accomplished to analyze the algorithm. The promising performance have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.
Adaptive Control of MEMS Gyroscope Based on T-S Fuzzy Model
Directory of Open Access Journals (Sweden)
Yunmei Fang
2015-01-01
Full Text Available A multi-input multioutput (MIMO Takagi-Sugeno (T-S fuzzy model is built on the basis of a nonlinear model of MEMS gyroscope. A reference model is adjusted so that a local linear state feedback controller could be designed for each T-S fuzzy submodel based on a parallel distributed compensation (PDC method. A parameter estimation scheme for updating the parameters of the T-S fuzzy models is designed and analyzed based on the Lyapunov theory. A new adaptive law can be selected to be the former adaptive law plus a nonnegative in variable to guarantee that the derivative of the Lyapunov function is smaller than zero. The controller output is implemented on the nonlinear model and T-S fuzzy model, respectively, for the purpose of comparison. Numerical simulations are investigated to verify the effectiveness of the proposed control scheme and the correctness of the T-S fuzzy model.
Directory of Open Access Journals (Sweden)
Xiao Luo
2017-01-01
Full Text Available An intuitionistic fuzzy VIKOR (IF-VIKOR method is proposed based on a new distance measure considering the waver of intuitionistic fuzzy information. The method aggregates all individual decision-makers’ assessment information based on intuitionistic fuzzy weighted averaging operator (IFWA, determines the weights of decision-makers and attributes objectively using intuitionistic fuzzy entropy, calculates the group utility and individual regret by the new distance measure, and then reaches a compromise solution. It can be effectively applied to multiattribute decision-making (MADM problems where the weights of decision-makers and attributes are completely unknown and the attribute values are intuitionistic fuzzy numbers (IFNs. The validity and stability of this method are verified by example analysis and sensitivity analysis, and its superiority is illustrated by the comparison with the existing method.
Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm
Directory of Open Access Journals (Sweden)
Baljit Singh Khehra
2015-03-01
Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.
Jung, Hye-Young; Leem, Sangseob; Lee, Sungyoung; Park, Taesung
2016-12-01
Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. We propose a novel Fuzzy MDR method to detect gene-gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Cao, Yuzhen; Cai, Lihui; Wang, Jiang; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing
2015-08-01
In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.
FUZZY LOGIC BASED TEMPERATURE CONTROL SYSTEM USING A MICROCONTROLLER
FİDAN, Uğur; BAY, Ö.FARUK
2002-01-01
This paper is aimed to illustrate the design and the implementation of a fuzzy logic controller(FLC) for an incubator using an AT89C205 microcontroller. The basis for fuzzy control and the general structure of the fuzzy logic controllers are illustrated. Then design and implementation steps of the FLC are explained. Experimental results are also included. The incubator temperature can be adjusted at any point between 25oC – 40 oC . The use of fuzzy logic controller in this application has pot...
Portfolio Selection Based on Distance between Fuzzy Variables
Directory of Open Access Journals (Sweden)
Weiyi Qian
2014-01-01
Full Text Available This paper researches portfolio selection problem in fuzzy environment. We introduce a new simple method in which the distance between fuzzy variables is used to measure the divergence of fuzzy investment return from a prior one. Firstly, two new mathematical models are proposed by expressing divergence as distance, investment return as expected value, and risk as variance and semivariance, respectively. Secondly, the crisp forms of the new models are also provided for different types of fuzzy variables. Finally, several numerical examples are given to illustrate the effectiveness of the proposed approach.
Moini, A
2002-01-01
In this paper, genetic algorithms are used in the design and robustification various mo el-ba ed/non-model-based fuzzy-logic controllers for robotic manipulators. It is demonstrated that genetic algorithms provide effective means of designing the optimal set of fuzzy rules as well as the optimal domains of associated fuzzy sets in a new class of model-based-fuzzy-logic controllers. Furthermore, it is shown that genetic algorithms are very effective in the optimal design and robustification of non-model-based multivariable fuzzy-logic controllers for robotic manipulators.
Directory of Open Access Journals (Sweden)
Safaa M. Z. Al-Ubaidi
2012-06-01
Full Text Available This paper presents an implementation of conventional PID (CPID controller using Ziegler-Nichols rules and fuzzy PD (FPD controller for position servo motor control based on Lab View (Laboratory Virtual Instrument Engineering Workbench Environment through Data Acquisition (DAQ Device PCI- 6521 of National Instrument's and Data Acquisition Accessory Board Model (CB-68LP.CPID controller is perhaps the most well-known and most widely used in industrial applications. However, it has been known that CPID controller generally don’t work well for non-linear systems, higher order and time-delayed linear system and particularly complex and vague system. To overcome these difficulties, this paper proposes to use the FPD controller for a servo motor system instead of CPID. The parameters of servo motor used are completely unknown. The FPD structure has two-input single-output and fairly similar characteristic to its conventional counterpart and provides good performance. Simple rules base are used for FPD (nine rules only. Performance evaluation was carried out via a comparison study for the proposed control scheme and other existing control scheme, such as CPID controller. The critical point for this experiment on position system is a steady state error and settling time. The performance showing that the FPD has less settling time and zero steady state error over its CPID. The algorithms of FPD and CPID controllers are implemented using PID, Fuzzy Logic and simulation toolkits of the Lab View environment.
Energy Technology Data Exchange (ETDEWEB)
Li, Kangji [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China); School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013 (China); Su, Hongye [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China)
2010-11-15
There are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel's daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy. (author)
Fuzzy-Set Based Sentiment Analysis of Big Social Data
DEFF Research Database (Denmark)
Mukkamala, Raghava Rao; Hussain, Abid; Vatrapu, Ravi
2014-01-01
Computational approaches to social media analytics are largely limited to graph theoretical approaches such as social network analysis (SNA) informed by the social philosophical approach of relational sociology. There are no other unified modelling approaches to social data that integrate...... the conceptual, formal, software, analytical and empirical realms. In this paper, we first present and discuss a theory and conceptual model of social data. Second, we outline a formal model based on fuzzy set theory and describe the operational semantics of the formal model with a real-world social data example...... from Facebook. Third, we briefly present and discuss the Social Data Analytics Tool (SODATO) that realizes the conceptual model in software and provisions social data analysis based on the conceptual and formal models. Fourth, we use SODATO to fetch social data from the facebook wall of a global brand...
Fuzzy Activity Based Life Cycle Costing For Repairable Equipment
Directory of Open Access Journals (Sweden)
Mulubrhan Freselam
2016-01-01
Full Text Available Life-cycle cost (LCC is the much known method used for decision making that considers all costs in the life of a system or equipment. Predicting LCCs is fraught with potential errors, owing to the uncertainty in future events, future costs, interest rates, and even hidden costs. These uncertainties have a direct impact on the decision making. Activity based LCC is used to identify the activities and cost drivers in acquisition, operation and maintenance phase. This activity based LCC is integrated with fuzzy set theory and interval mathematics to model these uncertainties. Day–Stout–Warren (DSW algorithm and the vertex method are then used to evaluate competing alternatives. A case of two pumps (Pump A and Pump B are taken and their LCC is analysed using the developed model. The equivalent annual cost of Pump B is greater than Pump A, which leads the decision maker to choose Pump A over Pump B.
Pota, Marco; Scalco, Elisa; Sanguineti, Giuseppe; Farneti, Alessia; Cattaneo, Giovanni Mauro; Rizzo, Giovanna; Esposito, Massimo
2017-09-01
Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers. In previous works, clinical, dosimetric and image-based features were considered separately, to find different possible predictors of parotid shrinkage. On the other hand, a few works reported possible image-based predictors of xerostomia, while the combination of different types of features has been little addressed. This paper proposes the application of a novel machine learning approach, based on both statistics and fuzzy logic, aimed at the classification of patients at risk of i) parotid gland shrinkage and ii) 12-months xerostomia. Both problems are addressed with the aim of individuating predictors and models to classify respective outcomes. Knowledge is extracted from a real dataset of radiotherapy patients, by means of a recently developed method named Likelihood-Fuzzy Analysis, based on the representation of statistical information by fuzzy rule-based models. This method enables to manage heterogeneous variables and missing data, and to obtain interpretable fuzzy models presenting good generalization power (thus high performance), and to measure classification confidence. Numerous features are extracted to characterize patients, coming from different sources, i.e. clinical features, dosimetric parameters, and radiomics-based measures obtained by texture analysis of Computed Tomography images. A learning approach based on the composition of simple models in a more complicated one
International Nuclear Information System (INIS)
Turkdogan-Aydinol, F. Ilter; Yetilmezsoy, Kaan
2010-01-01
A MIMO (multiple inputs and multiple outputs) fuzzy-logic-based model was developed to predict biogas and methane production rates in a pilot-scale 90-L mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Five input variables such as volumetric organic loading rate (OLR), volumetric total chemical oxygen demand (TCOD) removal rate (R V ), influent alkalinity, influent pH and effluent pH were fuzzified by the use of an artificial intelligence-based approach. Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 134 rules in the IF-THEN format. The product (prod) and the centre of gravity (COG, centroid) methods were employed as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two exponential non-linear regression models derived in this study. The UASB reactor showed a remarkable performance on the treatment of molasses wastewater, with an average TCOD removal efficiency of 93 (±3)% and an average volumetric TCOD removal rate of 6.87 (±3.93) kg TCOD removed /m 3 -day, respectively. Findings of this study clearly indicated that, compared to non-linear regression models, the proposed MIMO fuzzy-logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting of both biogas and methane production rates with satisfactory determination coefficients over 0.98.
Adaptive multimodal vibration suppression using fuzzy-based control with limited structural data
International Nuclear Information System (INIS)
Makihara, Kanjuro; Kuroishi, Chikako; Fukunaga, Hisao
2013-01-01
We propose a novel fuzzy-based method of adaptive multimodal vibration suppression with limited structural data. The adaptive control consists of fuzzy inference and a semi-active switching approach. We demonstrate it to be applicable to multimodal vibration suppression for vibrating structures, where a single piezoelectric actuator suppresses two modal vibrations simultaneously. Our fuzzy-based semi-active control requires only the structural information of natural frequencies for real-time adaptive feedback, whereas common adaptive controls require highly precise structural models or complete equations of motion. We conduct experiments in semi-active vibration suppression using the proposed fuzzy-based control, and compare the suppression performance of our fuzzy-based approach with conventional controls. The experiments indicate that the proposed fuzzy-based control demonstrates good adaptability when experiencing sudden changes in disturbance excitation, and also demonstrates high suppression performance. The fuzzy-based control can adapt to a wide range of disturbance conditions, both within and outside the range of vibration excitations assumed when the controller is designed. (paper)
The first order fuzzy predicate logic (I)
International Nuclear Information System (INIS)
Sheng, Y.M.
1986-01-01
Some analysis tools of fuzzy measures, Sugeno's integrals, etc. are introduced into the semantic of the first order predicate logic to explain the concept of fuzzy quantifiers. The truth value of a fuzzy quantification proposition is represented by Sugeno's integral. With this framework, several important notions of formation rules, fuzzy valutions and fuzzy validity are discussed
Zeghlache, Samir; Benslimane, Tarak; Bouguerra, Abderrahmen
2017-11-01
In this paper, a robust controller for a three degree of freedom (3 DOF) helicopter control is proposed in presence of actuator and sensor faults. For this purpose, Interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) technique are used to design a controller, named active fault tolerant interval type-2 Fuzzy Sliding mode controller (AFTIT2FSMC) based on non-linear adaptive observer to estimate and detect the system faults for each subsystem of the 3-DOF helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller. Exponential stability of the closed loop is guaranteed by using the Lyapunov method. The simulation results show that the AFTIT2FSMC can greatly alleviate the chattering effect, providing good tracking performance, even in presence of actuator and sensor faults. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Method for automatic control rod operation using rule-based control
International Nuclear Information System (INIS)
Kinoshita, Mitsuo; Yamada, Naoyuki; Kiguchi, Takashi
1988-01-01
An automatic control rod operation method using rule-based control is proposed. Its features are as follows: (1) a production system to recognize plant events, determine control actions and realize fast inference (fast selection of a suitable production rule), (2) use of the fuzzy control technique to determine quantitative control variables. The method's performance was evaluated by simulation tests on automatic control rod operation at a BWR plant start-up. The results were as follows; (1) The performance which is related to stabilization of controlled variables and time required for reactor start-up, was superior to that of other methods such as PID control and program control methods, (2) the process time to select and interpret the suitable production rule, which was the same as required for event recognition or determination of control action, was short (below 1 s) enough for real time control. The results showed that the method is effective for automatic control rod operation. (author)
Evaluation about the performance of E-government based on interval-valued intuitionistic fuzzy set.
Zhang, Shuai; Yu, Dejian; Wang, Yan; Zhang, Wenyu
2014-01-01
The evaluation is an important approach to promote the development of the E-Government. Since the rapid development of E-Government in the world, the E-Government performance evaluation has become a hot issue in the academia. In this paper, we develop a new evaluation method for the development of the E-Government based on the interval-valued intuitionistic fuzzy set which is a powerful technique in expressing the uncertainty of the real situation. First, we extend the geometric Heronian mean (GHM) operator to interval-valued intuitionistic fuzzy environment and proposed the interval-valued intuitionistic fuzzy GHM (IIFGHM) operator. Then, we investigate the relationships between the IIFGHM operator and some existing ones, such as generalized interval-valued intuitionistic fuzzy HM (GIIFHM) and interval-valued intuitionistic fuzzy weighted Bonferoni mean operator. Furthermore, we validate the effectiveness of the proposed method using a real case about the E-Government evaluation in Hangzhou City, China.
El Harchaoui, Nour-Eddine; Ait Kerroum, Mounir; Hammouch, Ahmed; Ouadou, Mohamed; Aboutajdine, Driss
2013-01-01
The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM) to initialize the parameters of possibilistic c-means (PCM), in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.
Directory of Open Access Journals (Sweden)
Hideki Katagiri
2017-10-01
Full Text Available This paper considers linear programming problems (LPPs where the objective functions involve discrete fuzzy random variables (fuzzy set-valued discrete random variables. New decision making models, which are useful in fuzzy stochastic environments, are proposed based on both possibility theory and probability theory. In multi-objective cases, Pareto optimal solutions of the proposed models are newly defined. Computational algorithms for obtaining the Pareto optimal solutions of the proposed models are provided. It is shown that problems involving discrete fuzzy random variables can be transformed into deterministic nonlinear mathematical programming problems which can be solved through a conventional mathematical programming solver under practically reasonable assumptions. A numerical example of agriculture production problems is given to demonstrate the applicability of the proposed models to real-world problems in fuzzy stochastic environments.
Directory of Open Access Journals (Sweden)
Nour-Eddine El Harchaoui
2013-01-01
Full Text Available The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM to initialize the parameters of possibilistic c-means (PCM, in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.
Multiple Fuzzy Classification Systems
Scherer, Rafał
2012-01-01
Fuzzy classiﬁers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientiﬁc and business applications. Fuzzy classiﬁers use fuzzy rules and do not require assumptions common to statistical classiﬁcation. Rough set theory is useful when data sets are incomplete. It deﬁnes a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classiﬁcation. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a ﬁnite set of learning models, usually weak learners. The present book discusses the three aforementioned ﬁelds – fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed o...
FUZZY-GENETIC CONTROL OF QUADROTOR UNMANNED AERIAL VEHICLES
Directory of Open Access Journals (Sweden)
Attila Nemes
2016-03-01
Full Text Available This article presents a novel fuzzy identification method for dynamic modelling of quadrotor unmanned aerial vehicles. The method is based on a special parameterization of the antecedent part of fuzzy systems that results in fuzzy-partitions for antecedents. This antecedent parameter representation method of fuzzy rules ensures upholding of predefined linguistic value ordering and ensures that fuzzy-partitions remain intact throughout an unconstrained hybrid evolutionary and gradient descent based optimization process. In the equations of motion the first order derivative component is calculated based on Christoffel symbols, the derivatives of fuzzy systems are used for modelling the Coriolis effects, gyroscopic and centrifugal terms. The non-linear parameters are subjected to an initial global evolutionary optimization scheme and fine tuning with gradient descent based local search. Simulation results of the proposed new quadrotor dynamic model identification method are promising.
Fuzzy Logic Based The Application of Multi-Microcontroller in Mobile Robot Model
Directory of Open Access Journals (Sweden)
Nuryono Satya Widodo
2009-12-01
Full Text Available This paper proposed a fuzzy logic based mobile robot as implemented in a multimicrocontroller system. Fuzzy logic controller was developed based on a behavior based approach. The Controller inputs were obtained from seven sonar sensor and three tactile switches. Behavior based approach was implemented in different level priority of behaviors. The behaviors were: obstacle avoidance, wall following and escaping as the emergency behavior. The results show that robot was able to navigate autonomously and avoid the entire obstacle.
Selection of optimal variant route based on dynamic fuzzy GRA
Directory of Open Access Journals (Sweden)
Jalil Heidary Dahooie
2018-09-01
Full Text Available Given the high costs of construction and maintenance, an optimum design methodology is one of the most important steps towards the development of transportation infrastructure, especially freeways. However, the effects of different variables on the decision-making process to find an optimal variant have caused the choice to become a very difficult and professional task for decision makers. So, the current paper aims to determine the optimal variant route for Isfahan-Shiraz freeway through MADM approaches. First, evaluation indices for an optimal route variant are derived through literature review and expert panel assessment. Then, a dynamic fuzzy GRA method is used for weightings and optimal route selection. Bases on the results, the road longevity, views of NGOs and route integration are identified as the highest-weighted criteria in route variant prioritization. Further, Route 3 is defined as the priority for the optimal variant for Isfahan–Shiraz freeway, which is the main basis in practice.
Clinical effect of fuzzy numbers based on center of gravity
African Journals Online (AJOL)
Jane
2011-10-05
Oct 5, 2011 ... Key words: Fuzzy number, defuzzification, centroid, clinical research. INTRODUCTION. Since Jain, Dubis and Prade (Dubios et al., 1987) introduced the relevant concepts of fuzzy numbers, in classical set theory, an element either belongs to a set or it does not. In other words, the status of the element.
Robust chaos synchronization based on adaptive fuzzy delayed ...
Indian Academy of Sciences (India)
In this paper, we propose a new adaptive H∞ synchronization strategy, called an adap- tive fuzzy delayed ... Sugeno (T–S) fuzzy model and adaptive delayed feedback H∞ control scheme, the AFDFHS controller is presented such ..... ciently by using the recently developed convex optimization algorithms [22]. In this paper,.
Fuzzy logic system for BBT based fertility prediction | Yazed | Journal ...
African Journals Online (AJOL)
... been obtained with the accuracy of 95 % and 80 respectively. Besides, this prediction system using fuzzy logic could improve the current practice in the FAM technique by integrating it with an Internet of Things (IoT) technology for automatic BBT charting and monitoring. Keywords: family planning; fertility; BBT; fuzzy logic.
International Nuclear Information System (INIS)
Schildt, G.H.
1996-01-01
After an introduction into safety terms a fuzzy controller for safety related process control will be presented, especially for applications in the field of NPPs. One can show that the size of necessary rules is relatively small. Thus, there exists a real chance for verification and validation of software due to the fact that the whole software can be structured into standard fuzzy software (like fuzzyfication, inference algorithms, and defuzzyfication), real-time operating system software, and the contents of the rule base. Furthermore, there is an excellent advantage due to real-time behaviour, because program execution time can be much more planned than for conventional PID-controller software. Additionally, up to now special know-how does exist to prove stability of fuzzy controller. Hardware design has been done due to fundamental principles of safety technique like watch dog function, dynamization principle, and quiescent current principle