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Sample records for fuzzy rule-based probability

  1. Rule based fuzzy logic approach for classification of fibromyalgia syndrome.

    PubMed

    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

  2. Modified risk graph method using fuzzy rule-based approach.

    PubMed

    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).

  3. Fuzzy-rule-based image reconstruction for positron emission tomography

    NASA Astrophysics Data System (ADS)

    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.

  4. Classification of Contaminated Sites Using a Fuzzy Rule Based System

    SciTech Connect

    Lemos, F.L. de; Van Velzen, K.; Ross, T.

    2006-07-01

    This paper presents the general framework of a multi level model to manage contaminated sites that is being developed. A rule based system along with a scoring system for ranking sites for phase 1 ESA is being proposed (Level 1). Level 2, which consists of the recommendation of the consultant based on their phase 1 ESA is reasonably straightforward. Level 3 which consists of classifying sites which already had a phase 2 ESA conducted on them will involve a multi-objective decision making tool. Fuzzy set theory, which includes the concept of membership functions, was adjudged as the best way to deal with uncertain and non-random information. (authors)

  5. Assessing flood vulnerability using a rule-based fuzzy system.

    PubMed

    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.

  6. Fuzzy-Rule-Based Object Identification Methodology for NAVI System

    NASA Astrophysics Data System (ADS)

    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.

  7. Applications of fuzzy sets to rule-based expert system development

    NASA Technical Reports Server (NTRS)

    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 indecision 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.

  8. Applications of fuzzy sets to rule-based expert system development

    NASA Technical Reports Server (NTRS)

    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.

  9. On Decision-Making Among Multiple Rule-Bases in Fuzzy Control Systems

    NASA Technical Reports Server (NTRS)

    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.

  10. Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG.

    PubMed

    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.

  11. Fuzzy rule-based models for decision support in ecosystem management.

    PubMed

    Adriaenssens, Veronique; De Baets, Bernard; Goethals, Peter L M; De Pauw, Niels

    2004-02-05

    To facilitate decision support in the ecosystem management, ecological expertise and site-specific data need to be integrated. Fuzzy logic can deal with highly variable, linguistic, vague and uncertain data or knowledge and, therefore, has the ability to allow for a logical, reliable and transparent information stream from data collection down to data usage in decision-making. Several environmental applications already implicate the use of fuzzy logic. Most of these applications have been set up by trial and error and are mainly limited to the domain of environmental assessment. In this article, applications of fuzzy logic for decision support in ecosystem management are reviewed and assessed, with an emphasis on rule-based models. In particular, the identification, optimisation, validation, the interpretability and uncertainty aspects of fuzzy rule-based models for decision support in ecosystem management are discussed.

  12. Inference in fuzzy rule bases with conflicting evidence

    NASA Technical Reports Server (NTRS)

    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.

  13. A fuzzy rule base system for object-based feature extraction and classification

    NASA Astrophysics Data System (ADS)

    Jin, Xiaoying; Paswaters, Scott

    2007-04-01

    In this paper, we present a fuzzy rule base system for object-based feature extraction and classification on remote sensing imagery. First, the object primitives are generated from the segmentation step. Object primitives are defined as individual regions with a set of attributes computed on the regions. The attributes computed include spectral, texture and shape measurements. Crisp rules are very intuitive to the users. They are usually represented as "GT (greater than)", "LT (less than)" and "IB (In Between)" with numerical values. The features can be manually generated by querying on the attributes using these crisp rules and monitoring the resulting selected object primitives. However, the attributes of different features are usually overlapping. The information is inexact and not suitable for traditional digital on/off decisions. Here a fuzzy rule base system is built to better model the uncertainty inherent in the data and vague human knowledge. Rather than representing attributes in linguistic terms like "Small", "Medium", "Large", we proposed a new method for automatic fuzzification of the traditional crisp concepts "GT", "LT" and "IB". Two sets of membership functions are defined to model those concepts. One is based on the piecewise linear functions, the other is based on S-type membership functions. A novel concept "fuzzy tolerance" is proposed to control the degree of fuzziness of each rule. The experimental results on classification and extracting features such as water, buildings, trees, fields and urban areas have shown that this newly designed fuzzy rule base system is intuitive and allows users to easily generate fuzzy rules.

  14. Objective Probability and Quantum Fuzziness

    NASA Astrophysics Data System (ADS)

    Mohrhoff, U.

    2009-02-01

    This paper offers a critique of the Bayesian interpretation of quantum mechanics with particular focus on a paper by Caves, Fuchs, and Schack containing a critique of the “objective preparations view” or OPV. It also aims to carry the discussion beyond the hardened positions of Bayesians and proponents of the OPV. Several claims made by Caves et al. are rebutted, including the claim that different pure states may legitimately be assigned to the same system at the same time, and the claim that the quantum nature of a preparation device cannot legitimately be ignored. Both Bayesians and proponents of the OPV regard the time dependence of a quantum state as the continuous dependence on time of an evolving state of some kind. This leads to a false dilemma: quantum states are either objective states of nature or subjective states of belief. In reality they are neither. The present paper views the aforesaid dependence as a dependence on the time of the measurement to whose possible outcomes the quantum state serves to assign probabilities. This makes it possible to recognize the full implications of the only testable feature of the theory, viz., the probabilities it assigns to measurement outcomes. Most important among these are the objective fuzziness of all relative positions and momenta and the consequent incomplete spatiotemporal differentiation of the physical world. The latter makes it possible to draw a clear distinction between the macroscopic and the microscopic. This in turn makes it possible to understand the special status of measurements in all standard formulations of the theory. Whereas Bayesians have written contemptuously about the “folly” of conjoining “objective” to “probability,” there are various reasons why quantum-mechanical probabilities can be considered objective, not least the fact that they are needed to quantify an objective fuzziness. But this cannot be appreciated without giving thought to the makeup of the world, which

  15. A fuzzy rule based metamodel for monthly catchment nitrate fate simulations

    NASA Astrophysics Data System (ADS)

    van der Heijden, S.; Haberlandt, U.

    2015-12-01

    The high complexity of nitrate dynamics and corresponding deterministic models make it very appealing to employ easy, fast, and parsimonious modelling alternatives for decision support. This study presents a fuzzy rule based metamodel consisting of eight fuzzy modules, which is able to simulate nitrate fluxes in large watersheds from their diffuse sources via surface runoff, interflow, and base flow to the catchment outlet. The fuzzy rules are trained on a database established with a calibrated SWAT model for an investigation area of 1000 km2. The metamodel performs well on this training area and on two out of three validation areas in different landscapes, with a Nash-Sutcliffe coefficient of around 0.5-0.7 for the monthly nitrate calculations. The fuzzy model proves to be fast, requires only few readily available input data, and the rule based model structure facilitates a common-sense interpretation of the model, which deems the presented approach suitable for the development of decision support tools.

  16. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    NASA Astrophysics Data System (ADS)

    Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

    2010-09-01

    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 counterpropagatiom fuzzy neural network (CFNN) 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.

  17. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    NASA Astrophysics Data System (ADS)

    Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

    2011-01-01

    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.

  18. Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

    PubMed

    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.

  19. A Fuzzy Rule-Base Model for Classification of Spirometric FVC Graphs in Chronical Obstructive Pulmonary Diseases

    DTIC Science & Technology

    2007-11-02

    of distinguishing COPD group diseases (chronic bronchitis, emphysema and asthma ) by using fuzzy theory and to put into practice a “fuzzy rule-base...FVC Plots”. Keywords - asthma , chronic bronchitis, COPD (Chronic Obstructive Pulmonary Disease), emphysema , expert systems, FVC (forced vital...the group of chronic bronchitis, emphysema and asthma because of these reasons [4-7]. Additionally, similar symptoms may cause fuzziness in

  20. Fuzzy-rule based metamodeling of nitrate transport in large catchments

    NASA Astrophysics Data System (ADS)

    van der Heijden, S.; Haberlandt, U.

    2012-04-01

    Especially for nutrient balance simulations, physically based ecohydrological modeling needs an abundance of measured data and model parameters, which for large catchments all too often are not available in sufficient spatial or temporal resolution or are simply unknown. For efficient large-scale studies it is thus beneficial to have methods at one's disposal which are parsimonious concerning the number of model parameters and the necessary input data. One such method is fuzzy-rule based modeling, which compared to other machine-learning techniques has the advantages to produce models (the fuzzy-rules) which are physically interpretable to a certain extent, and to allow the explicit introduction of expert knowledge through pre-defined rules. The study focuses on the application of fuzzy-rule based modeling for nitrate transport simulation in large catchments, in particular concerning decision support. To be able to construct such models it is possible to take a well-calibrated physically based model to produce data. This metamodeling approach replaces missing observed data. Thus, in a first step the ecohydrological model SWAT was calibrated for a 1000 km2 study area in Northern Germany and used to produce the needed data for rule training. Taking into account the different pathways of nitrate emission from soils (surface runoff, interflow, leaching to groundwater), a modular setup was chosen for the fuzzy model. Two modules were created for each pathway, one for the calculation of fertilized soils and one for non-fertilized soils. Adding one module for groundwater and one for river runoff yields a model consisting of eight modules in total. After selection of appropriate input variables (seven to nine variables for each module) the modules were trained using the SWAT data and simulated annealing as a discrete optimization method. Although flow components are of major importance when describing nitrate transport, they also imply a dependence on (deterministic) water

  1. Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.

    PubMed

    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.

  2. Fuzzy rule-based image segmentation in dynamic MR images of the liver

    NASA Astrophysics Data System (ADS)

    Kobashi, Syoji; Hata, Yutaka; Tokimoto, Yasuhiro; Ishikawa, Makato

    2000-06-01

    This paper presents a fuzzy rule-based region growing method for segmenting two-dimensional (2-D) and three-dimensional (3- D) magnetic resonance (MR) images. The method is an extension of the conventional region growing method. The proposed method evaluates the growing criteria by using fuzzy inference techniques. The use of the fuzzy if-then rules is appropriate for describing the knowledge of the legions on the MR images. To evaluate the performance of the proposed method, it was applied to artificially generated images. In comparison with the conventional method, the proposed method shows high robustness for noisy images. The method then applied for segmenting the dynamic MR images of the liver. The dynamic MR imaging has been used for diagnosis of hepatocellular carcinoma (HCC), portal hypertension, and so on. Segmenting the liver, portal vein (PV), and inferior vena cava (IVC) can give useful description for the diagnosis, and is a basis work of a pres-surgery planning system and a virtual endoscope. To apply the proposed method, fuzzy if-then rules are derived from the time-density curve of ROIs. In the experimental results, the 2-D reconstructed and 3-D rendered images of the segmented liver, PV, and IVC are shown. The evaluation by a physician shows that the generated images are comparable to the hepatic anatomy, and they would be useful to understanding, diagnosis, and pre-surgery planning.

  3. Fault tolerant synchronization of chaotic heavy symmetric gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control.

    PubMed

    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.

  4. Accurate crop classification using hierarchical genetic fuzzy rule-based systems

    NASA Astrophysics Data System (ADS)

    Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

    2014-10-01

    This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

  5. An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems

    NASA Astrophysics Data System (ADS)

    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.

  6. Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block.

    PubMed

    Shieh, J S; Fan, S Z; Chang, L W; Liu, C C

    2000-01-01

    activity. The results showed that a hierarchical rule-based monitoring and fuzzy logic control architecture can provide stable control of neuromuscular block despite the considerable individual variation in neuromuscular block required among patients. Also, there was less variation in T1% error compared with that of previous study on mivacurium. Meanwhile, the consistent medium CV of the MIR of both rocuronium and mivacurium indicated a good controller activity which is able to withstand noise, diathermy effect, artifacts and surgical disturbances.

  7. Optimal fuel loading pattern design using an artificial neural network and a fuzzy rule-based system

    SciTech Connect

    Han Gon Kim; Soon Heung Chang; Byung Ho Lee )

    1993-10-01

    The Optimal Fuel Shuffling System (OFSS) was developed for the optimal design of pressurized water reactor (PWR) fuel loading patterns. An optimal loading pattern is defined in which the local power peaking factor is lower than a predetermined value during one cycle and the effective multiplication factor is maximized to extract the maximum energy. The OFSS is a hybrid system in which a rule-based system, fuzzy logic, and an artificial neural network (ANN) are connected with each other. The rule-based system classifies loading patterns into two types by using several heuristic rules and a fuzzy rule. The fuzzy rule is introduced to achieve a more effective and faster search. Its membership function is automatically updated in accordance with the prediction results. The ANN predicts core parameters for the patterns generated from the rule-based system. A back propagation network is used for fast prediction of the core parameters. The ANN and fuzzy logic can be used to improve the capabilities of existing algorithms. The OFSS was demonstrated and validated for cycle 1 of the Kori-1 PWR.

  8. Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

    NASA Astrophysics Data System (ADS)

    Sundareswaran, Kartik S.; Frakes, David H.; Yoganathan, Ajit P.

    2008-02-01

    Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

  9. Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems

    PubMed Central

    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. PMID:28045964

  10. Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.

    PubMed

    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.

  11. A new intuitionistic fuzzy rule-based decision-making system for an operating system process scheduler.

    PubMed

    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.

  12. Automatic system for brain MRI analysis using a novel combination of fuzzy rule-based and automatic clustering techniques

    NASA Astrophysics Data System (ADS)

    Hillman, Gilbert R.; Chang, Chih-Wei; Ying, Hao; Kent, T. A.; Yen, John

    1995-05-01

    Analysis of magnetic resonance images (MRI) of the brain permits the identification and measurement of brain compartments. These compartments include normal subdivisions of brain tissue, such as gray matter, white matter and specific structures, and also include pathologic lesions associated with stroke or viral infection. A fuzzy system has been developed to analyze images of animal and human brain, segmenting the images into physiologically meaningful regions for display and measurement. This image segmentation system consists of two stages which include a fuzzy rule-based system and fuzzy c-means algorithm (FCM). The first stage of this system is a fuzzy rule-based system which classifies most pixels in MR images into several known classes and one `unclassified' group, which fails to fit the predetermined rules. In the second stage, this system uses the result of the first stage as initial estimates for the properties of the compartments and applies FCM to classify all the previously unclassified pixels. The initial prototypes are estimated by using the averages of the previously classified pixels. The combined processes constitute a fast, accurate and robust image segmentation system. This method can be applied to many clinical image segmentation problems. While the rule-based portion of the system allows specialized knowledge about the images to be incorporated, the FCM allows the resolution of ambiguities that result from noise and artifacts in the image data. The volumes and locations of the compartments can easily be measured and reported quantitatively once they are identified. It is easy to adapt this approach to new imaging problems, by introducing a new set of fuzzy rules and adjusting the number of expected compartments. However, for the purpose of building a practical fully automatic system, a rule learning mechanism may be necessary to improve the efficiency of modification of the fuzzy rules.

  13. A rule-based fuzzy logic controller for a PWM inverter in a stand alone wind energy conversion scheme

    SciTech Connect

    Hilloowala, R.M.; Sharaf, A.M.

    1996-01-01

    The paper presents a rule-based fuzzy logic controller to control the output power of a pulse width modulated (PWM) inverter used in a stand alone wind energy conversion scheme (SAWECS). The self-excited induction generator used in SAWECS has the inherent problem of fluctuations in the magnitude and frequency of its terminal voltage with changes in wind velocity and load. To overcome this drawback the variable magnitude, variable frequency voltage at the generator terminals is rectified and the dc power is transferred to the load through a PWM inverter. The objective is to track and extract maximum power from the wind energy system (WES) and transfer this power to the local isolated load. This is achieved by using the fuzzy logic controller which regulates the modulation index of the PWM inverter based on the input signals: the power error e = (P{sub ref} {minus} P{sub o}) and its rate of change {dot e}. These input signals are fuzzified, that is defined by a set of linguistic labels characterized by their membership functions predefined for each class. Using a set of 49 rules which relate the fuzzified input signals (e, {dot e}) to the fuzzy controller output U, fuzzy set theory and associated fuzzy logic operations, the fuzzy controller`s output (in terms of linguistic labels) is defuzzified to obtain the actual analog (numerical) output signal which is then used to control the PWM inverter and ensure complete utilization of the available wind energy. The proposed rule-based fuzzy logic controller is simulated and the results are experimentally verified on a scaled down laboratory prototype of the SAWECS.

  14. Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model

    NASA Astrophysics Data System (ADS)

    Farahi Ghasre Aboonasr, Sedigheh; Zamani, Ahmad; Razavipour, Fatemeh; Boostani, Reza

    2017-06-01

    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.

  15. A Fuzzy Rule Based Decision Support System for Identifying Location of Water Harvesting Technologies in Rainfed Agricultural Regions

    NASA Astrophysics Data System (ADS)

    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.

  16. Knowledge extraction from neural networks using the all-permutations fuzzy rule base: the LED display recognition problem.

    PubMed

    Kolman, Eyal; Margaliot, Michael

    2007-05-01

    A major drawback of artificial neural networks (ANNs) is their black-box character. Even when the trained network performs adequately, it is very difficult to understand its operation. In this letter, we use the mathematical equivalence between ANNs and a specific fuzzy rule base to extract the knowledge embedded in the network. We demonstrate this using a benchmark problem: the recognition of digits produced by a light emitting diode (LED) device. The method provides a symbolic and comprehensible description of the knowledge learned by the network during its training.

  17. Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

    SciTech Connect

    Hamadameen, Abdulqader Othman; Zainuddin, Zaitul Marlizawati

    2014-06-19

    This study deals with multiobjective fuzzy stochastic linear programming problems with uncertainty probability distribution which are defined as fuzzy assertions by ambiguous experts. The problem formulation has been presented and the two solutions strategies are; the fuzzy transformation via ranking function and the stochastic transformation when α{sup –}. cut technique and linguistic hedges are used in the uncertainty probability distribution. The development of Sen’s method is employed to find a compromise solution, supported by illustrative numerical example.

  18. Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

    NASA Astrophysics Data System (ADS)

    Hamadameen, Abdulqader Othman; Zainuddin, Zaitul Marlizawati

    2014-06-01

    This study deals with multiobjective fuzzy stochastic linear programming problems with uncertainty probability distribution which are defined as fuzzy assertions by ambiguous experts. The problem formulation has been presented and the two solutions strategies are; the fuzzy transformation via ranking function and the stochastic transformation when α-. cut technique and linguistic hedges are used in the uncertainty probability distribution. The development of Sen's method is employed to find a compromise solution, supported by illustrative numerical example.

  19. Equipment fault diagnosis system of sequencing batch reactors using rule-based fuzzy inference and on-line sensing data.

    PubMed

    Kim, Y J; Bae, H; Poo, K M; Ko, J H; Kim, B G; Park, T J; Kim, C W

    2006-01-01

    The importance of a detection technique to prevent process deterioration is increasing. For the fast detection of this disturbance, a diagnostic algorithm was developed to determine types of equipment faults by using on-line ORP and DO profile in sequencing batch reactors (SBRs). To develop the rule base for fault diagnosis, the sensor profiles were obtained from a pilot-scale SBR when blower, influent pump and mixer were broken. The rules were generated based on the calculated error between an abnormal profile and a normal profile, e(ORP)(t) and e(DO)(t). To provide intermediate diagnostic results between "normal" and "fault", a fuzzy inference algorithm was incorporated to the rules. Fuzzified rules could present the diagnosis result "need to be checked". The diagnosis showed good performance in detecting and diagnosing various faults. The developed algorithm showed its applicability to detect faults and make possible fast action to correct them.

  20. Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia

    PubMed Central

    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

  1. Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks

    NASA Astrophysics Data System (ADS)

    Wu, Zhengping; Wu, Hao

    With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.

  2. Fuzzy rule-based approach to segment the menisci regions from MR images

    NASA Astrophysics Data System (ADS)

    Sasaki, Takashi; Hata, Yutaka; Ando, Yoshiro; Ishikawa, Makato; Ishikawa, Hitoshi

    1999-05-01

    Injuries of the menisci are one of the most common internal derangement of the knee. To examine them with noninvasive, we propose an automated segmentation method of the menisci region from MR image. The method is composed of two steps based on fuzzy logic. First, we segment the cartilage region by thresholding of the intensity. We then extract the candidate region of the menisci as the region between the cartilages. Second, we segment the menisci voxels from the candidate region based on fuzzy if-then rules obtained from knowledge of location and intensity. We applied our method to five MR data sets. Three of them are the normal knees and the others are with some injuries. Quantitative evaluation by a physician shows that this method can successfully segment the menisci for the all. The generated visualizations will help medical doctor to diagnose the menisci with noninvasive.

  3. Developing a fuzzy rule based cognitive map for total system safety assessment

    SciTech Connect

    Lemos, Francisco Luiz de; Sullivan, Terry

    2007-07-01

    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

  4. Segmentation of the fractured foot CT image: a fuzzy-rule-based approach

    NASA Astrophysics Data System (ADS)

    Hirano, Shoji; Hata, Yutaka; Matsui, Nobuyuki; Ando, Yoshiro; Ishikawa, Makato

    2000-06-01

    This paper presents an automated method for segmenting CT images of the fractured foot. Segmentation boundary is determined by fuzzy inference with two types of knowledge acquired from orthopedic surgeons. Knowledge of joint is used to determine the boundary of adjacent normal bones. It gives higher degree to the articular cartilage according to local structure (parallelity) and intensity distribution around a joint part. Knowledge of fragment is used to find a contact place of fragments. It evaluates Euclidian distance map (EDM) of the contact place and gives higher degree to the narrow part. Each of the knowledge is represented by fuzzy if-then rules, which can provide degrees for segmentation boundary. By evaluating the degrees in region growing process, a whole foot bone is decomposed into each of anatomically meaningful bones and fragments. An experiment was done on CT images of the subjects who have depressed fractures on their calcanei. The method could effectively give higher degrees on the essential boundary, suppressing generation of useless boundary caused by the internal cavities in the bone. Each of the normal bones and fragments were correctly segmented.

  5. Fuzzy rule-based model for optimum orientation of solar panels using satellite image processing

    NASA Astrophysics Data System (ADS)

    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.

  6. Fuzzy rule-based expert system for assessment severity of asthma.

    PubMed

    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.

  7. Fuzzy rule-based expert system for evaluating level of asthma control.

    PubMed

    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.

  8. Analysis of Aircraft Control Performance using a Fuzzy Rule Base Representation of the Cooper-Harper Aircraft Handling Quality Rating

    NASA Technical Reports Server (NTRS)

    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

  9. Analysis of Aircraft Control Performance using a Fuzzy Rule Base Representation of the Cooper-Harper Aircraft Handling Quality Rating

    NASA Technical Reports Server (NTRS)

    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

  10. A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system

    PubMed Central

    Marateb, Hamid Reza; Goudarzi, Sobhan

    2015-01-01

    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. PMID:26109965

  11. Hemodynamic management of congestive heart failure by means of a multiple mode rule-based control system using fuzzy logic.

    PubMed

    Held, C M; Roy, R J

    2000-01-01

    A rule-based system was designed to control the mean arterial pressure (MAP) and the cardiac output (CO) of a patient with congestive heart failure (CHF), using two vasoactive drugs: sodium nitroprusside (SNP) and dopamine (DPM). The controller has three different modes, that engage according to the hemodynamic state. The critical conditions control mode (CCC) determines the initial infusion rates, and continues active if the MAP or the CO fall outside of the defined criticality thresholds: an upper and a lower boundary for the MAP and a lower boundary for the CO. Inside the boundaries the control is performed by noncritical conditions control modes (NCC's), which are fuzzy logic controllers. If the CO is within normal range and the MAP is close to the goal range, then the MAP is driven using only SNP, in a single-input-single-output mode (NCC-SISO). Otherwise the NCC multiple-input-multiple-output is active (NCC-MIMO). The goal values for the controlled variables are defined as a band of 5 mmHg for the MAP and 5 mL/kg/min for the CO, but there is little concern for this application if the CO is too high (i.e., in practical terms the CO only needs to achieve a necessary minimum rate). The NCC-MIMO includes a gain adaptation algorithm to cope with the wide variety in sensitivities to SNP. Supervisory capabilities to ensure adequate drug delivery complete the controller scheme. After extensive testing and tuning on a CHF-hemodynamics nonlinear model, the control system was applied in dog experiments, which led to further enhancements. The results show an adequate control, presenting a fast response to setpoint changes with an acceptable overshoot.

  12. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships.

    PubMed

    Chen, Shyi-Ming; Chen, Shen-Wen

    2015-03-01

    In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy-trend logical relationships. Firstly, the proposed method fuzzifies the historical training data of the main factor and the secondary factor into fuzzy sets, respectively, to form two-factors second-order fuzzy logical relationships. Then, it groups the obtained two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, it calculates the probability of the "down-trend," the probability of the "equal-trend" and the probability of the "up-trend" of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group, respectively. Finally, it performs the forecasting based on the probabilities of the down-trend, the equal-trend, and the up-trend of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the NTD/USD exchange rates. The experimental results show that the proposed method outperforms the existing methods.

  13. Simulation of operating rules and discretional decisions using a fuzzy rule-based system integrated into a water resources management model

    NASA Astrophysics Data System (ADS)

    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

  14. A rule-based decision-making diagnosis system to evaluate arteriovenous shunt stenosis for hemodialysis treatment of patients using fuzzy petri nets.

    PubMed

    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.

  15. Prediction of ground water quality index to assess suitability for drinking purposes using fuzzy rule-based approach

    NASA Astrophysics Data System (ADS)

    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.

  16. A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories.

    PubMed

    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.

  17. Application of intelligent systems in asthma disease: designing a fuzzy rule-based system for evaluating level of asthma exacerbation.

    PubMed

    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.

  18. Nodule detection in a lung region that's segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding.

    PubMed

    Ozekes, Serhat; Osman, Onur; Ucan, Osman 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.

  19. Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

    PubMed Central

    Osman, Onur; Ucan, Osman N.

    2008-01-01

    Objective 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. Materials and Methods 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. Results 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. Conclusion 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. PMID:18253070

  20. Fuzzy backward reasoning using fuzzy Petri nets.

    PubMed

    Chen, S M

    2000-01-01

    Chen, Ke and Chang (1990) have presented a fuzzy forward reasoning algorithm for rule-based systems using fuzzy Petri nets. In this paper, we extend the work of Chen, Ke and Chang (1990) to present a fuzzy backward reasoning algorithm for rule-based systems using fuzzy Petri nets, where the fuzzy production rules of a rule-based system are represented by fuzzy Petri nets. The system can perform fuzzy backward reasoning automatically to evaluate the degree of truth of any proposition specified by the user. The fuzzy backward reasoning capability allows the computers to perform reasoning in a more flexible manner and to think more like people.

  1. Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

    PubMed Central

    Doubravsky, Karel; Dohnal, Mirko

    2015-01-01

    Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

  2. Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

    PubMed

    Doubravsky, Karel; Dohnal, Mirko

    2015-01-01

    Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

  3. Development of Real Time Implementation of 5/5 Rule based Fuzzy Logic Controller Shunt Active Power Filter for Power Quality Improvement

    NASA Astrophysics Data System (ADS)

    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.

  4. Linking probability theory and fuzzy sets : a study in uncertainty assessment

    SciTech Connect

    Booker, J. M.; Bement, T. R.; Sellers, K. F.; Singpurwalla, Nozer D.

    2002-01-01

    Uncertainties enter into a complex problem from many sources: variability, errors, and lack of knowledge. A fundamental question arises in how to characterize the various kinds of uncertainty and then combine within a problem such as the verification and validation of a computer model, reliability of a dynamic system, or a complex decision problem. Our aim is to explore how probability theory and fuzzy set theory can be made to work in concert, so that uncertainty of outcomes and imprecision can be treated in a unified and coherent manner. Both the theoretical and application of a linkage between the two theories will be presented. An example from a reliability application will illustrate how the linkage between the theories is accomplished through the use of Bayes Theorem and the probability of fuzzy sets.

  5. Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor.

    PubMed

    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.

  6. Reliable Sampled-Data Control of Fuzzy Markovian Systems with Partly Known Transition Probabilities

    NASA Astrophysics Data System (ADS)

    Sakthivel, R.; Kaviarasan, B.; Kwon, O. M.; Rathika, M.

    2016-08-01

    This article presents a fuzzy dynamic reliable sampled-data control design for nonlinear Markovian jump systems, where the nonlinear plant is represented by a Takagi-Sugeno fuzzy model and the transition probability matrix for Markov process is permitted to be partially known. In addition, a generalised as well as more practical consideration of the real-world actuator fault model which consists of both linear and nonlinear fault terms is proposed to the above-addressed system. Then, based on the construction of an appropriate Lyapunov-Krasovskii functional and the employment of convex combination technique together with free-weighting matrices method, some sufficient conditions that promising the robust stochastic stability of system under consideration and the existence of the proposed controller are derived in terms of linear matrix inequalities, which can be easily solved by any of the available standard numerical softwares. Finally, a numerical example is provided to illustrate the validity of the proposed methodology.

  7. Testing the applicability of artificial intelligence techniques to the subject of erythemal ultraviolet solar radiation part one: the applicability of a fuzzy rule based approach.

    PubMed

    Riad, A M; Elminir, Hamdy K; Own, Hala S; Azzam, Yosry A

    2008-02-27

    This work presents the applicability of applying a fuzzy logic approach to the calculation of noontime erythemal UV irradiance for the plain areas of Egypt. When different combinations of data sets were examined from the test performance point of view, it was found that 91% of the whole series was estimated within a deviation of less than +/-10 mW/m(2), and 9% of these deviations lay within the range of +/-15 mW/m(2) to +/-25 mW/m(2).

  8. The double-slit experiment with Physarum polycephalum and p-adic valued probabilities and fuzziness

    NASA Astrophysics Data System (ADS)

    Schumann, Andrew; Adamatzky, Andrew

    2015-04-01

    Physarum polycephalum is a single-cell slime mould visible by unaided eye. When foraging for sources of nutrients the slime mould optimizes its body shape, or a network of protoplasmic tubes. This optimization can be interpreted as computation. Many experimental laboratory prototypes of Physarum-based non-silicon computing devices have been implemented recently yet the scope of the slime mould as a fuzzy processor was never explored. We are filling this gap and show, by implementing the double-slit experiment, that self-inconsistencies in the slime mould's behaviour cannot approximate atomic individual acts of Physarum. This finding is analogous to our inability to approximate single photons. We further interpret the Physarum's behaviour in terms of individual-collective duality using p-adic valued probabilities and fuzziness. We also construct a system of p-adic many-valued logic to describe experimental responses of Physarum and we show how this logic can be used in ?-adic fuzzy logic controllers on the medium of Physarum.

  9. A Neuro-Fuzzy Inference System Combining Wavelet Denoising, Principal Component Analysis, and Sequential Probability Ratio Test for Sensor Monitoring

    SciTech Connect

    Na, Man Gyun; Oh, Seungrohk

    2002-11-15

    A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.

  10. Fuzzy propositions weighted by veracities or how to relate fuzzy logic and probability theory for segmentation of ultrasound images

    NASA Astrophysics Data System (ADS)

    Debon, Renaud; Solaiman, Basel; Cauvin, Jean-Michel; Robaszkiewicz, Michel; Roux, Christian

    2002-03-01

    In medical imaging, and more generally in medical information, researches go towards fusion systems. Nowadays, the steps of information source definition, the pertinent data extraction and the fusion need to be conducted as a whole. In this work, our interest is related to the esophagus wall segmentation from ultrasound images sequences. We aim to elaborate a general methodology of data mining that coherently links works on data selection and fusion architectures, in order to extract useful information from raw data and to integrate efficiently the physician a prior. In the presented method, based on fuzzy logic, some fuzzy propositions are defined using physicians a prior knowledge. The use of probabilistic distributions, estimated thanks to a learning base of pathologic and non-pathologic cases, enables the veracity of these propositions to be qualified. This promising idea enables information to be managed through the consideration of both information imprecision and uncertainty. In the same time, the obtained benefit, when a prior knowledge source is injected in a fusion based decision system, can be quantified. By considering that, the fuzzyfication stage is optimized relatively to a given criteria using a genetic algorithm. By this manner, fuzzy sets corresponding to the physicians ambiguous a prior are defined objectively. At this level, we successively compare performances obtained when fuzzy functions are defined empirically and when they are optimized. We conclude this paper with the first results on esophagus wall segmentation and outline some further works.

  11. Use of an influence diagram and fuzzy probability for evaluating accident management in a boiling water reactor

    SciTech Connect

    Yu, D.; Kastenberg, W.E.; Okrent, D. . Mechanical, Aerospace, and Nuclear Engineering Dept.)

    1994-06-01

    A new approach is presented for evaluating the uncertainties inherent in severe accident management strategies. At first, this analysis considers accident management as a decision problem (i.e., applying a strategy compared with do nothing) and uses an influence diagram. To evaluate imprecise node probabilities in the influence diagram, the analysis introduces the concept of a fuzzy probability. When fuzzy logic is applied, fuzzy probabilities are easily propagated to obtain results. In addition, the results obtained provide not only information similar to the classical approach, which uses point-estimate values, but also additional information regarding the impact of using imprecise input data. As an illustrative example, the proposed methodology is applied to the evaluation of the drywell flooding strategy for a long-term station blackout sequence at the Peach Bottom nuclear power plant. The results show that the drywell flooding strategy is beneficial for preventing reactor vessel breach. It is also effective for reducing the probability of containment failure for both liner melt-through and late overpressurization. Even though uncertainty exists in the results, flooding is preferred to do nothing when evaluated in terms of two risk measures: early and late fatalities.

  12. Fuzzification of ASAT's rule based aimpoint selection

    NASA Astrophysics Data System (ADS)

    Weight, Thomas H.

    1993-06-01

    The aimpoint algorithms being developed at Dr. Weight and Associates are based on the concept of fuzzy logic. This approach does not require a particular type of sensor data or algorithm type, but allows the user to develop a fuzzy logic algorithm based on existing aimpoint algorithms and models. This provides an opportunity for the user to upgrade an existing system design to achieve higher performance at minimal cost. Many projects have aimpoint algorithms which are based on 'crisp' logic rule based algorithms. These algorithms are sensitive to glint, corner reflectors, or intermittent thruster firings, and to uncertainties in the a priori estimates of angle of attack. If these projects are continued through to a demonstration involving a launch to hit a target, it is quite possible that the crisp logic approaches will need to be upgraded to handle these important error sources.

  13. Coupled Probabilistic And Possibilistic Uncertainty Estimation In Rule-Based Analysis Systems

    NASA Astrophysics Data System (ADS)

    Tsoukalas, L.; Ragheb, M.

    1987-05-01

    A methodology is developed for estimating the Performance of monitored engineering devices. Inferencing and decision-making under uncertainty is considered in Production-Rule Analysis systems where the knowledge about the system is both probabilistic and possibilistic. In this case uncertainty is considered as consisting of two components: Randomness describing the uncertainty of occurrence of an object, and Fuzziness describing the imprecision of the meaning of the object. The concepts of information granularity and of the probability of a fuzzy event are used. Propagation of the coupled Probabilistic and possibilistic uncertainty is carried out over model-based systems using the Rule-Based paradigm. The approach provides a measure of both the performance level and the reliability of a device.

  14. An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease.

    PubMed

    Chiang, Hsiu-Sen; Pao, Shun-Chi

    2016-05-01

    Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early treatment of Alzheimer's disease can significantly reduce deterioration. Early diagnosis is difficult, and early symptoms are frequently overlooked. While much of the literature focuses on disease detection, the use of electroencephalography (EEG) in Alzheimer's diagnosis has received relatively little attention. This study combines the fuzzy and associative Petri net methodologies to develop a model for the effective and objective detection of Alzheimer's disease. Differences in EEG patterns between normal subjects and Alzheimer patients are used to establish prediction criteria for Alzheimer's disease, potentially providing physicians with a reference for early diagnosis, allowing for early action to delay the disease progression.

  15. Rule-based simulation models

    NASA Technical Reports Server (NTRS)

    Nieten, Joseph L.; Seraphine, Kathleen M.

    1991-01-01

    Procedural modeling systems, rule based modeling systems, and a method for converting a procedural model to a rule based model are described. Simulation models are used to represent real time engineering systems. A real time system can be represented by a set of equations or functions connected so that they perform in the same manner as the actual system. Most modeling system languages are based on FORTRAN or some other procedural language. Therefore, they must be enhanced with a reaction capability. Rule based systems are reactive by definition. Once the engineering system has been decomposed into a set of calculations using only basic algebraic unary operations, a knowledge network of calculations and functions can be constructed. The knowledge network required by a rule based system can be generated by a knowledge acquisition tool or a source level compiler. The compiler would take an existing model source file, a syntax template, and a symbol table and generate the knowledge network. Thus, existing procedural models can be translated and executed by a rule based system. Neural models can be provide the high capacity data manipulation required by the most complex real time models.

  16. Rules based process window OPC

    NASA Astrophysics Data System (ADS)

    O'Brien, Sean; Soper, Robert; Best, Shane; Mason, Mark

    2008-03-01

    As a preliminary step towards Model-Based Process Window OPC we have analyzed the impact of correcting post-OPC layouts using rules based methods. Image processing on the Brion Tachyon was used to identify sites where the OPC model/recipe failed to generate an acceptable solution. A set of rules for 65nm active and poly were generated by classifying these failure sites. The rules were based upon segment runlengths, figure spaces, and adjacent figure widths. 2.1 million sites for active were corrected in a small chip (comparing the pre and post rules based operations), and 59 million were found at poly. Tachyon analysis of the final reticle layout found weak margin sites distinct from those sites repaired by rules-based corrections. For the active layer more than 75% of the sites corrected by rules would have printed without a defect indicating that most rulesbased cleanups degrade the lithographic pattern. Some sites were missed by the rules based cleanups due to either bugs in the DRC software or gaps in the rules table. In the end dramatic changes to the reticle prevented catastrophic lithography errors, but this method is far too blunt. A more subtle model-based procedure is needed changing only those sites which have unsatisfactory lithographic margin.

  17. Rule-Based Runtime Verification

    NASA Technical Reports Server (NTRS)

    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.

  18. Water quality analysis in rivers with non-parametric probability distributions and fuzzy inference systems: application to the Cauca River, Colombia.

    PubMed

    Ocampo-Duque, William; Osorio, Carolina; Piamba, Christian; Schuhmacher, Marta; Domingo, José L

    2013-02-01

    The integration of water quality monitoring variables is essential in environmental decision making. Nowadays, advanced techniques to manage subjectivity, imprecision, uncertainty, vagueness, and variability are required in such complex evaluation process. We here propose a probabilistic fuzzy hybrid model to assess river water quality. Fuzzy logic reasoning has been used to compute a water quality integrative index. By applying a Monte Carlo technique, based on non-parametric probability distributions, the randomness of model inputs was estimated. Annual histograms of nine water quality variables were built with monitoring data systematically collected in the Colombian Cauca River, and probability density estimations using the kernel smoothing method were applied to fit data. Several years were assessed, and river sectors upstream and downstream the city of Santiago de Cali, a big city with basic wastewater treatment and high industrial activity, were analyzed. The probabilistic fuzzy water quality index was able to explain the reduction in water quality, as the river receives a larger number of agriculture, domestic, and industrial effluents. The results of the hybrid model were compared to traditional water quality indexes. The main advantage of the proposed method is that it considers flexible boundaries between the linguistic qualifiers used to define the water status, being the belongingness of water quality to the diverse output fuzzy sets or classes provided with percentiles and histograms, which allows classify better the real water condition. The results of this study show that fuzzy inference systems integrated to stochastic non-parametric techniques may be used as complementary tools in water quality indexing methodologies.

  19. Automated revision of CLIPS rule-bases

    NASA Technical Reports Server (NTRS)

    Murphy, Patrick M.; Pazzani, Michael J.

    1994-01-01

    This paper describes CLIPS-R, a theory revision system for the revision of CLIPS rule-bases. CLIPS-R may be used for a variety of knowledge-base revision tasks, such as refining a prototype system, adapting an existing system to slightly different operating conditions, or improving an operational system that makes occasional errors. We present a description of how CLIPS-R revises rule-bases, and an evaluation of the system on three rule-bases.

  20. Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

    NASA Astrophysics Data System (ADS)

    Khoury, Mehdi; Liu, Honghai

    This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.

  1. Introduction to Fuzzy Set Theory

    NASA Technical Reports Server (NTRS)

    Kosko, Bart

    1990-01-01

    An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

  2. Genetic learning in rule-based and neural systems

    NASA Technical Reports Server (NTRS)

    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.

  3. Genetic learning in rule-based and neural systems

    NASA Technical Reports Server (NTRS)

    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.

  4. A Novel Rules Based Approach for Estimating Software Birthmark

    PubMed Central

    Binti Alias, Norma; 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. PMID:25945363

  5. A novel rules based approach for estimating software birthmark.

    PubMed

    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.

  6. Computing Probability Masses in Rule-Based Systems

    DTIC Science & Technology

    1982-09-08

    class. Further elimination of impossible pairings can be made by using logical rules such as those in PTAPS (Platform-Track Association Production...contacts is known to be that same type or class, the PTAPS rules will operate to match the two and eliminate all other pairings for contact X (in this case...Identification in a Production System. Technical Document 288, Naval Ocean Systems Center, October 17, 1979. [12] Dillard RA. Experimental Tests of PTAPS

  7. Use of fuzzy logic in lignite inventory estimation

    SciTech Connect

    Tutmez, B.; Dag, A.

    2007-07-01

    Seam thickness is one of the most important parameters for reserve estimation of a lignite deposit. This paper addresses a case study on fuzzy estimation of lignite seam thickness from spatial coordinates. From the relationships between input (Cartesian coordinates) and output (thickness) parameters, fuzzy clustering and a fuzzy rule-based inference system were designed. Data-driven fuzzy model parameters were derived from numerical values directly. In addition, estimations of the fuzzy model were compared with kriging estimations. It was concluded that the performance ofthe fuzzy model was more satisfactory. The results indicated that the fuzzy modeling approach is very reliable for the estimation of lignite reserves.

  8. Fuzzy logic particle tracking velocimetry

    NASA Technical Reports Server (NTRS)

    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.

  9. Evolving fuzzy rules in a learning classifier system

    NASA Technical Reports Server (NTRS)

    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.

  10. Designing boosting ensemble of relational fuzzy systems.

    PubMed

    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.

  11. Fuzzy Q-Learning for Generalization of Reinforcement Learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1996-01-01

    Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

  12. Fuzzy Q-Learning for Generalization of Reinforcement Learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1996-01-01

    Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

  13. Parallelism In Rule-Based Systems

    NASA Astrophysics Data System (ADS)

    Sabharwal, Arvind; Iyengar, S. Sitharama; de Saussure, G.; Weisbin, C. R.

    1988-03-01

    Rule-based systems, which have proven to be extremely useful for several Artificial Intelligence and Expert Systems applications, currently face severe limitations due to the slow speed of their execution. To achieve the desired speed-up, this paper addresses the problem of parallelization of production systems and explores the various architectural and algorithmic possibilities. The inherent sources of parallelism in the production system structure are analyzed and the trade-offs, limitations and feasibility of exploitation of these sources of parallelism are presented. Based on this analysis, we propose a dedicated, coarse-grained, n-ary tree multiprocessor architecture for the parallel implementation of rule-based systems and then present algorithms for partitioning of rules in this architecture.

  14. Rule-based modeling with Virtual Cell

    PubMed Central

    Schaff, James C.; Vasilescu, Dan; Moraru, Ion I.; Loew, Leslie M.; Blinov, Michael L.

    2016-01-01

    Summary: Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine. Availability and implementation: Available as VCell (versions 6.0 and later) at the Virtual Cell web site (http://vcell.org/). The application installs and runs on all major platforms and does not require registration for use on the user’s computer. Tutorials are available at the Virtual Cell website and Help is provided within the software. Source code is available at Sourceforge. Contact: vcell_support@uchc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27497444

  15. Rule-based modeling with Virtual Cell.

    PubMed

    Schaff, James C; Vasilescu, Dan; Moraru, Ion I; Loew, Leslie M; Blinov, Michael L

    2016-09-15

    Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine. Available as VCell (versions 6.0 and later) at the Virtual Cell web site (http://vcell.org/). The application installs and runs on all major platforms and does not require registration for use on the user's computer. Tutorials are available at the Virtual Cell website and Help is provided within the software. Source code is available at Sourceforge. vcell_support@uchc.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  16. Evolving rule-based systems in two medical domains using genetic programming.

    PubMed

    Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan; Axer, Hubertus; Bjerregaard, Beth; von Keyserlingk, Diedrich Graf

    2004-11-01

    To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

  17. A self-learning rule base for command following in dynamical systems

    NASA Technical Reports Server (NTRS)

    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.

  18. A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty.

    PubMed

    Hossain, Mohammad Shahadat; Ahmed, Faisal; Fatema-Tuj-Johora; Andersson, Karl

    2017-03-01

    The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100 % certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomness, ignorance and incompleteness. Consequently, traditional primary diagnosis, based on these signs and symptoms, which is carried out by the physicians, cannot deliver reliable results. Therefore, this article presents the design, development and applications of a Belief Rule Based Expert System (BRBES) with the ability to handle various types of uncertainties to diagnose TB. The knowledge base of this system is constructed by taking experts' suggestions and by analyzing historical data of TB patients. The experiments, carried out, by taking the data of 100 patients demonstrate that the BRBES's generated results are more reliable than that of human expert as well as fuzzy rule based expert system.

  19. Analysis and minimization of overtraining effect in rule-based classifiers for computer-aided diagnosis

    SciTech Connect

    Li Qiang; Doi Kunio

    2006-02-15

    Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists detect various lesions in medical images. In CAD schemes, classifiers play a key role in achieving a high lesion detection rate and a low false-positive rate. Although many popular classifiers such as linear discriminant analysis and artificial neural networks have been employed in CAD schemes for reduction of false positives, a rule-based classifier has probably been the simplest and most frequently used one since the early days of development of various CAD schemes. However, with existing rule-based classifiers, there are major disadvantages that significantly reduce their practicality and credibility. The disadvantages include manual design, poor reproducibility, poor evaluation methods such as resubstitution, and a large overtraining effect. An automated rule-based classifier with a minimized overtraining effect can overcome or significantly reduce the extent of the above-mentioned disadvantages. In this study, we developed an 'optimal' method for the selection of cutoff thresholds and a fully automated rule-based classifier. Experimental results performed with Monte Carlo simulation and a real lung nodule CT data set demonstrated that the automated threshold selection method can completely eliminate overtraining effect in the procedure of cutoff threshold selection, and thus can minimize overall overtraining effect in the constructed rule-based classifier. We believe that this threshold selection method is very useful in the construction of automated rule-based classifiers with minimized overtraining effect.

  20. An architecture for rule based system explanation

    NASA Technical Reports Server (NTRS)

    Fennel, T. R.; Johannes, James D.

    1990-01-01

    A system architecture is presented which incorporate both graphics and text into explanations provided by rule based expert systems. This architecture facilitates explanation of the knowledge base content, the control strategies employed by the system, and the conclusions made by the system. The suggested approach combines hypermedia and inference engine capabilities. Advantages include: closer integration of user interface, explanation system, and knowledge base; the ability to embed links to deeper knowledge underlying the compiled knowledge used in the knowledge base; and allowing for more direct control of explanation depth and duration by the user. User models are suggested to control the type, amount, and order of information presented.

  1. A fuzzy convolution model for radiobiologically optimized radiotherapy margins.

    PubMed

    Mzenda, Bongile; Hosseini-Ashrafi, Mir; Gegov, Alex; Brown, David J

    2010-06-07

    In this study we investigate the use of a new knowledge-based fuzzy logic technique to derive radiotherapy margins based on radiotherapy uncertainties and their radiobiological effects. The main radiotherapy uncertainties considered and used to build the model were delineation, set-up and organ motion-induced errors. The radiobiological effects of these combined errors, in terms of prostate tumour control probability and rectal normal tissue complication probability, were used to formulate the rule base and membership functions for a Sugeno type fuzzy system linking the error effect to the treatment margin. The defuzzified output was optimized by convolving it with a Gaussian convolution kernel to give a uniformly varying transfer function which was used to calculate the required treatment margins. The margin derived using the fuzzy technique showed good agreement compared to current prostate margins based on the commonly used margin formulation proposed by van Herk et al (2000 Int. J. Radiat. Oncol. Biol. Phys. 47 1121-35), and has nonlinear variation above combined errors of 5 mm standard deviation. The derived margin is on average 0.5 mm bigger than currently used margins in the region of small treatment uncertainties where margin reduction would be applicable. The new margin was applied in an intensity modulated radiotherapy prostate treatment planning example where margin reduction and a dose escalation regime were implemented, and by inducing equivalent treatment uncertainties, the resulting target and organs at risk doses were found to compare well to results obtained using currently recommended margins.

  2. A fuzzy classifier system for process control

    NASA Technical Reports Server (NTRS)

    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.

  3. Weighted Fuzzy Interpolative Reasoning Based on the Slopes of Fuzzy Sets and Particle Swarm Optimization Techniques.

    PubMed

    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.

  4. Life insurance risk assessment using a fuzzy logic expert system

    NASA Technical Reports Server (NTRS)

    Carreno, Luis A.; Steel, Roy A.

    1992-01-01

    In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.

  5. Life insurance risk assessment using a fuzzy logic expert system

    NASA Technical Reports Server (NTRS)

    Carreno, Luis A.; Steel, Roy A.

    1992-01-01

    In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.

  6. On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation.

    PubMed

    Wong, Shen Yuong; Yap, Keem Siah; Yap, Hwa Jen; Tan, Shing Chiang; Chang, Siow Wee

    2015-07-01

    This paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, three parameters of F-ELM are randomly assigned. They are the standard deviation of the membership functions, matrix-C (rule-combination matrix), and matrix-D [don't care (DC) matrix]. Fuzzy if-then rules are formulated by the rule-combination Matrix of F-ELM, and a DC approach is adopted to minimize the number of input attributes in the rules. Furthermore, F-ELM utilizes the output weights of the ELM to form the target class and confidence factor for each of the rules. This is to indicate that the corresponding consequent parameters are determined analytically. The operations of F-ELM are equivalent to a fuzzy inference system. Several benchmark data sets and a real world fault detection and diagnosis problem have been used to empirically evaluate the efficacy of the proposed F-ELM in handling pattern classification tasks. The results show that the accuracy rates of F-ELM are comparable (if not superior) to ELM with distinctive ability of providing explicit knowledge in the form of interpretable rule base.

  7. Rule-Based Flight Software Cost Estimation

    NASA Technical Reports Server (NTRS)

    Stukes, Sherry A.; Spagnuolo, John N. Jr.

    2015-01-01

    This paper discusses the fundamental process for the computation of Flight Software (FSW) cost estimates. This process has been incorporated in a rule-based expert system [1] that can be used for Independent Cost Estimates (ICEs), Proposals, and for the validation of Cost Analysis Data Requirements (CADRe) submissions. A high-level directed graph (referred to here as a decision graph) illustrates the steps taken in the production of these estimated costs and serves as a basis of design for the expert system described in this paper. Detailed discussions are subsequently given elaborating upon the methodology, tools, charts, and caveats related to the various nodes of the graph. We present general principles for the estimation of FSW using SEER-SEM as an illustration of these principles when appropriate. Since Source Lines of Code (SLOC) is a major cost driver, a discussion of various SLOC data sources for the preparation of the estimates is given together with an explanation of how contractor SLOC estimates compare with the SLOC estimates used by JPL. Obtaining consistency in code counting will be presented as well as factors used in reconciling SLOC estimates from different code counters. When sufficient data is obtained, a mapping into the JPL Work Breakdown Structure (WBS) from the SEER-SEM output is illustrated. For across the board FSW estimates, as was done for the NASA Discovery Mission proposal estimates performed at JPL, a comparative high-level summary sheet for all missions with the SLOC, data description, brief mission description and the most relevant SEER-SEM parameter values is given to illustrate an encapsulation of the used and calculated data involved in the estimates. The rule-based expert system described provides the user with inputs useful or sufficient to run generic cost estimation programs. This system's incarnation is achieved via the C Language Integrated Production System (CLIPS) and will be addressed at the end of this paper.

  8. A rule-based software test data generator

    NASA Technical Reports Server (NTRS)

    Deason, William H.; Brown, David B.; Chang, Kai-Hsiung; Cross, James H., II

    1991-01-01

    Rule-based software test data generation is proposed as an alternative to either path/predicate analysis or random data generation. A prototype rule-based test data generator for Ada programs is constructed and compared to a random test data generator. Four Ada procedures are used in the comparison. Approximately 2000 rule-based test cases and 100,000 randomly generated test cases are automatically generated and executed. The success of the two methods is compared using standard coverage metrics. Simple statistical tests showing that even the primitive rule-based test data generation prototype is significantly better than random data generation are performed. This result demonstrates that rule-based test data generation is feasible and shows great promise in assisting test engineers, especially when the rule base is developed further.

  9. A rule-based software test data generator

    NASA Technical Reports Server (NTRS)

    Deason, William H.; Brown, David B.; Chang, Kai-Hsiung; Cross, James H., II

    1991-01-01

    Rule-based software test data generation is proposed as an alternative to either path/predicate analysis or random data generation. A prototype rule-based test data generator for Ada programs is constructed and compared to a random test data generator. Four Ada procedures are used in the comparison. Approximately 2000 rule-based test cases and 100,000 randomly generated test cases are automatically generated and executed. The success of the two methods is compared using standard coverage metrics. Simple statistical tests showing that even the primitive rule-based test data generation prototype is significantly better than random data generation are performed. This result demonstrates that rule-based test data generation is feasible and shows great promise in assisting test engineers, especially when the rule base is developed further.

  10. Individual Differences in Base Rate Neglect: A Fuzzy Processing Preference Index

    PubMed Central

    Wolfe, Christopher R.; Fisher, Christopher R.

    2013-01-01

    Little is known about individual differences in integrating numeric base-rates and qualitative text in making probability judgments. Fuzzy-Trace Theory predicts a preference for fuzzy processing. We conducted six studies to develop the FPPI, a reliable and valid instrument assessing individual differences in this fuzzy processing preference. It consists of 19 probability estimation items plus 4 "M-Scale" items that distinguish simple pattern matching from “base rate respect.” Cronbach's Alpha was consistently above 0.90. Validity is suggested by significant correlations between FPPI scores and three other measurers: "Rule Based" Process Dissociation Procedure scores; the number of conjunction fallacies in joint probability estimation; and logic index scores on syllogistic reasoning. Replicating norms collected in a university study with a web-based study produced negligible differences in FPPI scores, indicating robustness. The predicted relationships between individual differences in base rate respect and both conjunction fallacies and syllogistic reasoning were partially replicated in two web-based studies. PMID:23935255

  11. Interpretation of artificial neural networks by means of fuzzy rules.

    PubMed

    Castro, J L; Mantas, C J; Benitez, J M

    2002-01-01

    This paper presents an extension of the method presented by Benitez et al (1997) for extracting fuzzy rules from an artificial neural network (ANN) that express exactly its behavior. The extraction process provides an interpretation of the ANN in terms of fuzzy rules. The fuzzy rules presented are in accordance with the domain of the input variables. These rules use a new operator in the antecedent. The properties and intuitive meaning of this operator are studied. Next, the role of the biases in the fuzzy rule-based systems is analyzed. Several examples are presented to comment on the obtained fuzzy rule-based systems. Finally, the interpretation of ANNs with two or more hidden layers is also studied.

  12. Adaptive Rule Based Fetal QRS Complex Detection Using Hilbert Transform

    PubMed Central

    Ulusar, Umit D.; Govindan, R.B.; Wilson, James D.; Lowery, Curtis L.; Preissl, Hubert; Eswaran, Hari

    2010-01-01

    In this paper we introduce an adaptive rule based QRS detection algorithm using the Hilbert transform (adHQRS) for fetal magnetocardiography processing. Hilbert transform is used to combine multiple channel measurements and the adaptive rule based decision process is used to eliminate spurious beats. The algorithm has been tested with a large number of datasets and promising results were obtained. PMID:19964648

  13. Adaptive rule based fetal QRS complex detection using Hilbert transform.

    PubMed

    Ulusar, Umit D; Govindan, R B; Wilson, James D; Lowery, Curtis L; Preissl, Hubert; Eswaran, Hari

    2009-01-01

    In this paper we introduce an adaptive rule based QRS detection algorithm using the Hilbert transform (adHQRS) for fetal magnetocardiography processing. Hilbert transform is used to combine multiple channel measurements and the adaptive rule based decision process is used to eliminate spurious beats. The algorithm has been tested with a large number of datasets and promising results were obtained.

  14. Improvement on fuzzy controller design techniques

    NASA Technical Reports Server (NTRS)

    Wang, Paul P.

    1993-01-01

    This paper addresses three main issues, which are somewhat interrelated. The first issue deals with the classification or types of fuzzy controllers. Careful examination of the fuzzy controllers designed by various engineers reveals distinctive classes of fuzzy controllers. Classification is believed to be helpful from different perspectives. The second issue deals with the design according to specifications, experiments related to the tuning of fuzzy controllers, according to the specification, will be discussed. General design procedure, hopefully, can be outlined in order to ease the burden of a design engineer. The third issue deals with the simplicity and limitation of the rule-based IF-THEN logical statements. The methodology of fuzzy-constraint network is proposed here as an alternative to the design practice at present. It is our belief that predicate calculus and the first order logic possess much more expressive power.

  15. Evolutionary design of a fuzzy classifier from data.

    PubMed

    Chang, Xiaoguang; Lilly, John H

    2004-08-01

    Genetic algorithms show powerful capabilities for automatically designing fuzzy systems from data, but many proposed methods must be subjected to some minimal structure assumptions, such as rule base size. In this paper, we also address the design of fuzzy systems from data. A new evolutionary approach is proposed for deriving a compact fuzzy classification system directly from data without any a priori knowledge or assumptions on the distribution of the data. At the beginning of the algorithm, the fuzzy classifier is empty with no rules in the rule base and no membership functions assigned to fuzzy variables. Then, rules and membership functions are automatically created and optimized in an evolutionary process. To accomplish this, parameters of the variable input spread inference training (VISIT) algorithm are used to code fuzzy systems on the training data set. Therefore, we can derive each individual fuzzy system via the VISIT algorithm, and then search the best one via genetic operations. To evaluate the fuzzy classifier, a fuzzy expert system acts as the fitness function. This fuzzy expert system can effectively evaluate the accuracy and compactness at the same time. In the application section, we consider four benchmark classification problems: the iris data, wine data, Wisconsin breast cancer data, and Pima Indian diabetes data. Comparisons of our method with others in the literature show the effectiveness of the proposed method.

  16. Analysis of direct action fuzzy PID controller structures.

    PubMed

    Mann, G I; Hu, B G; Gosine, R G

    1999-01-01

    The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, 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. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.

  17. A simple fuzzy logic real-time camera tracking system

    NASA Technical Reports Server (NTRS)

    Magee, Kevin N.; Cheatham, John B., Jr.

    1993-01-01

    A fuzzy logic control of camera pan and tilt has been implemented to provide real-time camera tracking of a moving object. The user clicks a mouse button to identify the object that is to be tracked. A rapid centroid estimation algorithm is used to estimate the location of the moving object, and based on simple fuzzy membership functions, fuzzy x and y values are input into a six-rule fuzzy logic rule base. The output of this system is de-fuzzified to provide pan and tilt velocities required to keep the image of the object approximately centered in the camera field of view.

  18. Rule-based Cross-matching of Very Large Catalogs

    NASA Astrophysics Data System (ADS)

    Ogle, P. M.; Mazzarella, J.; Ebert, R.; Fadda, D.; Lo, T.; Terek, S.; Schmitz, M.; NED Team

    2015-09-01

    The NASA Extragalactic Database (NED) has deployed a new rule-based cross-matching algorithm called Match Expert (MatchEx), capable of cross-matching very large catalogs (VLCs) with >10 million objects. MatchEx goes beyond traditional position-based cross-matching algorithms by using other available data together with expert logic to determine which candidate match is the best. Furthermore, the local background density of sources is used to determine and minimize the false-positive match rate and to estimate match completeness. The logical outcome and statistical probability of each match decision is stored in the database and may be used to tune the algorithm and adjust match parameter thresholds. For our first production run, we cross-matched the GALEX All Sky Survey Catalog (GASC), containing nearly 40 million NUV-detected sources, against a directory of 180 million objects in NED. Candidate matches were identified for each GASC source within a 7''.5 radius. These candidates were filtered on position-based matching probability and on other criteria including object type and object name. We estimate a match completeness of 97.6% and a match accuracy of 99.75%. Over the next year, we will be cross-matching over 2 billion catalog sources to NED, including the Spitzer Source List, the 2MASS point-source catalog, AllWISE, and SDSS DR 10. We expect to add new capabilities to filter candidate matches based on photometry, redshifts, and refined object classifications. We will also extend MatchEx to handle more heterogenous datasets federated from smaller catalogs through NED's literature pipeline.

  19. A logical model of cooperating rule-based systems

    NASA Technical Reports Server (NTRS)

    Bailin, Sidney C.; Moore, John M.; Hilberg, Robert H.; Murphy, Elizabeth D.; Bahder, Shari A.

    1989-01-01

    A model is developed to assist in the planning, specification, development, and verification of space information systems involving distributed rule-based systems. The model is based on an analysis of possible uses of rule-based systems in control centers. This analysis is summarized as a data-flow model for a hypothetical intelligent control center. From this data-flow model, the logical model of cooperating rule-based systems is extracted. This model consists of four layers of increasing capability: (1) communicating agents, (2) belief-sharing knowledge sources, (3) goal-sharing interest areas, and (4) task-sharing job roles.

  20. ASICs Approach for the Implementation of a Symmetric Triangular Fuzzy Coprocessor and Its Application to Adaptive Filtering

    NASA Technical Reports Server (NTRS)

    Starks, Scott; Abdel-Hafeez, Saleh; Usevitch, Bryan

    1997-01-01

    This paper discusses the implementation of a fuzzy logic system using an ASICs design approach. The approach is based upon combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets to obtain a novel structure for fuzzy logic system application development. The resulting structure utilizes a fuzzy static RAM to store the rule-base and the end-points of the triangular membership functions. This provides advantages over other approaches in which all sampled values of membership functions for all universes must be stored. The fuzzy coprocessor structure implements the fuzzification and defuzzification processes through a two-stage parallel pipeline architecture which is capable of executing complex fuzzy computations in less than 0.55us with an accuracy of more than 95%, thus making it suitable for a wide range of applications. Using the approach presented in this paper, a fuzzy logic rule-base can be directly downloaded via a host processor to an onchip rule-base memory with a size of 64 words. The fuzzy coprocessor's design supports up to 49 rules for seven fuzzy membership functions associated with each of the chip's two input variables. This feature allows designers to create fuzzy logic systems without the need for additional on-board memory. Finally, the paper reports on simulation studies that were conducted for several adaptive filter applications using the least mean squared adaptive algorithm for adjusting the knowledge rule-base.

  1. Extending rule-based methods to model molecular geometry and 3D model resolution.

    PubMed

    Hoard, Brittany; Jacobson, Bruna; Manavi, Kasra; Tapia, Lydia

    2016-08-01

    Computational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a method that can be used to simulate these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects. We propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and binding rates. We demonstrate how rules are constructed according to the molecular curvature. We then perform a study of antigen-antibody aggregation using our proposed method. We simulate the binding of antibody complexes to binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the aggregate sizes. Then, using our novel approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. We use the distances between the binding regions of Pen a 1 to optimize the rules and binding rates. We perform this procedure for multiple conformations of Pen a 1 and analyze the impact of conformation and resolution on the optimal rule-based model. We find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that antibodies will bind to these regions. These optimized models

  2. A composite self tuning strategy for fuzzy control of dynamic systems

    NASA Technical Reports Server (NTRS)

    Shieh, C.-Y.; Nair, Satish S.

    1992-01-01

    The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.

  3. Single board system for fuzzy inference

    NASA Technical Reports Server (NTRS)

    Symon, James R.; Watanabe, Hiroyuki

    1991-01-01

    The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990.

  4. Fuzzy multimodel of timed Petri nets.

    PubMed

    Hennequin, S; Lefebvre, D; El Moudni, A

    2001-01-01

    This paper deals with discrete event systems (DES) modeled either by discrete timed Petri nets without conflict or by continuous Petri nets. A fuzzy rule-based multimodel is developed for this kind of system. The behavior of each Petri net transition is described by the combination of two linear local fuzzy models. Using the Takagi-Sugemo model in a systematic way, we define the exact modeling for both classes of timed Petri nets. As a result, we notice that classical sets result in the exact description of discrete timed Petri nets. On the contrary, only fuzzy sets are suitable to describe continuous Petri nets exactly. The proposed fuzzy multimodels are very interesting from a control point of view. In that sense, general results such as convergence for timed Petri nets are given.

  5. Intelligent fuzzy controller for event-driven real time systems

    NASA Technical Reports Server (NTRS)

    Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.

    1992-01-01

    Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

  6. Implementation of fuzzy inference with neural network: the NNFI structure

    NASA Astrophysics Data System (ADS)

    Shu, Shyh-Yeong; Hwang, Chung-Mu

    1993-12-01

    In many fuzzy system applications, the most difficult and time consuming problem is to built the fuzzy rule base. Usually, to build fuzzy rule base depends on a domain expert to reflect his experience. But for a complicated system, it is sometimes difficult for an expert to describe clearly the causal relationships among those linguistic variables. To overcome such a problem, a dense connectionist structure of artificial neural network, called as NN-Fuzzy Inferencer (NNFI), is constructed to implement the fuzzy inference. This NNFI incorporates the effects of neural network and fuzzy inference. It is trainable and gets a more desired output value than backpropagation neural network does. The idea of the NNFI architecture is driven from the traditional fuzzy inference method. It can avoid not only the difficulty that for a designer to define the casual relations between the input variables and output variables, but also determine the membership function for each linguistic value. Furthermore, the system will generate the weighting coefficients in antecedent part and consequent part respectively in every fuzzy rule.

  7. Suppression of impulse noise in medical images with the use of Fuzzy Adaptive Median Filter.

    PubMed

    Toprak, Abdullah; Güler, Inan

    2006-12-01

    A new rule based fuzzy filter for removal of highly impulse noise, called Rule Based Fuzzy Adaptive Median (RBFAM) Filter, is aimed to be discussed in this paper. The RBFAM filter is an improved version of Adaptive Median Filter (AMF) and is presented in the aim of noise reduction of images corrupted with additive impulse noise. The filter has three stages. Two of those stages are fuzzy rule based and last stage is based on standard median and adaptive median filter. The proposed filter can preserve image details better then AMF while suppressing additive salt & pepper or impulse type noise. In this paper, we placed our preference on bell-shaped membership function instead of triangular membership function in order to observe better results. Experimental results indicates that the proposed filter is improvable with increased fuzzy rules to reduce more noise corrupted images and to remove salt and pepper noise in a more effective way than what AMF filter does.

  8. Fusion of multiple algorithms for detecting buried objects using fuzzy inference

    NASA Astrophysics Data System (ADS)

    Khalifa, Amine; Frigui, Hichem

    2014-05-01

    We present a fusion method, based on fuzzy inference, for detecting buried objects using ground-penetrating radar (GPR) data. The GPR sensor generates 3-dimensional data that correspond to depth, down-track, and cross-track. Most discrimination algorithms process only 2-D slices of the 3-D cube: (down-track, depth) or (cross-track, depth). The performance of the down-track and cross-track discrimination algorithms can vary significantly depending on the target shape, burial orientation, and other environmental conditions. In some cases, these algorithms can provide complementary evidence, while in other cases they provide contradicting evidence. Thus, effective fusion of these algorithms can achieve higher probability of detection with fewer false alarms. The proposed fusion method is capable of learning meaningful and simple fuzzy rules for different regions of the input space, generated by partial confidence values of the different discriminators as well as additional background information. To learn the rules, first, the input space is partitioned to identify local contexts. Second, input membership functions are learned based on the distribution of the partial confidence values of the individual discriminators within each context. Third, output membership functions are generated by considering the relative numbers of targets and non-targets within each context. Finally, the input and output membership functions are combined into a Mamdani-type fuzzy inference system. The output of the learning process is a fuzzy rule base adapted to different contexts. Results on large and diverse GPR data collections show that the proposed fusion can identify local, simple, and meaningful rules capable of non-linear fusion of different discriminators. We also show that the proposed fuzzy inference outperforms commonly used fusion methods.

  9. Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping

    NASA Astrophysics Data System (ADS)

    Lucas, Richard; Rowlands, Aled; Brown, Alan; Keyworth, Steve; Bunting, Peter

    AimTo evaluate the use of time-series of Landsat sensor data acquired over an annual cycle for mapping semi-natural habitats and agricultural land cover. LocationBerwyn Mountains, North Wales, United Kingdom. MethodsUsing eCognition Expert, segmentation of the Landsat sensor data was undertaken for actively managed agricultural land based on Integrated Administration and Control System (IACS) land parcel boundaries, whilst a per-pixel level segmentation was undertaken for all remaining areas. Numerical decision rules based on fuzzy logic that coupled knowledge of ecology and the information content of single and multi-date remotely sensed data and derived products (e.g., vegetation indices) were developed to discriminate vegetation types based primarily on inferred differences in phenology, structure, wetness and productivity. ResultsThe rule-based classification gave a good representation of the distribution of habitats and agricultural land. The more extensive, contiguous and homogeneous habitats could be mapped with accuracies exceeding 80%, although accuracies were lower for more complex environments (e.g., upland mosaics) or those with broad definition (e.g., semi-improved grasslands). Main conclusionsThe application of a rule-based classification to temporal imagery acquired over selected periods within an annual cycle provides a viable approach for mapping and monitoring of habitats and agricultural land in the United Kingdom that could be employed operationally.

  10. Fuzzy Deterrence

    DTIC Science & Technology

    2010-05-01

    cognitive map. Three examples illustrate fuzzy cognitive maps‘ potential for understanding a non -state actor‘s decision-making calculus and...of the Cold War, the United States has wrestled with how rational deterrence applies to non -state actors in today’s complex security environment...Fuzzy logic’s themes of flexibility, adaptability, and ambiguity lay the foundation for applying fuzzy logic to non -state actor deterrence. Because

  11. Fuzzy resource optimization for safeguards

    SciTech Connect

    Zardecki, A.; Markin, J.T.

    1991-01-01

    Authorization, enforcement, and verification -- three key functions of safeguards systems -- form the basis of a hierarchical description of the system risk. When formulated in terms of linguistic rather than numeric attributes, the risk can be computed through an algorithm based on the notion of fuzzy sets. Similarly, this formulation allows one to analyze the optimal resource allocation by maximizing the overall detection probability, regarded as a linguistic variable. After summarizing the necessary elements of the fuzzy sets theory, we outline the basic algorithm. This is followed by a sample computation of the fuzzy optimization. 10 refs., 1 tab.

  12. The Neural Correlates of Similarity- and Rule-based Generalization.

    PubMed

    Milton, Fraser; Bealing, Pippa; Carpenter, Kathryn L; Bennattayallah, Abdelmalek; Wills, Andy J

    2017-01-01

    The idea that there are multiple learning systems has become increasingly influential in recent years, with many studies providing evidence that there is both a quick, similarity-based or feature-based system and a more effortful rule-based system. A smaller number of imaging studies have also examined whether neurally dissociable learning systems are detectable. We further investigate this by employing for the first time in an imaging study a combined positive and negative patterning procedure originally developed by Shanks and Darby [Shanks, D. R., & Darby, R. J. Feature- and rule-based generalization in human associative learning. Journal of Experimental Psychology: Animal Behavior Processes, 24, 405-415, 1998]. Unlike previous related studies employing other procedures, rule generalization in the Shanks-Darby task is beyond any simple non-rule-based (e.g., associative) account. We found that rule- and similarity-based generalization evoked common activation in diverse regions including the pFC and the bilateral parietal and occipital lobes indicating that both strategies likely share a range of common processes. No differences between strategies were identified in whole-brain comparisons, but exploratory analyses indicated that rule-based generalization led to greater activation in the right middle frontal cortex than similarity-based generalization. Conversely, the similarity group activated the anterior medial frontal lobe and right inferior parietal lobes more than the rule group did. The implications of these results are discussed.

  13. Rule-Based Category Learning in Down Syndrome

    ERIC Educational Resources Information Center

    Phillips, B. Allyson; Conners, Frances A.; Merrill, Edward; Klinger, Mark R.

    2014-01-01

    Rule-based category learning was examined in youths with Down syndrome (DS), youths with intellectual disability (ID), and typically developing (TD) youths. Two tasks measured category learning: the Modified Card Sort task (MCST) and the Concept Formation test of the Woodcock-Johnson-III (Woodcock, McGrew, & Mather, 2001). In regression-based…

  14. Optimal Test Design with Rule-Based Item Generation

    ERIC Educational Resources Information Center

    Geerlings, Hanneke; van der Linden, Wim J.; Glas, Cees A. W.

    2013-01-01

    Optimal test-design methods are applied to rule-based item generation. Three different cases of automated test design are presented: (a) test assembly from a pool of pregenerated, calibrated items; (b) test generation on the fly from a pool of calibrated item families; and (c) test generation on the fly directly from calibrated features defining…

  15. A Rule-Based System for Test Quality Improvement

    ERIC Educational Resources Information Center

    Costagliola, Gennaro; Fuccella, Vittorio

    2009-01-01

    To correctly evaluate learners' knowledge, it is important to administer tests composed of good quality question items. By the term "quality" we intend the potential of an item in effectively discriminating between skilled and untrained students and in obtaining tutor's desired difficulty level. This article presents a rule-based e-testing system…

  16. Risk Levels for Rule-Based Weather Decision Aids

    DTIC Science & Technology

    2009-01-01

    Risk Levels for Rule-Based Weather Decision Aids, Army Research Laboratory Technical Report ARL-TR-4586, September 20008. 6. Richmond, P., Ed. Notes...Cold Weather on Productivity, in Technology Transfer Opportunities for the Construction Engineering Commmunity , Cold Regions Research and Engineering

  17. Rule-Based Category Learning in Down Syndrome

    ERIC Educational Resources Information Center

    Phillips, B. Allyson; Conners, Frances A.; Merrill, Edward; Klinger, Mark R.

    2014-01-01

    Rule-based category learning was examined in youths with Down syndrome (DS), youths with intellectual disability (ID), and typically developing (TD) youths. Two tasks measured category learning: the Modified Card Sort task (MCST) and the Concept Formation test of the Woodcock-Johnson-III (Woodcock, McGrew, & Mather, 2001). In regression-based…

  18. Optimal Test Design with Rule-Based Item Generation

    ERIC Educational Resources Information Center

    Geerlings, Hanneke; van der Linden, Wim J.; Glas, Cees A. W.

    2013-01-01

    Optimal test-design methods are applied to rule-based item generation. Three different cases of automated test design are presented: (a) test assembly from a pool of pregenerated, calibrated items; (b) test generation on the fly from a pool of calibrated item families; and (c) test generation on the fly directly from calibrated features defining…

  19. Data mining of gene expression data by fuzzy and hybrid fuzzy methods.

    PubMed

    Schaefer, Gerald; Nakashima, Tomoharu

    2010-01-01

    Microarray studies and gene expression analysis have received tremendous attention over the last few years and provide many promising avenues toward the understanding of fundamental questions in biology and medicine. Data mining of these vasts amount of data is crucial in gaining this understanding. In this paper, we present a fuzzy rule-based classification system that allows for effective analysis of gene expression data. The applied classifier consists of a set of fuzzy if-then rules that enable accurate nonlinear classification of input patterns. We further present a hybrid fuzzy classification scheme in which a small number of fuzzy if-then rules are selected through means of a genetic algorithm, leading to a compact classifier for gene expression analysis. Extensive experimental results on various well-known gene expression datasets confirm the efficacy of our approaches.

  20. Fuzzy logic

    NASA Technical Reports Server (NTRS)

    Zadeh, Lofti A.

    1988-01-01

    The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.

  1. Fuzzy logic

    NASA Technical Reports Server (NTRS)

    Zadeh, Lofti A.

    1988-01-01

    The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.

  2. Dynamic compensatory pattern matching in a fuzzy rule-based control system

    NASA Technical Reports Server (NTRS)

    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.

  3. Fuzzy architecture assessment for critical infrastructure resilience

    SciTech Connect

    Muller, George

    2012-12-01

    This paper presents an approach for the selection of alternative architectures in a connected infrastructure system to increase resilience of the overall infrastructure system. The paper begins with a description of resilience and critical infrastructure, then summarizes existing approaches to resilience, and presents a fuzzy-rule based method of selecting among alternative infrastructure architectures. This methodology includes considerations which are most important when deciding on an approach to resilience. The paper concludes with a proposed approach which builds on existing resilience architecting methods by integrating key system aspects using fuzzy memberships and fuzzy rule sets. This novel approach aids the systems architect in considering resilience for the evaluation of architectures for adoption into the final system architecture.

  4. Automated rule-base creation via CLIPS-Induce

    NASA Technical Reports Server (NTRS)

    Murphy, Patrick M.

    1994-01-01

    Many CLIPS rule-bases contain one or more rule groups that perform classification. In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information.

  5. Using new aggregation operators in rule-based intelligent control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Chen, Yung-Yaw; Yager, Ronald R.

    1990-01-01

    A new aggregation operator is applied in the design of an approximate reasoning-based controller. The ordered weighted averaging (OWA) operator has the property of lying between the And function and the Or function used in previous fuzzy set reasoning systems. It is shown here that, by applying OWA operators, more generalized types of control rules, which may include linguistic quantifiers such as Many and Most, can be developed. The new aggregation operators, as tested in a cart-pole balancing control problem, illustrate improved performance when compared with existing fuzzy control aggregation schemes.

  6. Using new aggregation operators in rule-based intelligent control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Chen, Yung-Yaw; Yager, Ronald R.

    1990-01-01

    A new aggregation operator is applied in the design of an approximate reasoning-based controller. The ordered weighted averaging (OWA) operator has the property of lying between the And function and the Or function used in previous fuzzy set reasoning systems. It is shown here that, by applying OWA operators, more generalized types of control rules, which may include linguistic quantifiers such as Many and Most, can be developed. The new aggregation operators, as tested in a cart-pole balancing control problem, illustrate improved performance when compared with existing fuzzy control aggregation schemes.

  7. An approach to articulating expert system rule bases

    NASA Technical Reports Server (NTRS)

    Abernethy, Ken

    1988-01-01

    A rule-base generation procedure is developed for expert systems used to diagnose anomalies in the performance of mechanical plants and similar engineering systems. The method is based on construction of a failure-mode information-propagation model (FIPM). Details of the FIPM procedure are discussed and illustrated with diagrams; reference is made to a sample application involving the turbopump of the high-pressure oxidizer for the Space Shuttle main engine.

  8. An Embedded Rule-Based Diagnostic Expert System in Ada

    NASA Technical Reports Server (NTRS)

    Jones, Robert E.; Liberman, Eugene M.

    1992-01-01

    Ada is becoming an increasingly popular programming language for large Government-funded software projects. Ada with it portability, transportability, and maintainability lends itself well to today's complex programming environment. In addition, expert systems have also assumed a growing role in providing human-like reasoning capability expertise for computer systems. The integration is discussed of expert system technology with Ada programming language, especially a rule-based expert system using an ART-Ada (Automated Reasoning Tool for Ada) system shell. NASA Lewis was chosen as a beta test site for ART-Ada. The test was conducted by implementing the existing Autonomous Power EXpert System (APEX), a Lisp-based power expert system, in ART-Ada. Three components, the rule-based expert systems, a graphics user interface, and communications software make up SMART-Ada (Systems fault Management with ART-Ada). The rules were written in the ART-Ada development environment and converted to Ada source code. The graphics interface was developed with the Transportable Application Environment (TAE) Plus, which generates Ada source code to control graphics images. SMART-Ada communicates with a remote host to obtain either simulated or real data. The Ada source code generated with ART-Ada, TAE Plus, and communications code was incorporated into an Ada expert system that reads the data from a power distribution test bed, applies the rule to determine a fault, if one exists, and graphically displays it on the screen. The main objective, to conduct a beta test on the ART-Ada rule-based expert system shell, was achieved. The system is operational. New Ada tools will assist in future successful projects. ART-Ada is one such tool and is a viable alternative to the straight Ada code when an application requires a rule-based or knowledge-based approach.

  9. Guidelines for visualizing and annotating rule-based models†

    PubMed Central

    Chylek, Lily A.; Hu, Bin; Blinov, Michael L.; Emonet, Thierry; Faeder, James R.; Goldstein, Byron; Gutenkunst, Ryan N.; Haugh, Jason M.; Lipniacki, Tomasz; Posner, Richard G.; Yang, Jin; Hlavacek, William S.

    2011-01-01

    Rule-based modeling provides a means to represent cell signaling systems in a way that captures site-specific details of molecular interactions. For rule-based models to be more widely understood and (re)used, conventions for model visualization and annotation are needed. We have developed the concepts of an extended contact map and a model guide for illustrating and annotating rule-based models. An extended contact map represents the scope of a model by providing an illustration of each molecule, molecular component, direct physical interaction, post-translational modification, and enzyme-substrate relationship considered in a model. A map can also illustrate allosteric effects, structural relationships among molecular components, and compartmental locations of molecules. A model guide associates elements of a contact map with annotation and elements of an underlying model, which may be fully or partially specified. A guide can also serve to document the biological knowledge upon which a model is based. We provide examples of a map and guide for a published rule-based model that characterizes early events in IgE receptor (FcεRI) signaling. We also provide examples of how to visualize a variety of processes that are common in cell signaling systems but not considered in the example model, such as ubiquitination. An extended contact map and an associated guide can document knowledge of a cell signaling system in a form that is visual as well as executable. As a tool for model annotation, a map and guide can communicate the content of a model clearly and with precision, even for large models. PMID:21647530

  10. Medical application of fuzzy logic: fuzzy patient state in arterial hypertension analysis

    NASA Astrophysics Data System (ADS)

    Blinowska, Aleksandra; Duckstein, Lucien

    1993-12-01

    A few existing applications of fuzzy logic in medicine are briefly described and some potential applications are reviewed. The problem of classification of patient states and medical decision making is discussed more in detail and illustrated by the example of a fuzzy rule based model developed to elicit, analyze and reproduce the opinions of multiple medical experts in the case of arterial hypertension. The goal was to reproduce the average coded answers using an adequate fuzzy procedure, here a fuzzy rule. State categories and the initial set of experimental parameters were defined according to medical practice. The fuzzy set membership functions were then assessed for each parameter in each category and a small subset of representative and pertinent parameters selected for each question. The data were split into two sets of 50 patient files each, the calibration set and the validation set. Two evaluation criteria were used: the sum of squared deviations and the sum of deviations. Fuzzy rules were then sought that reproduced the target, which was the average coded answer. Only one fuzzy rule `and' appeared to be necessary to describe the patient state in a continuous way and to approach the target as closely as the majority of experts.

  11. Dynamic Influence Networks for Rule-based Models.

    PubMed

    Forbes, Angus G; Burks, Andrew; Lee, Kristine; Li, Xing; Boutillier, Pierre; Krivine, Jean; Fontana, Walter

    2017-08-29

    We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rulebased models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.

  12. Kappa rule-based modeling in synthetic biology.

    PubMed

    Wilson-Kanamori, John; Danos, Vincent; Thomson, Ty; Honorato-Zimmer, Ricardo

    2015-01-01

    Rule-based modeling, an alternative to traditional reaction-based modeling, allows us to intuitively specify biological interactions while abstracting from the underlying combinatorial complexity. One such rule-based modeling formalism is Kappa, which we introduce to readers in this chapter. We discuss the application of Kappa to three modeling scenarios in synthetic biology: a unidirectional switch based on nitrosylase induction in Saccharomyces cerevisiae, the repressilator in Escherichia coli formed from BioBrick parts, and a light-mediated extension to said repressilator developed by the University of Edinburgh team during iGEM 2010. The second and third scenarios in particular form a case-based introduction to the Kappa BioBrick Framework, allowing us to systematically address the modeling of devices and circuits based on BioBrick parts in Kappa. Through the use of these examples, we highlight the ease with which Kappa can model biological interactions both at the genetic and the protein-protein interaction level, resulting in detailed stochastic models accounting naturally for transcriptional and translational resource usage. We also hope to impart the intuitively modular nature of the modeling processes involved, supported by the introduction of visual representations of Kappa models. Concluding, we explore future endeavors aimed at making modeling of synthetic biology more user-friendly and accessible, taking advantage of the strengths of rule-based modeling in Kappa.

  13. Index : A Rule Based Expert System For Computer Network Maintenance

    NASA Astrophysics Data System (ADS)

    Chaganty, Srinivas; Pitchai, Anandhi; Morgan, Thomas W.

    1988-03-01

    Communications is an expert intensive discipline. The application of expert systems for maintenance of large and complex networks, mainly as an aid in trouble shooting, can simplify the task of network management. The important steps involved in troubleshooting are fault detection, fault reporting, fault interpretation and fault isolation. At present, Network Maintenance Facilities are capable of detecting and reporting the faults to network personnel. Fault interpretation refers to the next step in the process, which involves coming up with reasons for the failure. Fault interpretation can be characterized in two ways. First, it involves such a diversity of facts that it is difficult to predict. Secondly, it embodies a wealth of knowledge in the form of network management personnel. The application of expert systems in these interpretive tasks is an important step towards automation of network maintenance. In this paper, INDEX (Intelligent Network Diagnosis Expediter), a rule based production system for computer network alarm interpretation is described. It acts as an intelligent filter for people analyzing network alarms. INDEX analyzes the alarms in the network and identifies proper maintenance action to be taken.The important feature of this production system is that it is data driven. Working memory is the principal data repository of production systems and its contents represent the current state of the problem. Control is based upon which productions match the constantly changing working memory elements. Implementation of the prototype is in OPS83. Major issues in rule based system development such as rule base organization, implementation and efficiency are discussed.

  14. A rule-based specification system for computational fluid dynamics

    NASA Astrophysics Data System (ADS)

    Luke, Edward Allen

    This study seeks to reduce the complexity and associated costs of developing computation fluid dynamics simulation software. A high level rule-based specification language is proposed as an approach to reducing the complexity of simulation software. The proposed specification language, using a mixture of declarative single-assignment semantics and domain specific mapping operators, provides a means of automatically assembling numerical simulation components. As a result of both the high level of specification and the automatic assembly process, much of the more mundane implementation issues involved in traditional Fortran based specifications are eliminated. In addition to a description of the proposed specification language, this study develops numerical simulation software for compressible flows that include finite-rate chemical kinetics. This application is used as a illustration the proposed rule-based approach in the development of complex numerical software. The numerical software is validated against several test cases using a five species chemically reacting model for air including a high temperature supersonic diffuser nozzle and a Mach 10 blunt body geometry. The performance of this application is measured and found to be competitive with a representative Fortran simulation. The growth of scheduling overhead incurred when using the rule-based approach is also measured. The results of these measurements indicate that the scheduling costs will remain small even for large simulation meshes.

  15. Fuzzy logic-based flight control system design

    NASA Astrophysics Data System (ADS)

    Nho, Kyungmoon

    The application of fuzzy logic to aircraft motion control is studied in this dissertation. The self-tuning fuzzy techniques are developed by changing input scaling factors to obtain a robust fuzzy controller over a wide range of operating conditions and nonlinearities for a nonlinear aircraft model. It is demonstrated that the properly adjusted input scaling factors can meet the required performance and robustness in a fuzzy controller. For a simple demonstration of the easy design and control capability of a fuzzy controller, a proportional-derivative (PD) fuzzy control system is compared to the conventional controller for a simple dynamical system. This thesis also describes the design principles and stability analysis of fuzzy control systems by considering the key features of a fuzzy control system including the fuzzification, rule-base and defuzzification. The wing-rock motion of slender delta wings, a linear aircraft model and the six degree of freedom nonlinear aircraft dynamics are considered to illustrate several self-tuning methods employing change in input scaling factors. Finally, this dissertation is concluded with numerical simulation of glide-slope capture in windshear demonstrating the robustness of the fuzzy logic based flight control system.

  16. LinguisticBelief: a java application for linguistic evaluation using belief, fuzzy sets, and approximate reasoning.

    SciTech Connect

    Darby, John L.

    2007-03-01

    LinguisticBelief is a Java computer code that evaluates combinations of linguistic variables using an approximate reasoning rule base. Each variable is comprised of fuzzy sets, and a rule base describes the reasoning on combinations of variables fuzzy sets. Uncertainty is considered and propagated through the rule base using the belief/plausibility measure. The mathematics of fuzzy sets, approximate reasoning, and belief/ plausibility are complex. Without an automated tool, this complexity precludes their application to all but the simplest of problems. LinguisticBelief automates the use of these techniques, allowing complex problems to be evaluated easily. LinguisticBelief can be used free of charge on any Windows XP machine. This report documents the use and structure of the LinguisticBelief code, and the deployment package for installation client machines.

  17. Fuzzy logic for personalized healthcare and diagnostics: FuzzyApp--a fuzzy logic based allergen-protein predictor.

    PubMed

    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.

  18. A fuzzy behaviorist approach to sensor-based robot control

    SciTech Connect

    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.

  19. Probabilistic and fuzzy logic in clinical diagnosis.

    PubMed

    Licata, G

    2007-06-01

    In this study I have compared classic and fuzzy logic and their usefulness in clinical diagnosis. The theory of probability is often considered a device to protect the classical two-valued logic from the evidence of its inadequacy to understand and show the complexity of world [1]. This can be true, but it is not possible to discard the theory of probability. I will argue that the problems and the application fields of the theory of probability are very different from those of fuzzy logic. After the introduction on the theoretical bases of fuzzy approach to logic, I have reported some diagnostic argumentations employing fuzzy logic. The state of normality and the state of disease often fight their battle on scalar quantities of biological values and it is not hard to establish a correspondence between the biological values and the percent values of fuzzy logic. Accordingly, I have suggested some applications of fuzzy logic in clinical diagnosis and in particular I have utilised a fuzzy curve to recognise subjects with diabetes mellitus, renal failure and liver disease. The comparison between classic and fuzzy logic findings seems to indicate that fuzzy logic is more adequate to study the development of biological events. In fact, fuzzy logic is useful when we have a lot of pieces of information and when we dispose to scalar quantities. In conclusion, increasingly the development of technology offers new instruments to measure pathological parameters through scalar quantities, thus it is reasonable to think that in the future fuzzy logic will be employed more in clinical diagnosis.

  20. On the fusion of tuning parameters of fuzzy rules and neural network

    NASA Astrophysics Data System (ADS)

    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.

  1. Fuzzy logic based robotic controller

    NASA Technical Reports Server (NTRS)

    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.

  2. Connecting clinical and actuarial prediction with rule-based methods.

    PubMed

    Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

    2015-06-01

    Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

  3. Simulation of large-scale rule-based models

    SciTech Connect

    Hlavacek, William S; Monnie, Michael I; Colvin, Joshua; Faseder, James

    2008-01-01

    Interactions of molecules, such as signaling proteins, with multiple binding sites and/or multiple sites of post-translational covalent modification can be modeled using reaction rules. Rules comprehensively, but implicitly, define the individual chemical species and reactions that molecular interactions can potentially generate. Although rules can be automatically processed to define a biochemical reaction network, the network implied by a set of rules is often too large to generate completely or to simulate using conventional procedures. To address this problem, we present DYNSTOC, a general-purpose tool for simulating rule-based models. DYNSTOC implements a null-event algorithm for simulating chemical reactions in a homogenous reaction compartment. The simulation method does not require that a reaction network be specified explicitly in advance, but rather takes advantage of the availability of the reaction rules in a rule-based specification of a network to determine if a randomly selected set of molecular components participates in a reaction during a time step. DYNSTOC reads reaction rules written in the BioNetGen language which is useful for modeling protein-protein interactions involved in signal transduction. The method of DYNSTOC is closely related to that of STOCHSIM. DYNSTOC differs from STOCHSIM by allowing for model specification in terms of BNGL, which extends the range of protein complexes that can be considered in a model. DYNSTOC enables the simulation of rule-based models that cannot be simulated by conventional methods. We demonstrate the ability of DYNSTOC to simulate models accounting for multisite phosphorylation and multivalent binding processes that are characterized by large numbers of reactions. DYNSTOC is free for non-commercial use. The C source code, supporting documentation and example input files are available at .

  4. Hierarchical graphs for rule-based modeling of biochemical systems

    PubMed Central

    2011-01-01

    Background In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Results For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR) complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm. Conclusions Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for specifying rule-based models

  5. Mapping Rule-Based And Stochastic Constraints To Connection Architectures: Implication For Hierarchical Image Processing

    NASA Astrophysics Data System (ADS)

    Miller, Michael I.; Roysam, Badrinath; Smith, Kurt R.

    1988-10-01

    Essential to the solution of ill posed problems in vision and image processing is the need to use object constraints in the reconstruction. While Bayesian methods have shown the greatest promise, a fundamental difficulty has persisted in that many of the available constraints are in the form of deterministic rules rather than as probability distributions and are thus not readily incorporated as Bayesian priors. In this paper, we propose a general method for mapping a large class of rule-based constraints to their equivalent stochastic Gibbs' distribution representation. This mapping allows us to solve stochastic estimation problems over rule-generated constraint spaces within a Bayesian framework. As part of this approach we derive a method based on Langevin's stochastic differential equation and a regularization technique based on the classical autologistic transfer function that allows us to update every site simultaneously regardless of the neighbourhood structure. This allows us to implement a completely parallel method for generating the constraint sets corresponding to the regular grammar languages on massively parallel networks. We illustrate these ideas by formulating the image reconstruction problem based on a hierarchy of rule-based and stochastic constraints, and derive a fully parallelestimator structure. We also present results computed on the AMT DAP500 massively parallel digital computer, a mesh-connected 32x32 array of processing elements which are configured in a Single-Instruction, Multiple Data stream architecture.

  6. Rule-based navigation control design for autonomous flight

    NASA Astrophysics Data System (ADS)

    Contreras, Hugo; Bassi, Danilo

    2008-04-01

    This article depicts a navigation control system design that is based on a set of rules in order to follow a desired trajectory. The full control of the aircraft considered here comprises: a low level stability control loop, based on classic PID controller and the higher level navigation whose main job is to exercise lateral control (course) and altitude control, trying to follow a desired trajectory. The rules and PID gains were adjusted systematically according to the result of flight simulation. In spite of its simplicity, the rule-based navigation control proved to be robust, even with big perturbation, like crossing winds.

  7. An Application of Fuzzy Logic Control to a Classical Military Tracking Problem

    DTIC Science & Technology

    1994-05-19

    34Probability Measures of Fuzzy Events", Journal of Mathematical Analysis and Applications , vol.23, 1968, p.421. 9. Kosko, Bart. "Fuzziness Versus...January 1973, pp.28-44. Zadeh, L.A. "Probability Measures of Fuzzy Events", Journal of Mathematical Analysis and Applications , vol.23, 1968, pp.421

  8. Design and implementation of fuzzy logic controllers. Thesis Final Report, 27 Jul. 1992 - 1 Jan. 1993

    NASA Technical Reports Server (NTRS)

    Abihana, Osama A.; Gonzalez, Oscar R.

    1993-01-01

    The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.

  9. Rule-based expert system for maritime anomaly detection

    NASA Astrophysics Data System (ADS)

    Roy, Jean

    2010-04-01

    Maritime domain operators/analysts have a mandate to be aware of all that is happening within their areas of responsibility. This mandate derives from the needs to defend sovereignty, protect infrastructures, counter terrorism, detect illegal activities, etc., and it has become more challenging in the past decade, as commercial shipping turned into a potential threat. In particular, a huge portion of the data and information made available to the operators/analysts is mundane, from maritime platforms going about normal, legitimate activities, and it is very challenging for them to detect and identify the non-mundane. To achieve such anomaly detection, they must establish numerous relevant situational facts from a variety of sensor data streams. Unfortunately, many of the facts of interest just cannot be observed; the operators/analysts thus use their knowledge of the maritime domain and their reasoning faculties to infer these facts. As they are often overwhelmed by the large amount of data and information, automated reasoning tools could be used to support them by inferring the necessary facts, ultimately providing indications and warning on a small number of anomalous events worthy of their attention. Along this line of thought, this paper describes a proof-of-concept prototype of a rule-based expert system implementing automated rule-based reasoning in support of maritime anomaly detection.

  10. Rule-based semantic web services matching strategy

    NASA Astrophysics Data System (ADS)

    Fan, Hong; Wang, Zhihua

    2011-12-01

    With the development of Web services technology, the number of service increases rapidly, and it becomes a challenge task that how to efficiently discovery the services that exactly match the user's requirements from the large scale of services library. Many semantic Web services discovery technologies proposed by the recent literatures only focus on the keyword-based or primary semantic based service's matching. This paper studies the rules and rule reasoning based service matching algorithm in the background of large scale services library. Firstly, the formal descriptions of semantic web services and service matching is presented. The services' matching are divided into four levels: Exact, Plugin, Subsume and Fail and their formal descriptions are also presented. Then, the service matching is regarded as rule-based reasoning issues. A set of match rules are firstly given and the related services set is retrieved from services ontology base through rule-based reasoning, and their matching levels are determined by distinguishing the relationships between service's I/O and user's request I/O. Finally, the experiment based on two services sets show that the proposed services matching strategy can easily implement the smart service discovery and obtains the high service discovery efficiency in comparison with the traditional global traversal strategy.

  11. Prediction of Conductivity by Adaptive Neuro-Fuzzy Model

    PubMed Central

    Akbarzadeh, S.; Arof, A. K.; Ramesh, S.; Khanmirzaei, M. H.; Nor, R. M.

    2014-01-01

    Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582

  12. Fuzzy jets

    NASA Astrophysics Data System (ADS)

    Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; Stansbury, Conrad

    2016-06-01

    Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.

  13. Fuzzy jets

    DOE PAGES

    Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; ...

    2016-06-01

    Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets . To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets , are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variablesmore » in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.« less

  14. Fuzzy jets

    SciTech Connect

    Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; Stansbury, Conrad

    2016-06-01

    Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets . To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets , are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.

  15. Robust adaptive self-structuring fuzzy control design for nonaffine, nonlinear systems

    NASA Astrophysics Data System (ADS)

    Chen, Pin-Cheng; Wang, Chi-Hsu; Lee, Tsu-Tian

    2011-01-01

    In this article, a robust adaptive self-structuring fuzzy control (RASFC) scheme for the uncertain or ill-defined nonlinear, nonaffine systems is proposed. The RASFC scheme is composed of a robust adaptive controller and a self-structuring fuzzy controller. In the self-structuring fuzzy controller design, a novel self-structuring fuzzy system (SFS) is used to approximate the unknown plant nonlinearity, and the SFS can automatically grow and prune fuzzy rules to realise a compact fuzzy rule base. The robust adaptive controller is designed to achieve an L 2 tracking performance to stabilise the closed-loop system. This L 2 tracking performance can provide a clear expression of tracking error in terms of the sum of lumped uncertainty and external disturbance, which has not been shown in previous works. Finally, five examples are presented to show that the proposed RASFC scheme can achieve favourable tracking performance, yet heavy computational burden is relieved.

  16. Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach

    NASA Astrophysics Data System (ADS)

    Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

    2016-06-01

    The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.

  17. Automatic generation of fuzzy rules for the sensor-based navigation of a mobile robot

    SciTech Connect

    Pin, F.G.; Watanabe, Y.

    1994-10-01

    A system for automatic generation of fuzzy rules is proposed which is based on a new approach, called {open_quotes}Fuzzy Behaviorist,{close_quotes} and on its associated formalism for rule base development in behavior-based robot control systems. The automated generator of fuzzy rules automatically constructs the set of rules and the associated membership functions that implement reasoning schemes that have been expressed in qualitative terms. The system also checks for completeness of the rule base and independence and/or redundancy of the rules to ensure that the requirements of the formalism are satisfied. Examples of the automatic generation of fuzzy rules for cases involving suppression and/or inhibition of fuzzy behaviors are given and discussed. Experimental results obtained with the automated fuzzy rule generator applied to the domain of sensor-based navigation in a priori unknown environments using one of our autonomous test-bed robots are then presented and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using our proposed {open_quotes}Fuzzy Behaviorist{close_quotes} approach.

  18. Sensor-based navigation of a mobile robot using automatically constructed fuzzy rules

    SciTech Connect

    Watanabe, Y.; Pin, F.G.

    1993-10-01

    A system for automatic generation of fuzzy rules is proposed which is based on a new approach, called ``Fuzzy Behaviorist,`` and on its associated formalism for rule base development in behavior-based robot control systems. The automated generator of fuzzy rules automatically constructs the set of rules and the associated membership functions that implement reasoning schemes that have been expressed in qualitative terms. The system also checks for completeness of the rule base and independence and/or redundancy of the rules to ensure that the requirements of the formalism are satisfied. Examples of the automatic generation of fuzzy rules for cases involving suppression and/or inhibition of fuzzy behaviors are given and discussed. Experimental results obtained with the automated fuzzy rule generator applied to the domain of sensor-based navigation in a priori unknown environments using one of our autonomous test-bed robots are then presented and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using our proposed ``Fuzzy Behaviorist`` approach.

  19. Rule-based category learning in Down syndrome.

    PubMed

    Phillips, B Allyson; Conners, Frances A; Merrill, Edward; Klinger, Mark R

    2014-05-01

    Rule-based category learning was examined in youths with Down syndrome (DS), youths with intellectual disability (ID), and typically developing (TD) youths. Two tasks measured category learning: the Modified Card Sort task (MCST) and the Concept Formation test of the Woodcock-Johnson-III ( Woodock, McGrew, & Mather, 2001 ). In regression-based analyses, DS and ID groups performed below the level expected for their nonverbal ability. In cross-sectional developmental trajectory analyses, results depended on the task. On the MCST, the DS and ID groups were similar to the TD group. On the Concept Formation test, the DS group had slower cross-sectional change than the other 2 groups. Category learning may be an area of difficulty for those with ID, but task-related factors may affect trajectories for youths with DS.

  20. Rule based design of conceptual models for formative evaluation

    NASA Technical Reports Server (NTRS)

    Moore, Loretta A.; Chang, Kai; Hale, Joseph P.; Bester, Terri; Rix, Thomas; Wang, Yaowen

    1994-01-01

    A Human-Computer Interface (HCI) Prototyping Environment with embedded evaluation capability has been investigated. This environment will be valuable in developing and refining HCI standards and evaluating program/project interface development, especially Space Station Freedom on-board displays for payload operations. This environment, which allows for rapid prototyping and evaluation of graphical interfaces, includes the following four components: (1) a HCI development tool; (2) a low fidelity simulator development tool; (3) a dynamic, interactive interface between the HCI and the simulator; and (4) an embedded evaluator that evaluates the adequacy of a HCI based on a user's performance. The embedded evaluation tool collects data while the user is interacting with the system and evaluates the adequacy of an interface based on a user's performance. This paper describes the design of conceptual models for the embedded evaluation system using a rule-based approach.

  1. Rule-based extrapolation: a continuing challenge for exemplar models.

    PubMed

    Denton, Stephen E; Kruschke, John K; Erickson, Michael A

    2008-08-01

    Erickson and Kruschke (1998, 2002) demonstrated that in rule-plus-exception categorization, people generalize category knowledge by extrapolating in a rule-like fashion, even when they are presented with a novel stimulus that is most similar to a known exception. Although exemplar models have been found to be deficient in explaining rule-based extrapolation, Rodrigues and Murre (2007) offered a variation of an exemplar model that was better able to account for such performance. Here, we present the results of a new rule-plus-exception experiment that yields rule-like extrapolation similar to that of previous experiments, and yet the data are not accounted for by Rodrigues and Murre's augmented exemplar model. Further, a hybrid rule-and-exemplar model is shown to better describe the data. Thus, we maintain that rule-plus-exception categorization continues to be a challenge for exemplar-only models.

  2. Approaches to the verification of rule-based expert systems

    NASA Technical Reports Server (NTRS)

    Culbert, Chris; Riley, Gary; Savely, Robert T.

    1987-01-01

    Expert systems are a highly useful spinoff of artificial intelligence research. One major stumbling block to extended use of expert systems is the lack of well-defined verification and validation (V and V) methodologies. Since expert systems are computer programs, the definitions of verification and validation from conventional software are applicable. The primary difficulty with expert systems is the use of development methodologies which do not support effective V and V. If proper techniques are used to document requirements, V and V of rule-based expert systems is possible, and may be easier than with conventional code. For NASA applications, the flight technique panels used in previous programs should provide an excellent way to verify the rules used in expert systems. There are, however, some inherent differences in expert systems that will affect V and V considerations.

  3. Ontology and rules based model for traffic query

    NASA Astrophysics Data System (ADS)

    Cheng, Gang; Du, Qingyun; Huang, Qian; Zhao, Haiyun

    2008-10-01

    This paper will combine ontology and rule based qualitative reason with real time calculation, designing a combined traffic model of national scope which contains highway, railroad, water carriage, scheduled flight etc. That method follows the sense of people to space, establishes ontologies and rules knowledge base, using concepts, instances, relations and rules of traffic field as the basic knowledge for qualitative reason to discover implicit semantic information and eliminate unnecessary ambiguities. The knowledge from the ontologies and rules provides abundant information for query which can lighten the burden of computation, in the mean time, real-time calculation guarantees the accuracy of the data, has raised accuracy and efficiency of the query, which has strengthened the ease of query service and improved web users' experience.

  4. A High-Level Language for Rule-Based Modelling

    PubMed Central

    Pedersen, Michael; Phillips, Andrew; Plotkin, Gordon D.

    2015-01-01

    Rule-based languages such as Kappa excel in their support for handling the combinatorial complexities prevalent in many biological systems, including signalling pathways. But Kappa provides little structure for organising rules, and large models can therefore be hard to read and maintain. This paper introduces a high-level, modular extension of Kappa called LBS-κ. We demonstrate the constructs of the language through examples and three case studies: a chemotaxis switch ring, a MAPK cascade, and an insulin signalling pathway. We then provide a formal definition of LBS-κ through an abstract syntax and a translation to plain Kappa. The translation is implemented in a compiler tool which is available as a web application. We finally demonstrate how to increase the expressivity of LBS-κ through embedded scripts in a general-purpose programming language, a technique which we view as generally applicable to other domain specific languages. PMID:26043208

  5. Rule based design of conceptual models for formative evaluation

    NASA Technical Reports Server (NTRS)

    Moore, Loretta A.; Chang, Kai; Hale, Joseph P.; Bester, Terri; Rix, Thomas; Wang, Yaowen

    1994-01-01

    A Human-Computer Interface (HCI) Prototyping Environment with embedded evaluation capability has been investigated. This environment will be valuable in developing and refining HCI standards and evaluating program/project interface development, especially Space Station Freedom on-board displays for payload operations. This environment, which allows for rapid prototyping and evaluation of graphical interfaces, includes the following four components: (1) a HCI development tool, (2) a low fidelity simulator development tool, (3) a dynamic, interactive interface between the HCI and the simulator, and (4) an embedded evaluator that evaluates the adequacy of a HCI based on a user's performance. The embedded evaluation tool collects data while the user is interacting with the system and evaluates the adequacy of an interface based on a user's performance. This paper describes the design of conceptual models for the embedded evaluation system using a rule-based approach.

  6. A Rule-Based Industrial Boiler Selection System

    NASA Astrophysics Data System (ADS)

    Tan, C. F.; Khalil, S. N.; Karjanto, J.; Tee, B. T.; Wahidin, L. S.; Chen, W.; Rauterberg, G. W. M.; Sivarao, S.; Lim, T. L.

    2015-09-01

    Boiler is a device used for generating the steam for power generation, process use or heating, and hot water for heating purposes. Steam boiler consists of the containing vessel and convection heating surfaces only, whereas a steam generator covers the whole unit, encompassing water wall tubes, super heaters, air heaters and economizers. The selection of the boiler is very important to the industry for conducting the operation system successfully. The selection criteria are based on rule based expert system and multi-criteria weighted average method. The developed system consists of Knowledge Acquisition Module, Boiler Selection Module, User Interface Module and Help Module. The system capable of selecting the suitable boiler based on criteria weighted. The main benefits from using the system is to reduce the complexity in the decision making for selecting the most appropriate boiler to palm oil process plant.

  7. A high-level language for rule-based modelling.

    PubMed

    Pedersen, Michael; Phillips, Andrew; Plotkin, Gordon D

    2015-01-01

    Rule-based languages such as Kappa excel in their support for handling the combinatorial complexities prevalent in many biological systems, including signalling pathways. But Kappa provides little structure for organising rules, and large models can therefore be hard to read and maintain. This paper introduces a high-level, modular extension of Kappa called LBS-κ. We demonstrate the constructs of the language through examples and three case studies: a chemotaxis switch ring, a MAPK cascade, and an insulin signalling pathway. We then provide a formal definition of LBS-κ through an abstract syntax and a translation to plain Kappa. The translation is implemented in a compiler tool which is available as a web application. We finally demonstrate how to increase the expressivity of LBS-κ through embedded scripts in a general-purpose programming language, a technique which we view as generally applicable to other domain specific languages.

  8. Rule-Based Orientation Recognition Of A Moving Object

    NASA Astrophysics Data System (ADS)

    Gove, Robert J.

    1989-03-01

    This paper presents a detailed description and a comparative analysis of the algorithms used to determine the position and orientation of an object in real-time. The exemplary object, a freely moving gold-fish in an aquarium, provides "real-world" motion, with definable characteristics of motion (the fish never swims upside-down) and the complexities of a non-rigid body. For simplicity of implementation, and since a restricted and stationary viewing domain exists (fish-tank), we reduced the problem of obtaining 3D correspondence information to trivial alignment calculations by using two cameras orthogonally viewing the object. We applied symbolic processing techniques to recognize the 3-D orientation of a moving object of known identity in real-time. Assuming motion, each new frame (sensed by the two cameras) provides images of the object's profile which has most likely undergone translation, rotation, scaling and/or bending of the non-rigid object since the previous frame. We developed an expert system which uses heuristics of the object's motion behavior in the form of rules and information obtained via low-level image processing (like numerical inertial axis calculations) to dynamically estimate the object's orientation. An inference engine provides these estimates at frame rates of up to 10 per second (which is essentially real-time). The advantages of the rule-based approach to orientation recognition will be compared other pattern recognition techniques. Our results of an investigation of statistical pattern recognition, neural networks, and procedural techniques for orientation recognition will be included. We implemented the algorithms in a rapid-prototyping environment, the TI-Ezplorer, equipped with an Odyssey and custom imaging hardware. A brief overview of the workstation is included to clarify one motivation for our choice of algorithms. These algorithms exploit two facets of the prototype image processing and understanding workstation - both low

  9. Timescale analysis of rule-based biochemical reaction networks

    PubMed Central

    Klinke, David J.; Finley, Stacey D.

    2012-01-01

    The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150

  10. A fuzzy-based approach for open-transistor fault diagnosis in voltage-source inverter induction motor drives

    NASA Astrophysics Data System (ADS)

    Zhang, Jianghan; Luo, Hui; Zhao, Jin; Wu, Feng

    2015-02-01

    This paper develops a novel method for the detection and isolation of open-transistor faults in voltage-source inverters feeding induction motors. Based on analyzing the load currents trajectories after Concordia transformation, six diagnostic signals each of which indicates a certain switch are extracted and a fuzzy rule base is designed to perform fuzzy reasoning in order to detect and isolate 21 fault modes including single- and double-transistor faults. In addition, the fuzzy rules are rearranged and each of them is set to a reasonable value representing the fault modes. The simulation and experiment are carried out to demonstrate the effectiveness of the proposed fuzzy approach.

  11. Fuzzy modelling of HEART methodology

    NASA Astrophysics Data System (ADS)

    Casamirra, M.; Castiglia, F.; Giardina, M.; Tomarchio, E.

    This study refers to the results obtained by Fuzzy Fault Tree analyses of accidental scenarios that entail the potential exposure of operators working in irradiation industrial plants. For these analyses, the HEART methodology, a first generation of the Human Reliability Analysis method, has been employed to evaluate the probability of human erroneous actions. This technique has been modified on the basis of a fuzzy set concept to more directly take into account the uncertainties of the so-called error-promoting factors, on which the method is grounded. The results also allow us to provide some recommendations on procedures and safety equipment to reduce the risk of radiological exposure.

  12. Cayley Bipolar Fuzzy Graphs

    PubMed Central

    Alshehri, Noura O.

    2013-01-01

    We introduce the concept of Cayley bipolar fuzzy graphs and investigate some of their properties. We present some interesting properties of bipolar fuzzy graphs in terms of algebraic structures. We also discuss connectedness in Cayley bipolar fuzzy graphs. PMID:24453797

  13. Seizure detection in intracranial EEG using a fuzzy inference system.

    PubMed

    Aarabi, A; Fazel-Rezai, R; Aghakhani, Y

    2009-01-01

    In this paper, we present a fuzzy rule-based system for the automatic detection of seizures in the intracranial EEG (IEEG) recordings. A total of 302.7 hours of the IEEG with 78 seizures, recorded from 21 patients aged between 10 and 47 years were used for the evaluation of the system. After preprocessing, temporal, spectral, and complexity features were extracted from the segmented IEEGs. The results were thresholded using the statistics of a reference window and integrated spatio-temporally using a fuzzy rule-based decision making system. The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11 s. The results from the automatic system correlate well with the visual analysis of the seizures by the expert. This system may serve as a good seizure detection tool for monitoring long-term IEEG with relatively high sensitivity and low false detection rate.

  14. Intuitionistic Fuzzy Cycles and Intuitionistic Fuzzy Trees

    PubMed Central

    Alshehri, N. O.

    2014-01-01

    Connectivity has an important role in neural networks, computer network, and clustering. In the design of a network, it is important to analyze connections by the levels. The structural properties of intuitionistic fuzzy graphs provide a tool that allows for the solution of operations research problems. In this paper, we introduce various types of intuitionistic fuzzy bridges, intuitionistic fuzzy cut vertices, intuitionistic fuzzy cycles, and intuitionistic fuzzy trees in intuitionistic fuzzy graphs and investigate some of their interesting properties. Most of these various types are defined in terms of levels. We also describe comparison of these types. PMID:24701155

  15. Intuitionistic fuzzy segmentation of medical images.

    PubMed

    Chaira, Tamalika

    2010-06-01

    This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy set theory to segment blood vessels and also the blood cells in pathological images. This type of segmentation is very important in detecting different types of human diseases, e.g., an increase in the number of vessels may lead to cancer in prostates, mammary, etc. The medical images are not properly illuminated, and segmentation in that case becomes very difficult. A novel image segmentation approach using intuitionistic fuzzy set theory and a new membership function is proposed using restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. Local thresholding is applied to threshold medical images. The results showed a much better performance on poor contrast medical images, where almost all the blood vessels and blood cells are visible properly. There are several fuzzy and intuitionistic fuzzy thresholding methods, but these methods are not related to the medical images. To make a comparison with the proposed method with other thresholding methods, the method is compared with six nonfuzzy, fuzzy, and intuitionistic fuzzy methods.

  16. Imprecise (fuzzy) information in geostatistics

    SciTech Connect

    Bardossy, A.; Bogardi, I.; Kelly, W.E.

    1988-05-01

    A methodology based on fuzzy set theory for the utilization of imprecise data in geostatistics is presented. A common problem preventing a broader use of geostatistics has been the insufficient amount of accurate measurement data. In certain cases, additional but uncertain (soft) information is available and can be encoded as subjective probabilities, and then the soft kriging method can be applied (Journal, 1986). In other cases, a fuzzy encoding of soft information may be more realistic and simplify the numerical calculations. Imprecise (fuzzy) spatial information on the possible variogram is integrated into a single variogram which is used in a fuzzy kriging procedure. The overall uncertainty of prediction is represented by the estimation variance and the calculated membership function for each kriged point. The methodology is applied to the permeability prediction of a soil liner for hazardous waste containment. The available number of hard measurement data (20) was not enough for a classical geostatistical analysis. An additional 20 soft data made it possible to prepare kriged contour maps using the fuzzy geostatistical procedure.

  17. A Rule-based Track Anomaly Detection Algorithm for Maritime Force Protection

    DTIC Science & Technology

    2014-08-01

    UNCLASSIFIED UNCLASSIFIED A Rule- based Track Anomaly Detection Algorithm for Maritime Force Protection S.Boinepalli and...detection tool using a Rule- based Algorithm that can detect anomalies in a set of pre-recorded tracks using their curvature, speed and weave. We...Australia 2014 AR 016-049 August 2014 APPROVED FOR PUBLIC RELEASE UNCLASSIFIED UNCLASSIFIED A Rule- based Track Anomaly Detection

  18. Rule-based deduplication of article records from bibliographic databases.

    PubMed

    Jiang, Yu; Lin, Can; Meng, Weiyi; Yu, Clement; Cohen, Aaron M; Smalheiser, Neil R

    2014-01-01

    We recently designed and deployed a metasearch engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments.

  19. A Rules-Based Simulation of Bacterial Turbulence

    NASA Astrophysics Data System (ADS)

    Mikel-Stites, Maxwell; Staples, Anne

    2015-11-01

    In sufficiently dense bacterial populations (>40% bacteria by volume), unusual collective swimming behaviors have been consistently observed, resembling von Karman vortex streets. The source of these collective swimming behavior has yet to be fully determined, and as of yet, no research has been conducted that would define whether or not this behavior is derived predominantly from the properties of the surrounding media, or if it is an emergent behavior as a result of the ``rules'' governing the behavior of individual bacteria. The goal of this research is to ascertain whether or not it is possible to design a simulation that can replicate the qualitative behavior of the densely packed bacterial populations using only behavioral rules to govern the actions of each bacteria, with the physical properties of the media being neglected. The results of the simulation will address whether or not it is possible for the system's overall behavior to be driven exclusively by these rule-based dynamics. In order to examine this, the behavioral simulation was written in MATLAB on a fixed grid, and updated sequentially with the bacterial behavior, including randomized tumbling, gathering and perceptual sub-functions. If the simulation is successful, it will serve as confirmation that it is possible to generate these qualitatively vortex-like behaviors without specific physical media (that the phenomena arises in emergent fashion from behavioral rules), or as evidence that the observed behavior requires some specific set of physical parameters.

  20. Rule-based deduplication of article records from bibliographic databases

    PubMed Central

    Jiang, Yu; Lin, Can; Meng, Weiyi; Yu, Clement; Cohen, Aaron M.; Smalheiser, Neil R.

    2014-01-01

    We recently designed and deployed a metasearch engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments. PMID:24434031

  1. Rule extraction with fuzzy neural network.

    PubMed

    D'Alché-Buc, F; Andrès, V; Nadal, J P

    1994-03-01

    This paper deals with the learning of understandable decision rules with connectionist systems. Our approach consists of extracting fuzzy control rules with a new fuzzy neural network. Whereas many other works on this area propose to use combinations of nonlinear neurons to approximate fuzzy operations, we use a fuzzy neuron that computes max-min operations. Thus, this neuron can be interpreted as a possibility estimator, just as sigma-pi neurons can support a probabilistic interpretation. Within this context, possibilistic inferences can be drawn through the multi-layered network, using a distributed representation of the information. A new learning procedure has been developed in order that each part of the network can be learnt sequentially, while other parts are frozen. Each step of the procedure is based on the same kind of learning scheme: the backpropagation of a well-chosen cost function with appropriate derivatives of max-min function. An appealing result of the learning phase is the ability of the network to automatically reduce the number of the condition-parts of the rules, if needed. The network has been successfully tested on the learning of a control rule base for an inverted pendulum.

  2. An analytical fuzzy-based approach to ?-gain optimal control of input-affine nonlinear systems using Newton-type algorithm

    NASA Astrophysics Data System (ADS)

    Milic, Vladimir; Kasac, Josip; Novakovic, Branko

    2015-10-01

    This paper is concerned with ?-gain optimisation of input-affine nonlinear systems controlled by analytic fuzzy logic system. Unlike the conventional fuzzy-based strategies, the non-conventional analytic fuzzy control method does not require an explicit fuzzy rule base. As the first contribution of this paper, we prove, by using the Stone-Weierstrass theorem, that the proposed fuzzy system without rule base is universal approximator. The second contribution of this paper is an algorithm for solving a finite-horizon minimax problem for ?-gain optimisation. The proposed algorithm consists of recursive chain rule for first- and second-order derivatives, Newton's method, multi-step Adams method and automatic differentiation. Finally, the results of this paper are evaluated on a second-order nonlinear system.

  3. Fuzzy controllers and fuzzy expert systems: industrial applications of fuzzy technology

    NASA Astrophysics Data System (ADS)

    Bonissone, Piero P.

    1995-06-01

    We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.

  4. Individual Differences in Base Rate Neglect: A Fuzzy Processing Preference Index

    ERIC Educational Resources Information Center

    Wolfe, Christopher R.; Fisher, Christopher R.

    2013-01-01

    Little is known about individual differences in integrating numeric base-rates and qualitative text in making probability judgments. Fuzzy-Trace Theory predicts a preference for fuzzy processing. We conducted six studies to develop the FPPI, a reliable and valid instrument assessing individual differences in this fuzzy processing preference. It…

  5. Individual Differences in Base Rate Neglect: A Fuzzy Processing Preference Index

    ERIC Educational Resources Information Center

    Wolfe, Christopher R.; Fisher, Christopher R.

    2013-01-01

    Little is known about individual differences in integrating numeric base-rates and qualitative text in making probability judgments. Fuzzy-Trace Theory predicts a preference for fuzzy processing. We conducted six studies to develop the FPPI, a reliable and valid instrument assessing individual differences in this fuzzy processing preference. It…

  6. Computer vision for general purpose visual inspection: a fuzzy logic approach

    NASA Astrophysics Data System (ADS)

    Chen, Y. H.

    In automatic visual industrial inspection, computer vision systems have been widely used. Such systems are often application specific, and therefore require domain knowledge in order to have a successful implementation. Since visual inspection can be viewed as a decision making process, it is argued that the integration of fuzzy logic analysis and computer vision systems provides a practical approach to general purpose visual inspection applications. This paper describes the development of an integrated fuzzy-rule-based automatic visual inspection system. Domain knowledge about a particular application is represented as a set of fuzzy rules. From the status of predefined fuzzy variables, the set of fuzzy rules are defuzzified to give the inspection results. A practical application where IC marks (often in the forms of English characters and a company logo) inspection is demonstrated, which shows a more consistent result as compared to a conventional thresholding method.

  7. Application of fuzzy GA for optimal vibration control of smart cylindrical shells

    NASA Astrophysics Data System (ADS)

    Jin, Zhanli; Yang, Yaowen; Kiong Soh, Chee

    2005-12-01

    In this paper, a fuzzy-controlled genetic-based optimization technique for optimal vibration control of cylindrical shell structures incorporating piezoelectric sensor/actuators (S/As) is proposed. The geometric design variables of the piezoelectric patches, including the placement and sizing of the piezoelectric S/As, are processed using fuzzy set theory. The criterion based on the maximization of energy dissipation is adopted for the geometric optimization. A fuzzy-rule-based system (FRBS) representing expert knowledge and experience is incorporated in a modified genetic algorithm (GA) to control its search process. A fuzzy logic integrated GA is then developed and implemented. The results of three numerical examples, which include a simply supported plate, a simply supported cylindrical shell, and a clamped simply supported plate, provide some meaningful and heuristic conclusions for practical design. The results also show that the proposed fuzzy-controlled GA approach is more effective and efficient than the pure GA method.

  8. A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

    NASA Astrophysics Data System (ADS)

    Ozkop, Emre; Altas, Ismail H.; Okumus, H. Ibrahim; Sharaf, Adel M.

    2015-11-01

    In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional-integral-derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.

  9. Fuzzy associative memories

    NASA Technical Reports Server (NTRS)

    Kosko, Bart

    1991-01-01

    Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.

  10. A fuzzy approach for modelling radionuclide in lake system.

    PubMed

    Desai, H K; Christian, R A; Banerjee, J; Patra, A K

    2013-10-01

    Radioactive liquid waste is generated during operation and maintenance of Pressurised Heavy Water Reactors (PHWRs). Generally low level liquid waste is diluted and then discharged into the near by water-body through blowdown water discharge line as per the standard waste management practice. The effluents from nuclear installations are treated adequately and then released in a controlled manner under strict compliance of discharge criteria. An attempt was made to predict the concentration of (3)H released from Kakrapar Atomic Power Station at Ratania Regulator, about 2.5 km away from the discharge point, where human exposure is expected. Scarcity of data and complex geometry of the lake prompted the use of Heuristic approach. Under this condition, Fuzzy rule based approach was adopted to develop a model, which could predict (3)H concentration at Ratania Regulator. Three hundred data were generated for developing the fuzzy rules, in which input parameters were water flow from lake and (3)H concentration at discharge point. The Output was (3)H concentration at Ratania Regulator. These data points were generated by multiple regression analysis of the original data. Again by using same methodology hundred data were generated for the validation of the model, which were compared against the predicted output generated by using Fuzzy Rule based approach. Root Mean Square Error of the model came out to be 1.95, which showed good agreement by Fuzzy model of natural ecosystem.

  11. A fuzzy neural network for intelligent data processing

    NASA Astrophysics Data System (ADS)

    Xie, Wei; Chu, Feng; Wang, Lipo; Lim, Eng Thiam

    2005-03-01

    In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.

  12. Diagnosis of arthritis through fuzzy inference system.

    PubMed

    Singh, Sachidanand; Kumar, Atul; Panneerselvam, K; Vennila, J Jannet

    2012-06-01

    Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process.

  13. The analyses of dynamic response and reliability of fuzzy-random truss under stationary stochastic excitation

    NASA Astrophysics Data System (ADS)

    Ma, Juan; Gao, Wei; Wriggers, Peter; Wu, Tao; Sahraee, Shahab

    2010-04-01

    A new two-factor method based on the probability and the fuzzy sets theory is used for the analyses of the dynamic response and reliability of fuzzy-random truss systems under the stationary stochastic excitation. Considering the fuzzy-randomness of the structural physical parameters and geometric dimensions simultaneously, the fuzzy-random correlation function matrix of structural displacement response in time domain and the fuzzy-random mean square values of structural dynamic response in frequency domain are developed by using the two-factor method, and the fuzzy numerical characteristics of dynamic responses are then derived. Based on numerical characteristics of structural fuzzy-random dynamic responses, the structural fuzzy-random dynamic reliability and its fuzzy numerical characteristic are obtained from the Poisson equation. The effects of the uncertainty of the structural parameters on structural dynamic response and reliability are illustrated via two engineering examples and some important conclusions are obtained.

  14. Universal Approximation of Mamdani Fuzzy Controllers and Fuzzy Logical Controllers

    NASA Technical Reports Server (NTRS)

    Yuan, Bo; Klir, George J.

    1997-01-01

    In this paper, we first distinguish two types of fuzzy controllers, Mamdani fuzzy controllers and fuzzy logical controllers. Mamdani fuzzy controllers are based on the idea of interpolation while fuzzy logical controllers are based on fuzzy logic in its narrow sense, i.e., fuzzy propositional logic. The two types of fuzzy controllers treat IF-THEN rules differently. In Mamdani fuzzy controllers, rules are treated disjunctively. In fuzzy logic controllers, rules are treated conjunctively. Finally, we provide a unified proof of the property of universal approximation for both types of fuzzy controllers.

  15. Assessment of safety and health in the tea industry of Barak valley, Assam: a fuzzy logic approach.

    PubMed

    Gupta, Rajat; Dey, Sanjoy Kumar

    2013-01-01

    Traditional safety and health system measurement procedures, practiced in various industries produce qualitative results with a degree of uncertainty. This paper presents a fuzzy-logic-based approach to developing a fuzzy model for assessing the safety and health status in the tea industry. For this, the overall safety and health status at a tea estate has been considered as a function of 4 inputs: occupational safety, occupational health, behavioral safety and competency. A set of fuzzy rules based on expert human judgment has been used to correlate different fuzzy inputs and output. Fuzzy set operations are used to calculate the safety and health status of the tea industry. Application of the developed model at a tea estate showed that the safety and health status belongs to the fuzzy class of good with a crisp value of 7.2.

  16. Fuzzy lattice neurocomputing (FLN) models.

    PubMed

    Kaburlasos, V G; Petridis, V

    2000-12-01

    In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the capacity to deal rigorously with: disparate types of data such as numeric and linguistic data, intervals of values, 'missing' and 'don't care' data. A novel notation for the FL-framework is introduced here in order to simplify mathematical expressions without losing content. Two concrete FLN models are presented, namely 'sigma-FLN' for competitive clustering, and 'FLN with tightest fits (FLNtf)' for supervised clustering. Learning by the sigma-FLN, is rapid as it requires a single pass through the data, whereas learning by the FLNtf, is incremental, data order independent, polynomial theta(n3), and it guarantees maximization of the degree of inclusion of an input in a learned class as explained in the text. Convenient geometric interpretations are provided. The sigma-FLN is presented here as fuzzy-ART's extension in the FL-framework such that sigma-FLN widens fuzzy-ART's domain of application to (mathematical) lattices by augmenting the scope of both of fuzzy-ART's choice (Weber) and match functions, and by enhancing fuzzy-ART's complement coding technique. The FLNtf neural model is applied to four benchmark data sets of various sizes for pattern recognition and rule extraction. The benchmark data sets in question involve jointly numeric and nominal data with 'missing' and/or 'don't care' attribute values, whereas the lattices involved include the unit-hypercube, a probability space, and a Boolean algebra. The potential of the FL-framework in computing is also delineated.

  17. Experiments on neural network architectures for fuzzy logic

    NASA Technical Reports Server (NTRS)

    Keller, James M.

    1991-01-01

    The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.

  18. Rule-Based and Information-Integration Category Learning in Normal Aging

    ERIC Educational Resources Information Center

    Maddox, W. Todd; Pacheco, Jennifer; Reeves, Maia; Zhu, Bo; Schnyer, David M.

    2010-01-01

    The basal ganglia and prefrontal cortex play critical roles in category learning. Both regions evidence age-related structural and functional declines. The current study examined rule-based and information-integration category learning in a group of older and younger adults. Rule-based learning is thought to involve explicit, frontally mediated…

  19. Fuzzy probabilistic design of water distribution networks

    NASA Astrophysics Data System (ADS)

    Fu, Guangtao; Kapelan, Zoran

    2011-05-01

    The primary aim of this paper is to present a fuzzy probabilistic approach for optimal design and rehabilitation of water distribution systems, combining aleatoric and epistemic uncertainties in a unified framework. The randomness and imprecision in future water consumption are characterized using fuzzy random variables whose realizations are not real but fuzzy numbers, and the nodal head requirements are represented by fuzzy sets, reflecting the imprecision in customers' requirements. The optimal design problem is formulated as a two-objective optimization problem, with minimization of total design cost and maximization of system performance as objectives. The system performance is measured by the fuzzy random reliability, defined as the probability that the fuzzy head requirements are satisfied across all network nodes. The satisfactory degree is represented by necessity measure or belief measure in the sense of the Dempster-Shafer theory of evidence. An efficient algorithm is proposed, within a Monte Carlo procedure, to calculate the fuzzy random system reliability and is effectively combined with the nondominated sorting genetic algorithm II (NSGAII) to derive the Pareto optimal design solutions. The newly proposed methodology is demonstrated with two case studies: the New York tunnels network and Hanoi network. The results from both cases indicate that the new methodology can effectively accommodate and handle various aleatoric and epistemic uncertainty sources arising from the design process and can provide optimal design solutions that are not only cost-effective but also have higher reliability to cope with severe future uncertainties.

  20. Fuzzy power control algorithm for a pressurized water reactor

    SciTech Connect

    Hah, Y.J. ); Lee, B.W. )

    1994-05-01

    A fuzzy power control algorithm is presented for automatic reactor power control in a pressurized water reactor (PWR). Automatic power shape control is complicated by the use of control rods with a conventional proportional-integral-differential controller because it is highly coupled with reactivity compensation. Thus, manual shape controls are usually employed even for the limited capability needed for load-following operations including frequency control. In an attempt to achieve automatic power shape control without any design modifications to the core, a fuzzy power control algorithm is proposed. For the fuzzy control, the rule base is formulated based on a multiple-input multiple-output system. The minimum operation rule and the center of area method are implemented for the development of the fuzzy algorithm. The fuzzy power control algorithm has been applied to Yonggwang Nuclear Unit 3. The simulation results show that the fuzzy control can be adapted as a practical control strategy for automatic reactor power control of PWRs during the load-following operations.

  1. A comparative design and tuning for conventional fuzzy control.

    PubMed

    Li, H X

    1997-01-01

    A new methodology is introduced for designing and tuning the scaling gains of the conventional fuzzy logic controller (FLC) based on its well-tuned linear counterpart. The conventional FLC with a linear rule base is very similar to its linear counterpart. The linear three-term controller has proportional, integral and/or derivative gains. Similarly, the conventional fuzzy three-term controller also has fuzzy proportional, integral and/or derivative gains. The new concept "fuzzy transfer function" is invented to connect these fuzzy gains with the corresponding scaling gains. The comparative gain design is presented by using the gains of the well-tuned linear counterpart as the initial fuzzy gains of the conventional FLC. Furthermore, the relationship between the scaling gains and the performance can be deduced to produce the comparative tuning algorithm, which can tune the scaling gains to their optimum by less trial and error. The performance comparison in the simulation demonstrates the viability of the new methodology.

  2. Risk assessment for transboundary rivers using fuzzy synthetic evaluation technique

    NASA Astrophysics Data System (ADS)

    Rai, Subash P.; Sharma, Nayan; Lohani, A. K.

    2014-11-01

    Large scale urbanization has resulted in greater withdrawals of shared waters and this withdrawal has been largely dependent on the hegemony of the riparian's. The last few decades has seen the upward surge of many countries in terms of development as well as hegemony. Existing structures of established water sharing framework typically evaluate only parameters related to historic water use such as historic water demand and supply, contribution to flow, and hydrology. Water conflicts and cooperation is affected by various issues related with development and hegemony. Characterization and quantification of development and hegemony parameters is a very complex process. This paper establishes a novel approach to predict river basins at risk; the approach addresses the issue of water conflict and cooperation within a methodologically more rigorous predictive framework. Fuzzy synthetic evaluation technique is used in this paper to undertake the risk assessment of international transboundary rivers. In this paper the fuzzy domain of risk consists of two fuzzy sets - hegemony and development, indices of which are developed with the help of fuzzy synthetic evaluation techniques. Then the compositional rule-base is framed to ascertain the fuzzy risk. This fuzzy risk can be further used to prioritize all the international river basins which can help in the identification of potentially high risk basins. Risk identification of international river basins is not only scientifically valuable, but also practically highly useful. Identifying those basins that are likely to be particularly prone to conflict or cooperation is of high interest to policy makers.

  3. Comparison of induced rules based on likelihood estimation

    NASA Astrophysics Data System (ADS)

    Tsumoto, Shusaku

    2002-03-01

    Rule induction methods have been applied to knowledge discovery in databases and data mining, The empirical results obtained show that they are very powerful and that important knowledge has been extracted from datasets. However, comparison and evaluation of rules are based not on statistical evidence but on rather naive indices, such as conditional probabilities and functions of conditional probabilities. In this paper, we introduce two approaches to induced statistical comparison of induced rules. For the statistical evaluation, likelihood ratio test and Fisher's exact test play an important role: likelihood ratio statistic measures statistical information about an information table and it is used to measure the difference between two tables.

  4. Qualitative information modeling: The role of fuzzy logic in project economic evaluations

    SciTech Connect

    Warnken, P.G.

    1995-12-31

    Conventional models rely on a precise mathematical formalism to express the quantitative essentials of the system being modeled. In contrast, decisionmakers in the real world employ cognitive skills to process information and arrive at decisions based on judgement and experience. Bridging the gap between the two analytic approaches -- that is, formulating intelligent models -- has met with very limited success using traditional computational methods. The difficulty stems from two problems. First, imprecision, which is the distinguishing feature of qualitative factors, is an information attribute that is not easily computable using the rules of traditional set theory and Boolean (bivalent) logic. Second, cognitive information processing is cumbersome using the numerical rule-based approaches common in today`s expert systems. Fuzzy models overcome these problems by employing new mathematical rules for expressing and processing knowledge. These rules are based on fuzzy logic. Fuzzy logic is the formal symbolic language used to represent linguistic terms and verbal rules for computational and modeling purposes. This language provides model builders with the means to incorporate subjective judgements, imprecise information, and human reasoning capabilities as part of a model`s framework. This paper outlines the concepts needed to understand fuzzy modeling systems. The key concepts discussed include fuzzy sets, fuzzy logical operators, linguistic variables, and verbal rules. A simple fuzzy economic rating model for project investments is presented to demonstrate the fuzzy modeling technique. Finally, the paper discusses the role of fuzzy logic in the economic modeling process.

  5. Knowledge typology for imprecise probabilities.

    SciTech Connect

    Wilson, G. D.; Zucker, L. J.

    2002-01-01

    When characterizing the reliability of a complex system there are often gaps in the data available for specific subsystems or other factors influencing total system reliability. At Los Alamos National Laboratory we employ ethnographic methods to elicit expert knowledge when traditional data is scarce. Typically, we elicit expert knowledge in probabilistic terms. This paper will explore how we might approach elicitation if methods other than probability (i.e., Dempster-Shafer, or fuzzy sets) prove more useful for quantifying certain types of expert knowledge. Specifically, we will consider if experts have different types of knowledge that may be better characterized in ways other than standard probability theory.

  6. Development of a neuro-fuzzy expert system for predictive maintenance

    NASA Astrophysics Data System (ADS)

    Yen, Gary G.; Meesad, Phayung

    2001-07-01

    In this paper, a method for automatic constructing a fuzzy expert system from numerical data using the ILFN network and the Genetic Algorithm is presented. The Incremental Learning Fuzzy Neural (ILFN) network was developed for pattern classification applications. The ILFN network, employed fuzzy sets and neural network theory, is a fast, one-pass, on-line, and incremental nearing algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly mapped into if-then rule bases. A knowledge base for fuzzy expert systems can then be 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 important features of input patterns needed to provide to a fuzzy rule-based system. Three computer simulations using the Wisconsin breast cancer data set were performed. Using 400 patterns for training and 299 patterns for testing, the derived fuzzy expert system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.

  7. Probability Theory

    NASA Astrophysics Data System (ADS)

    Jaynes, E. T.; Bretthorst, G. Larry

    2003-04-01

    Foreword; Preface; Part I. Principles and Elementary Applications: 1. Plausible reasoning; 2. The quantitative rules; 3. Elementary sampling theory; 4. Elementary hypothesis testing; 5. Queer uses for probability theory; 6. Elementary parameter estimation; 7. The central, Gaussian or normal distribution; 8. Sufficiency, ancillarity, and all that; 9. Repetitive experiments, probability and frequency; 10. Physics of 'random experiments'; Part II. Advanced Applications: 11. Discrete prior probabilities, the entropy principle; 12. Ignorance priors and transformation groups; 13. Decision theory: historical background; 14. Simple applications of decision theory; 15. Paradoxes of probability theory; 16. Orthodox methods: historical background; 17. Principles and pathology of orthodox statistics; 18. The Ap distribution and rule of succession; 19. Physical measurements; 20. Model comparison; 21. Outliers and robustness; 22. Introduction to communication theory; References; Appendix A. Other approaches to probability theory; Appendix B. Mathematical formalities and style; Appendix C. Convolutions and cumulants.

  8. Posterror slowing predicts rule-based but not information-integration category learning.

    PubMed

    Tam, Helen; Maddox, W Todd; Huang-Pollock, Cynthia L

    2013-12-01

    We examined whether error monitoring, operationalized as the degree to which individuals slow down after committing an error (i.e., posterror slowing), is differentially important in the learning of rule-based versus information-integration category structures. Rule-based categories are most efficiently solved through the application of an explicit verbal strategy (e.g., "sort by color"). In contrast, information-integration categories are believed to be learned in a trial-by-trial, associative manner. Our results indicated that posterror slowing predicts enhanced rule-based but not information-integration category learning. Implications for multiple category-learning systems are discussed.

  9. Fuzzy logic controller optimization

    DOEpatents

    Sepe, Jr., Raymond B; Miller, John Michael

    2004-03-23

    A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.

  10. Multiple Instance Fuzzy Inference

    DTIC Science & Technology

    2015-12-02

    Zhang, Xin Chen, and Wei-Bang Chen, “An online multiple instance learn - ing system for semantic image retrieval,” in Multimedia Workshops, 2007. ISMW...INFERENCE A novel fuzzy learning framework that employs fuzzy inference to solve the problem of multiple instance learning (MIL) is presented. The...fuzzy learning framework that employs fuzzy inference to solve the problem of multiple instance learning (MIL) is presented. The framework introduces a

  11. Adaptive hierarchical fuzzy controller

    SciTech Connect

    Raju, G.V.S.; Jun Zhou

    1993-07-01

    A methodology for designing adaptive hierarchical fuzzy controllers is presented. In order to evaluate this concept, several suitable performance indices were developed and converted to linguistic fuzzy variables. Based on those variables, a supervisory fuzzy rule set was constructed and used to change the parameters of a hierarchical fuzzy controller to accommodate the variations of system parameters. The proposed algorithm was used in feedwater flow control to a steam generator. Simulation studies are presented that illustrate the effectiveness of the approach

  12. Fuzzy Logic Engine

    NASA Technical Reports Server (NTRS)

    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.

  13. Advances in fuzzy theory.

    PubMed

    Sadegh-Zadeh, K

    1999-03-01

    Recent advances in the foundations of fuzzy theory facilitated by Bart Kosko's discovery of the fuzzy hypercube are of high relevance to artificial intelligence in medicine research. The new concepts of set inclusion, equality, and similarity are therefore reported in this paper. A unifying notion of a Lotfi Zadeh space is also introduced to show that Bart Kosko's fuzzy hypercube is a Zadeh space.

  14. Exploration of SWRL Rule Bases through Visualization, Paraphrasing, and Categorization of Rules

    NASA Astrophysics Data System (ADS)

    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.

  15. Robust Fuzzy Controllers Using FPGAs

    NASA Technical Reports Server (NTRS)

    Monroe, Author Gene S., Jr.

    2007-01-01

    Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.

  16. Intrusion detection: a novel approach that combines boosting genetic fuzzy classifier and data mining techniques

    NASA Astrophysics Data System (ADS)

    Ozyer, Tansel; Alhajj, Reda; Barker, Ken

    2005-03-01

    This paper proposes an intelligent intrusion detection system (IDS) which is an integrated approach that employs fuzziness and two of the well-known data mining techniques: namely classification and association rule mining. By using these two techniques, we adopted the idea of using an iterative rule learning that extracts out rules from the data set. Our final intention is to predict different behaviors in networked computers. To achieve this, we propose to use a fuzzy rule based genetic classifier. Our approach has two main stages. First, fuzzy association rule mining is applied and a large number of candidate rules are generated for each class. Then the rules pass through pre-screening mechanism in order to reduce the fuzzy rule search space. Candidate rules obtained after pre-screening are used in genetic fuzzy classifier to generate rules for the specified classes. Classes are defined as Normal, PRB-probe, DOS-denial of service, U2R-user to root and R2L- remote to local. Second, an iterative rule learning mechanism is employed for each class to find its fuzzy rules required to classify data each time a fuzzy rule is extracted and included in the system. A Boosting mechanism evaluates the weight of each data item in order to help the rule extraction mechanism focus more on data having relatively higher weight. Finally, extracted fuzzy rules having the corresponding weight values are aggregated on class basis to find the vote of each class label for each data item.

  17. A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG

    PubMed Central

    Rabbi, Ahmed Fazle; Fazel-Rezai, Reza

    2012-01-01

    We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved. PMID:22577370

  18. A fuzzy logic system for seizure onset detection in intracranial EEG.

    PubMed

    Rabbi, Ahmed Fazle; Fazel-Rezai, Reza

    2012-01-01

    We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.

  19. Fuzzy self-learning control for magnetic servo system

    NASA Technical Reports Server (NTRS)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  20. Lexicographic Probability, Conditional Probability, and Nonstandard Probability

    DTIC Science & Technology

    2009-11-11

    the following conditions: CP1. µ(U |U) = 1 if U ∈ F ′. CP2 . µ(V1 ∪ V2 |U) = µ(V1 |U) + µ(V2 |U) if V1 ∩ V2 = ∅, U ∈ F ′, and V1, V2 ∈ F . CP3. µ(V |U...µ(V |X)× µ(X |U) if V ⊆ X ⊆ U , U,X ∈ F ′, V ∈ F . Note that it follows from CP1 and CP2 that µ(· |U) is a probability measure on (W,F) (and, in... CP2 hold. This is easily seen to determine µ. Moreover, µ vaciously satisfies CP3, since there do not exist distinct sets U and X in F ′ such that U

  1. Indeterminacy, linguistic semantics and fuzzy logic

    SciTech Connect

    Novak, V.

    1996-12-31

    In this paper, we discuss the indeterminacy phenomenon which has two distinguished faces, namely uncertainty modeled especially by the probability theory and vagueness, modeled by fuzzy logic. Other important mathematical model of vagueness is provided by the Alternative Set Theory. We focus on some of the basic concepts of these theories in connection with mathematical modeling of the linguistic semantics.

  2. Fuzzy Logic Model for Multi-Reservoir Operation

    NASA Astrophysics Data System (ADS)

    Mohan, S.; Prasad, M. Anjaneya

    Reservoirs are built usually to serve multiple purposes, viz. irrigation, municipal and industrial water supply, hydro-power and flood control. Because of high variability of annual rainfall and conflicting demands on scarce water resources, the study and operation of reservoir systems has assumed great significance to meet the short and long-term requirements. The reservoir managers do not find previous techniques of complex optimization models are difficult to adopt practically. New methods have to be developed, which are simple to understand and can be effectively adopted for the existing systems. Artificial intelligent tools like Genetic programming, Neural Networks and fuzzy logic methods are increasingly becoming popular in Water resources applications. The fuzzy-rule-based systems are very much suitable for inferring developed operating policies. In the present work a rule-based Fuzzy model is attempted for long-term operation a multi-reservoir system. The model was developed on monthly basis for operation and the model was demonstrated with a case of two serial reservoirs on River Godavari sub-system located in South India. The uniqueness of the present paper is that the model was developed based on the historical operation so that the model may be acceptable to reservoir managers, since the departmental expertise was the basis for the model development. The performance of the model was tested with both calibrated and validation periods. The performance of model during two crop seasons of year was reported.

  3. Applications of fuzzy logic

    SciTech Connect

    Zargham, M.R.

    1995-06-01

    Recently, fuzzy logic has been applied to many areas, such as process control, image understanding, robots, expert systems, and decision support systems. This paper will explain the basic concepts of fuzzy logic and its application in different fields. The steps to design a control system will be explained in detail. Fuzzy control is the first successful industrial application of fuzzy logic. A fuzzy controller is able to control systems which previously could only be controlled by skilled operators. In recent years Japan has achieved significant progress in this area and has applied it to variety of products such as cruise control for cars, video cameras, rice cookers, washing machines, etc.

  4. Recurrent fuzzy ranking methods

    NASA Astrophysics Data System (ADS)

    Hajjari, Tayebeh

    2012-11-01

    With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.

  5. Fuzzy inference game approach to uncertainty in business decisions and market competitions.

    PubMed

    Oderanti, Festus Oluseyi

    2013-01-01

    The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Therefore, we developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors. We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments.

  6. Fuzzy C-means clustering with local information and kernel metric for image segmentation.

    PubMed

    Gong, Maoguo; Liang, Yan; Shi, Jiao; Ma, Wenping; Ma, Jingjing

    2013-02-01

    In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.

  7. Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass.

    PubMed

    Tai, Chao; Voltan, Diego S; Keshwani, Deepak R; Meyer, George E; Kuhar, Pankaj S

    2016-06-01

    A fuzzy logic feedback control system was developed for process monitoring and feeding control in fed-batch enzymatic hydrolysis of a lignocellulosic biomass, dilute acid-pretreated corn stover. Digested glucose from hydrolysis reaction was assigned as input while doser feeding time and speed of pretreated biomass were responses from fuzzy logic control system. Membership functions for these three variables and rule-base were created based on batch hydrolysis data. The system response was first tested in LabVIEW environment then the performance was evaluated through real-time hydrolysis reaction. The feeding operations were determined timely by fuzzy logic control system and efficient responses were shown to plateau phases during hydrolysis. Feeding of proper amount of cellulose and maintaining solids content was well balanced. Fuzzy logic proved to be a robust and effective online feeding control tool for fed-batch enzymatic hydrolysis.

  8. The Temperature Fuzzy Control System of Barleythe Malt Drying Based on Microcontroller

    NASA Astrophysics Data System (ADS)

    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.

  9. Comparative study of a learning fuzzy PID controller and a self-tuning controller.

    PubMed

    Kazemian, H B

    2001-01-01

    The self-organising fuzzy controller is an extension of the rule-based fuzzy controller with an additional learning capability. The self-organising fuzzy (SOF) is used as a master controller to readjust conventional PID gains at the actuator level during the system operation, copying the experience of a human operator. The application of the self-organising fuzzy PID (SOF-PID) controller to a 2-link non-linear revolute-joint robot-arm is studied using path tracking trajectories at the setpoint. For the purpose of comparison, the same experiments are repeated by using the self-tuning controller subject to the same data supplied at the setpoint. For the path tracking experiments, the output trajectories of the SOF-PID controller followed the specified path closer and smoother than the self-tuning controller.

  10. An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination.

    PubMed

    Li, Jianbing; Huang, Gordon H; Zeng, Guangming; Maqsood, Imran; Huang, Yuefei

    2007-01-01

    An integrated fuzzy-stochastic risk assessment (IFSRA) approach was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with site conditions, environmental guidelines, and health impact criteria. The contaminant concentrations in groundwater predicted from a numerical model were associated with probabilistic uncertainties due to the randomness in modeling input parameters, while the consequences of contaminant concentrations violating relevant environmental quality guidelines and health evaluation criteria were linked with fuzzy uncertainties. The contaminant of interest in this study was xylene. The environmental quality guideline was divided into three different strictness categories: "loose", "medium" and "strict". The environmental-guideline-based risk (ER) and health risk (HR) due to xylene ingestion were systematically examined to obtain the general risk levels through a fuzzy rule base. The ER and HR risk levels were divided into five categories of "low", "low-to-medium", "medium", "medium-to-high" and "high", respectively. The general risk levels included six categories ranging from "low" to "very high". The fuzzy membership functions of the related fuzzy events and the fuzzy rule base were established based on a questionnaire survey. Thus the IFSRA integrated fuzzy logic, expert involvement, and stochastic simulation within a general framework. The robustness of the modeling processes was enhanced through the effective reflection of the two types of uncertainties as compared with the conventional risk assessment approaches. The developed IFSRA was applied to a petroleum-contaminated groundwater system in western Canada. Three scenarios with different environmental quality guidelines were analyzed, and reasonable results were obtained. The risk assessment approach developed in this study offers a unique tool for systematically quantifying various uncertainties in contaminated site management, and it also

  11. Adaptive neural-based fuzzy modeling for biological systems.

    PubMed

    Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong

    2013-04-01

    The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems. Copyright © 2013 Elsevier Inc. All rights reserved.

  12. Confidence Probability versus Detection Probability

    SciTech Connect

    Axelrod, M

    2005-08-18

    In a discovery sampling activity the auditor seeks to vet an inventory by measuring (or inspecting) a random sample of items from the inventory. When the auditor finds every sample item in compliance, he must then make a confidence statement about the whole inventory. For example, the auditor might say: ''We believe that this inventory of 100 items contains no more than 5 defectives with 95% confidence.'' Note this is a retrospective statement in that it asserts something about the inventory after the sample was selected and measured. Contrast this to the prospective statement: ''We will detect the existence of more than 5 defective items in this inventory with 95% probability.'' The former uses confidence probability while the latter uses detection probability. For a given sample size, the two probabilities need not be equal, indeed they could differ significantly. Both these probabilities critically depend on the auditor's prior belief about the number of defectives in the inventory and how he defines non-compliance. In other words, the answer strongly depends on how the question is framed.

  13. Fuzzy energy management for hybrid fuel cell/battery systems for more electric aircraft

    NASA Astrophysics Data System (ADS)

    Corcau, Jenica-Ileana; Dinca, Liviu; Grigorie, Teodor Lucian; Tudosie, Alexandru-Nicolae

    2017-06-01

    In this paper is presented the simulation and analysis of a Fuzzy Energy Management for Hybrid Fuel cell/Battery Systems used for More Electric Aircraft. The fuel cell hybrid system contains of fuel cell, lithium-ion batteries along with associated dc to dc boost converters. In this configuration the battery has a dc to dc converter, because it is an active in the system. The energy management scheme includes the rule based fuzzy logic strategy. This scheme has a faster response to load change and is more robust to measurement imprecisions. Simulation will be provided using Matlab/Simulink based models. Simulation results are given to show the overall system performance.

  14. Parameter Interval Estimation of System Reliability for Repairable Multistate Series-Parallel System with Fuzzy Data

    PubMed Central

    2014-01-01

    The purpose of this paper is to create an interval estimation of the fuzzy system reliability for the repairable multistate series–parallel system (RMSS). Two-sided fuzzy confidence interval for the fuzzy system reliability is constructed. The performance of fuzzy confidence interval is considered based on the coverage probability and the expected length. In order to obtain the fuzzy system reliability, the fuzzy sets theory is applied to the system reliability problem when dealing with uncertainties in the RMSS. The fuzzy number with a triangular membership function is used for constructing the fuzzy failure rate and the fuzzy repair rate in the fuzzy reliability for the RMSS. The result shows that the good interval estimator for the fuzzy confidence interval is the obtained coverage probabilities the expected confidence coefficient with the narrowest expected length. The model presented herein is an effective estimation method when the sample size is n ≥ 100. In addition, the optimal α-cut for the narrowest lower expected length and the narrowest upper expected length are considered. PMID:24987728

  15. A Metrics Taxonomy and Reporting Strategy for Rule-Based Alerts

    PubMed Central

    Krall, Michael; Gerace, Alexander

    2015-01-01

    Context: Because institutions rely on rule-based alerts as an important component of their safety and quality strategies, they should determine whether the alerts achieve the expected benefit. Objective: To develop and to test a method of reporting outcome metrics for rule-based electronic health record alerts on a large scale. Methods: We empirically developed an action-oriented alerts taxonomy according to structure, actions, and implicit intended process outcomes using a set of 333 rule-based alerts at Kaiser Permanente Northwest. Next we developed a method for producing metrics reports for alert classes. Finally, we applied this method to alert taxa. Main Outcome Measures: Outcome measures were the successful development of a rule-based alerts taxonomy and the demonstration of its application in a reporting strategy. Results: We identified 9 major and 17 overall classes of alerts. We developed a specific metric approach for 5 of these classes, including the 3 most numerous ones in our institution, accounting for 224 (67%) of our alerts. Some alert classes do not readily lend themselves to this approach. Conclusions: We developed a taxonomy for rule-based alerts and demonstrated its application in developing outcome metrics reports on a large scale. This information allows tuning or retiring alerts and may inform the need to develop complementary or alternative approaches to address organizational imperatives. A method that assigns alerts to classes each amenable to a particular reporting strategy could reduce the difficulty of producing metrics reports. PMID:26057684

  16. Differential Impact of Visuospatial Working Memory on Rule-based and Information-integration Category Learning.

    PubMed

    Xing, Qiang; Sun, Hailong

    2017-01-01

    Previous studies have indicated that the category learning system is a mechanism with multiple processing systems, and that working memory has different effects on category learning. But how does visuospatial working memory affect perceptual category learning? As there is no definite answer to this question, we conducted three experiments. In Experiment 1, the dual-task paradigm with sequential presentation was adopted to investigate the influence of visuospatial working memory on rule-based and information-integration category learning. The results showed that visuospatial working memory interferes with rule-based but not information-integration category learning. In Experiment 2, the dual-task paradigm with simultaneous presentation was used, in which the categorization task was integrated into the visuospatial working memory task. The results indicated that visuospatial working memory affects information-integration category learning but not rule-based category learning. In Experiment 3, the dual-task paradigm with simultaneous presentation was employed, in which visuospatial working memory was integrated into the category learning task. The results revealed that visuospatial working memory interferes with both rule-based and information-integration category learning. Through these three experiments, we found that, regarding the rule-based category learning, working memory load is the main mechanism by which visuospatial working memory influences the discovery of the category rules. In addition, regarding the information-integration category learning, visual resources mainly operates on the category representation.

  17. Differential Impact of Visuospatial Working Memory on Rule-based and Information-integration Category Learning

    PubMed Central

    Xing, Qiang; Sun, Hailong

    2017-01-01

    Previous studies have indicated that the category learning system is a mechanism with multiple processing systems, and that working memory has different effects on category learning. But how does visuospatial working memory affect perceptual category learning? As there is no definite answer to this question, we conducted three experiments. In Experiment 1, the dual-task paradigm with sequential presentation was adopted to investigate the influence of visuospatial working memory on rule-based and information-integration category learning. The results showed that visuospatial working memory interferes with rule-based but not information-integration category learning. In Experiment 2, the dual-task paradigm with simultaneous presentation was used, in which the categorization task was integrated into the visuospatial working memory task. The results indicated that visuospatial working memory affects information-integration category learning but not rule-based category learning. In Experiment 3, the dual-task paradigm with simultaneous presentation was employed, in which visuospatial working memory was integrated into the category learning task. The results revealed that visuospatial working memory interferes with both rule-based and information-integration category learning. Through these three experiments, we found that, regarding the rule-based category learning, working memory load is the main mechanism by which visuospatial working memory influences the discovery of the category rules. In addition, regarding the information-integration category learning, visual resources mainly operates on the category representation. PMID:28439250

  18. An approach to explainable deep learning using fuzzy inference

    NASA Astrophysics Data System (ADS)

    Bonanno, David; Nock, Kristen; Smith, Leslie; Elmore, Paul; Petry, Fred

    2017-05-01

    Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Convolutional neural networks continue to dominate image classification problems and recursive neural networks have proven their utility in caption generation and language translations. While these approaches are powerful, they do not offer explanation for how the output is generated. Without understanding how deep learning arrives at a solution there is no guarantee that these networks will transition from controlled laboratory environments to fieldable systems. This paper presents an approach for incorporating such rule based methodology into neural networks by embedding fuzzy inference systems into deep learning networks.

  19. Fuzzy mathematical techniques with applications

    SciTech Connect

    Kandel, A.

    1986-01-01

    This text presents the basic concepts of fuzzy set theory within a context of real-world applications. The book is self-contained and can be used as a starting point for people interested in this fast growing field as well as by researchers looking for new application techniques. The section on applications includes: Manipulation of knowledge in expert systems; relational database structures; pattern clustering; analysis of transient behavior in digital systems; modeling of uncertainty and search trees. Contents: Fuzzy sets; Possibility theory and fuzzy quantification; Fuzzy functions; Fuzzy events and fuzzy statistics; Fuzzy relations; Fuzzy logics; Some applications; Bibliography.

  20. Fuzzy risk matrix.

    PubMed

    Markowski, Adam S; Mannan, M Sam

    2008-11-15

    A risk matrix is a mechanism to characterize and rank process risks that are typically identified through one or more multifunctional reviews (e.g., process hazard analysis, audits, or incident investigation). This paper describes a procedure for developing a fuzzy risk matrix that may be used for emerging fuzzy logic applications in different safety analyses (e.g., LOPA). The fuzzification of frequency and severity of the consequences of the incident scenario are described which are basic inputs for fuzzy risk matrix. Subsequently using different design of risk matrix, fuzzy rules are established enabling the development of fuzzy risk matrices. Three types of fuzzy risk matrix have been developed (low-cost, standard, and high-cost), and using a distillation column case study, the effect of the design on final defuzzified risk index is demonstrated.

  1. Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data

    SciTech Connect

    Timothy R. McJunkin; Jill R. Scott

    2008-06-01

    The fuzzy logic method is applied to classification of mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (I2LD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown sample against a library of spectra from known samples. Automated positioning and acquisition allow the I2LD-FTMS to acquire data from very large grids, which would require classification of up to 3600 spectra per hour to keep pace with the acquisition. The tedious job of classifying numerous spectra generated in an I2LD-FTMS imaging application can be replaced by a fuzzy rule base if the cues an operator uses can be encapsulated. Appropriate methods for assigning fuzzy membership values for inputs (e.g., mass spectrum abundances) and choice of fuzzy inference operators to translate linguistic antecedent into confidence values for the consequence (or in this case the classification) is followed by using the maximum confidence and a necessary minimum threshold for making a crisp decision. This paper also describes a method for gathering statistics on ions, which are not currently used in the rule base, but which may be candidates for making the rule base more accurate and complete or to form new rule bases based on data obtained from known samples. A spatial method for classifying spectra with low membership values, based on neighboring sample classifications, is also presented.

  2. A reinforcement learning-based architecture for fuzzy logic control

    NASA Technical Reports Server (NTRS)

    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.

  3. A reinforcement learning-based architecture for fuzzy logic control

    NASA Technical Reports Server (NTRS)

    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.

  4. Implementing a commercial rule base as a medication order safety net.

    PubMed

    Reichley, Richard M; Seaton, Terry L; Resetar, Ervina; Micek, Scott T; Scott, Karen L; Fraser, Victoria J; Dunagan, W Claiborne; Bailey, Thomas C

    2005-01-01

    A commercial rule base (Cerner Multum) was used to identify medication orders exceeding recommended dosage limits at five hospitals within BJC HealthCare, an integrated health care system. During initial testing, clinical pharmacists determined that there was an excessive number of nuisance and clinically insignificant alerts, with an overall alert rate of 9.2%. A method for customizing the commercial rule base was implemented to increase rule specificity for problematic rules. The system was subsequently deployed at two facilities and achieved alert rates of less than 1%. Pharmacists screened these alerts and contacted ordering physicians in 21% of cases. Physicians made therapeutic changes in response to 38% of alerts presented to them. By applying simple techniques to customize rules, commercial rule bases can be used to rapidly deploy a safety net to screen drug orders for excessive dosages, while preserving the rule architecture for later implementations of more finely tuned clinical decision support.

  5. Rule-based system for three-dimensional shape recovery from a single perspective view

    NASA Astrophysics Data System (ADS)

    Young, Tzay Y.; Gunasekaran, Seetharaman; Shomar, Wasim J.

    1988-03-01

    A rule based system for 3D shape recovery and orientation estimation from a single perspective view is described. The primary input to our system is a set of line segments extracted from images by a complex segmentation process. In practice, humans are able to interpret 3D shape and orientation from 2D images with very little a priori information. The heuristics behind shape constancy suggest that certain regularity assumptions play an important role. Fifteen rules have been developed for the rule base which can be extended to include additional rules. The current rules deal with parallel lines, perpendicular lines, and right corners in the object space that lead to the given image instance recorded by the camera. Forward chaining methodology is adopted. The implementation is written in the rule base language OPS5 in conjunction with Pascal on a VAX/VMS system. Two examples are presented, and the results are consistent with human perception.

  6. Overlay optimization for 1x node technology and beyond via rule based sparse sampling

    NASA Astrophysics Data System (ADS)

    Aung, Nyan L.; Chung, Woong Jae; Subramany, Lokesh; Hussain, Shehzeen; Samudrala, Pavan; Gao, Haiyong; Hao, Xueli; Chen, Yen-Jen; Gomez, Juan-Manuel

    2016-03-01

    We demonstrate a cost-effective automated rule based sparse sampling method that can detect the spatial variation of overlay errors as well as the overlay signature of the fields. Our technique satisfies the following three rules: (i) homogeneous distribution of ~200 samples across the wafer, (ii) equal number of samples in scan up and scan down condition and (iii) equal number of sampling on each overlay marks per field. When rule based samplings are implemented on the two products, the differences between the full wafer map sampling and the rule based sampling are within 3.5 nm overlay spec with residuals M+3σ of 2.4 nm (x) and 2.43 nm (y) for Product A and 2.98 nm (x) and 3.32 nm (y) for Product B.

  7. A Rule-Based System Implementing a Method for Translating FOL Formulas into NL Sentences

    NASA Astrophysics Data System (ADS)

    Mpagouli, Aikaterini; Hatzilygeroudis, Ioannis

    In this paper, we mainly present the implementation of a system that translates first order logic (FOL) formulas into natural language (NL) sentences. The motivation comes from an intelligent tutoring system teaching logic as a knowledge representation language, where it is used as a means for feedback to the students-users. FOL to NL conversion is achieved by using a rule-based approach, where we exploit the pattern matching capabilities of rules. So, the system consists of rule-based modules corresponding to the phases of our translation methodology. Facts are used in a lexicon providing lexical and grammatical information that helps in producing the NL sentences. The whole system is implemented in Jess, a java-implemented rule-based programming tool. Experimental results confirm the success of our choices.

  8. A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

    NASA Technical Reports Server (NTRS)

    Shehadeh, Hana; Lea, Robert N.

    1992-01-01

    Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

  9. A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

    NASA Technical Reports Server (NTRS)

    Shehadeh, Hana; Lea, Robert N.

    1992-01-01

    Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

  10. Fuzzy and neural control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  11. Optimization Of Mean-Semivariance-Skewness Portfolio Selection Model In Fuzzy Random Environment

    NASA Astrophysics Data System (ADS)

    Chatterjee, Amitava; Bhattacharyya, Rupak; Mukherjee, Supratim; Kar, Samarjit

    2010-10-01

    The purpose of the paper is to construct a mean-semivariance-skewness portfolio selection model in fuzzy random environment. The objective is to maximize the skewness with predefined maximum risk tolerance and minimum expected return. Here the security returns in the objectives and constraints are assumed to be fuzzy random variables in nature and then the vagueness of the fuzzy random variables in the objectives and constraints are transformed into fuzzy variables which are similar to trapezoidal numbers. The newly formed fuzzy model is then converted into a deterministic optimization model. The feasibility and effectiveness of the proposed method is verified by numerical example extracted from Bombay Stock Exchange (BSE). The exact parameters of fuzzy membership function and probability density function are obtained through fuzzy random simulating the past dates.

  12. Rule-based and information-integration category learning in normal aging.

    PubMed

    Maddox, W Todd; Pacheco, Jennifer; Reeves, Maia; Zhu, Bo; Schnyer, David M

    2010-08-01

    The basal ganglia and prefrontal cortex play critical roles in category learning. Both regions evidence age-related structural and functional declines. The current study examined rule-based and information-integration category learning in a group of older and younger adults. Rule-based learning is thought to involve explicit, frontally mediated processes, whereas information-integration is thought to involve implicit, striatally mediated processes. As a group, older adults showed rule-based and information-integration deficits. A series of models were applied that provided insights onto the type of strategy used to solve the task. Interestingly, when the analyses focused only on participants who used the task appropriate strategy in the final block of trials, the age-related rule-based deficit disappeared whereas the information-integration deficit remained. For this group of individuals, the final block information-integration deficit was due to less consistent application of the task appropriate strategy by older adults, and over the course of learning these older adults shifted from an explicit hypothesis-testing strategy to the task appropriate strategy later in learning. In addition, the use of the task appropriate strategy was associated with less interference and better inhibitory control for rule-based and information-information learning, whereas use of the task appropriate strategy was associated with greater working memory and better new verbal learning only for the rule-based task. These results suggest that normal aging impacts both forms of category learning and that there are some important similarities and differences in the explanatory locus of these deficits. The data also support a two-component model of information-integration category learning that includes a striatal component that mediated procedural-based learning, and a prefrontal cortical component that mediates the transition from hypothesis-testing to procedural-based strategies

  13. Improving textual medication extraction using combined conditional random fields and rule-based systems.

    PubMed

    Tikk, Domonkos; Solt, Illés

    2010-01-01

    In the i2b2 Medication Extraction Challenge, medication names together with details of their administration were to be extracted from medical discharge summaries. The task of the challenge was decomposed into three pipelined components: named entity identification, context-aware filtering and relation extraction. For named entity identification, first a rule-based (RB) method that was used in our overall fifth place-ranked solution at the challenge was investigated. Second, a conditional random fields (CRF) approach is presented for named entity identification (NEI) developed after the completion of the challenge. The CRF models are trained on the 17 ground truth documents, the output of the rule-based NEI component on all documents, a larger but potentially inaccurate training dataset. For both NEI approaches their effect on relation extraction performance was investigated. The filtering and relation extraction components are both rule-based. In addition to the official entry level evaluation of the challenge, entity level analysis is also provided. On the test data an entry level F(1)-score of 80% was achieved for exact matching and 81% for inexact matching with the RB-NEI component. The CRF produces a significantly weaker result, but CRF outperforms the rule-based model with 81% exact and 82% inexact F(1)-score (p<0.02). This study shows that a simple rule-based method is on a par with more complicated machine learners; CRF models can benefit from the addition of the potentially inaccurate training data, when only very few training documents are available. Such training data could be generated using the outputs of rule-based methods.

  14. Rule-Based and Information-Integration Category Learning in Normal Aging

    PubMed Central

    Maddox, W. Todd; Pacheco, Jennifer; Reeves, Maia; Zhu, Bo; Schnyer, David M.

    2010-01-01

    The basal ganglia and prefrontal cortex play critical roles in category learning. Both regions evidence age-related structural and functional declines. The current study examined rule-based and information-integration category learning in a group of older and younger adults. Rule-based learning is thought to involve explicit, frontally mediated processes, whereas information-integration is thought to involve implicit, striatally mediated processes. As a group, older adults showed rule-based and information-integration deficits. A series of models were applied that provided insights onto the type of strategy used to solve the task. Interestingly, when the analyses focused only on participants who used the task appropriate strategy in the final block of trials, the age-related rule-based deficit disappeared whereas the information-integration deficit remained. For this group of individuals, the final block information-integration deficit was due to less consistent application of the task appropriate strategy by older adults, and over the course of learning these older adults shifted from an explicit hypothesis-testing strategy to the task appropriate strategy later in learning. In addition, the use of the task appropriate strategy was associated with less interference and better inhibitory control for rule-based and information-information learning, whereas use of the task appropriate strategy was associated with greater working memory and better new verbal learning only for the rule-based task. These results suggest that normal aging impacts both forms of category learning and that there are some important similarities and differences in the explanatory locus of these deficits. The data also support a two-component model of information-integration category learning that includes a striatal component that mediated procedural-based learning, and a prefrontal cortical component that mediates the transition from hypothesis-testing to procedural-based strategies

  15. Hybrid neural network and rule-based pattern recognition system capable of self-modification

    SciTech Connect

    Glover, C.W.; Silliman, M.; Walker, M.; Spelt, P.F. ); Rao, N.S.V. . Dept. of Computer Science)

    1990-01-01

    This paper describes a hybrid neural network and rule-based pattern recognition system architecture which is capable of self-modification or learning. The central research issue to be addressed for a multiclassifier hybrid system is whether such a system can perform better than the two classifiers taken by themselves. The hybrid system employs a hierarchical architecture, and it can be interfaced with one or more sensors. Feature extraction routines operating on raw sensor data produce feature vectors which serve as inputs to neural network classifiers at the next level in the hierarchy. These low-level neural networks are trained to provide further discrimination of the sensor data. A set of feature vectors is formed from a concatenation of information from the feature extraction routines and the low-level neural network results. A rule-based classifier system uses this feature set to determine if certain expected environmental states, conditions, or objects are present in the sensors' current data stream. The rule-based system has been given an a priori set of models of the expected environmental states, conditions, or objects which it is expected to identify. The rule-based system forms many candidate directed graphs of various combinations of incoming features vectors, and it uses a suitably chosen metric to measure the similarity between candidate and model directed graphs. The rule-based system must decide if there is a match between one of the candidate graphs and a model graph. If a match is found, then the rule-based system invokes a routine to create and train a new high-level neural network from the appropriate feature vector data to recognize when this model state is present in future sensor data streams. 12 refs., 3 figs.

  16. Fuzzy Subspace Clustering

    NASA Astrophysics Data System (ADS)

    Borgelt, Christian

    In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to transfer an alternative to the fuzzifier (Klawonn and Höppner, What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier, In: Proc. 5th Int. Symp. on Intelligent Data Analysis, 254-264, Springer, Berlin, 2003) to attribute weighting fuzzy clustering (Keller and Klawonn, Int J Uncertain Fuzziness Knowl Based Syst 8:735-746, 2000). In addition, by reformulating Gustafson-Kessel fuzzy clustering, a scheme for weighting and selecting principal axes can be obtained. While in Borgelt (Feature weighting and feature selection in fuzzy clustering, In: Proc. 17th IEEE Int. Conf. on Fuzzy Systems, IEEE Press, Piscataway, NJ, 2008) I already presented such an approach for a global selection of attributes and principal axes, this paper extends it to a cluster-specific selection, thus arriving at a fuzzy subspace clustering algorithm (Parsons, Haque, and Liu, 2004).

  17. Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization

    PubMed Central

    Misirli, Goksel; Cavaliere, Matteo; Waites, William; Pocock, Matthew; Madsen, Curtis; Gilfellon, Owen; Honorato-Zimmer, Ricardo; Zuliani, Paolo; Danos, Vincent; Wipat, Anil

    2016-01-01

    Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. Availability and implementation: The annotation ontology for rule-based models can be found at http

  18. A rule-based systems approach to spacecraft communications configuration optimization

    NASA Technical Reports Server (NTRS)

    Rash, James L.; Wong, Yen F.; Cieplak, James J.

    1988-01-01

    An experimental rule-based system for optimizing user spacecraft communications configurations was developed at NASA to support mission planning for spacecraft that obtain telecommunications services through NASA's Tracking and Data Relay Satellite System. Designated Expert for Communications Configuration Optimization (ECCO), and implemented in the OPS5 production system language, the system has shown the validity of a rule-based systems approach to this optimization problem. The development of ECCO and the incremental optimizatin method on which it is based are discussed. A test case using hypothetical mission data is included to demonstrate the optimization concept.

  19. GetBonNie for building, analyzing and sharing rule-based models

    SciTech Connect

    Hu, Bin

    2008-01-01

    GetBonNie is a suite of web-based services for building, analyzing, and sharing rule-based models specified according to the conventions of the BioNetGen language (BNGL). Services include (1) an applet for drawing, editing, and viewing graphs of BNGL; (2) a network-generation engine for translating a set of rules into a chemical reaction network; (3) simulation engines that implement generate-first, on-the-fly, and network-free methods for simulating rule-based models; and (4) a database for sharing models, parameter values, annotations, simulation tasks and results.

  20. RuleMonkey: software for stochastic simulation of rule-based models

    PubMed Central

    2010-01-01

    Background The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems. Results Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods. Conclusions RuleMonkey enables the simulation of

  1. RuleMonkey: software for stochastic simulation of rule-based models.

    PubMed

    Colvin, Joshua; Monine, Michael I; Gutenkunst, Ryan N; Hlavacek, William S; Von Hoff, Daniel D; Posner, Richard G

    2010-07-30

    The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems. Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods. RuleMonkey enables the simulation of rule-based models for which the

  2. Some Properties of Fuzzy Soft Proximity Spaces

    PubMed Central

    Demir, İzzettin; Özbakır, Oya Bedre

    2015-01-01

    We study the fuzzy soft proximity spaces in Katsaras's sense. First, we show how a fuzzy soft topology is derived from a fuzzy soft proximity. Also, we define the notion of fuzzy soft δ-neighborhood in the fuzzy soft proximity space which offers an alternative approach to the study of fuzzy soft proximity spaces. Later, we obtain the initial fuzzy soft proximity determined by a family of fuzzy soft proximities. Finally, we investigate relationship between fuzzy soft proximities and proximities. PMID:25793224

  3. Some properties of fuzzy soft proximity spaces.

    PubMed

    Demir, İzzettin; Özbakır, Oya Bedre

    2015-01-01

    We study the fuzzy soft proximity spaces in Katsaras's sense. First, we show how a fuzzy soft topology is derived from a fuzzy soft proximity. Also, we define the notion of fuzzy soft δ-neighborhood in the fuzzy soft proximity space which offers an alternative approach to the study of fuzzy soft proximity spaces. Later, we obtain the initial fuzzy soft proximity determined by a family of fuzzy soft proximities. Finally, we investigate relationship between fuzzy soft proximities and proximities.

  4. Fuzzy modelling for selecting headgear types.

    PubMed

    Akçam, M Okan; Takada, Kenji

    2002-02-01

    The purpose of this study was to develop a computer-assisted inference model for selecting appropriate types of headgear appliance for orthodontic patients and to investigate its clinical versatility as a decision-making aid for inexperienced clinicians. Fuzzy rule bases were created for degrees of overjet, overbite, and mandibular plane angle variables, respectively, according to subjective criteria based on the clinical experience and knowledge of the authors. The rules were then transformed into membership functions and the geometric mean aggregation was performed to develop the inference model. The resultant fuzzy logic was then tested on 85 cases in which the patients had been diagnosed as requiring headgear appliances. Eight experienced orthodontists judged each of the cases, and decided if they 'agreed', 'accepted', or 'disagreed' with the recommendations of the computer system. Intra-examiner agreements were investigated using repeated judgements of a set of 30 orthodontic cases and the kappa statistic. All of the examiners exceeded a kappa score of 0.7, allowing them to participate in the test run of the validity of the proposed inference model. The examiners' agreement with the system's recommendations was evaluated statistically. The average satisfaction rate of the examiners was 95.6 per cent and, for 83 out of the 85 cases, 97.6 per cent. The majority of the examiners (i.e. six or more out of the eight) were satisfied with the recommendations of the system. Thus, the usefulness of the proposed inference logic was confirmed.

  5. Automated cloud classification with a fuzzy logic expert system

    NASA Technical Reports Server (NTRS)

    Tovinkere, Vasanth; Baum, Bryan A.

    1993-01-01

    An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

  6. A self-organizing fuzzy control approach to arc sensor for weld joint tracking in gas metal arc welding of butt joints

    SciTech Connect

    Na, S.J. ); Kim, J.W.

    1993-02-01

    For the artificial intelligence (AI) approach to automatic control, the fuzzy rule-based control schemes have been successfully applied to the control of complex processes. The arc welding process is one of the processes due to the fact that it possesses complex and nonlinear characteristics such as a moving distributed heat source, a current path and metal transfer. One possible solution to the design of an effective controller suitable for such a process is to use the fuzzy control scheme. The fuzzy rule-based control can easily realize the heuristic rules obtained from human experiences that cannot be expressed in mathematical form. In this study, an arc sensor, which utilizes the electrical signal obtained from the welding arc itself, was developed for CO[sub 2] gas metal arc welding of butt joints using the fuzzy set theory. A simple fuzzy controller without any adaptation was implemented for the weld joint tracking. A set of fixed rules, which was designed based upon the experiments, and a self-organizing fuzzy controller, which could improve the control rules automatically, were examined. Through a series of experiments, the performance and learning action of the proposed self-organizing fuzzy controller were assessed.

  7. Improving Sector Hash Carving with Rule-Based and Entropy-Based Non-Probative Block Filters

    DTIC Science & Technology

    2015-03-01

    NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS IMPROVING SECTOR HASH CARVING WITH RULE-BASED AND ENTROPY -BASED NON-PROBATIVE BLOCK...AND DATES COVERED Master’s Thesis 01-07-2013 to 03-27-2015 4. TITLE AND SUBTITLE IMPROVING SECTOR HASH CARVING WITH RULE-BASED AND ENTROPY - BASED NON...phase uses rule-based and entropy -based non-probative block filters to improve matching against all file types. In the second phase, we restrict the

  8. Prefrontal Contributions to Rule-Based and Information-Integration Category Learning

    ERIC Educational Resources Information Center

    Schnyer, David M.; Maddox, W. Todd; Ell, Shawn; Davis, Sarah; Pacheco, Jenni; Verfaellie, Mieke

    2009-01-01

    Previous research revealed that the basal ganglia play a critical role in category learning [Ell, S. W., Marchant, N. L., & Ivry, R. B. (2006). "Focal putamen lesions impair learning in rule-based, but not information-integration categorization tasks." "Neuropsychologia", 44(10), 1737-1751; Maddox, W. T. & Filoteo, J.…

  9. Segmentation-based and rule-based spectral mixture analysis for estimating urban imperviousness

    NASA Astrophysics Data System (ADS)

    Li, Miao; Zang, Shuying; Wu, Changshan; Deng, Yingbin

    2015-03-01

    For detailed estimation of urban imperviousness, numerous image processing methods have been developed, and applied to different urban areas with some success. Most of these methods, however, are global techniques. That is, they have been applied to the entire study area without considering spatial and contextual variations. To address this problem, this paper explores whether two spatio-contextual analysis techniques, namely segmentation-based and rule-based analysis, can improve urban imperviousness estimation. These two spatio-contextual techniques were incorporated to a classic urban imperviousness estimation technique, fully-constrained linear spectral mixture analysis (FCLSMA) method. In particular, image segmentation was applied to divide the image to homogenous segments, and spatially varying endmembers were chosen for each segment. Then an FCLSMA was applied for each segment to estimate the pixel-wise fractional coverage of high-albedo material, low-albedo material, vegetation, and soil. Finally, a rule-based analysis was carried out to estimate the percent impervious surface area (%ISA). The developed technique was applied to a Landsat TM image acquired in Milwaukee River Watershed, an urbanized watershed in Wisconsin, United States. Results indicate that the performance of the developed segmentation-based and rule-based LSMA (S-R-LSMA) outperforms traditional SMA techniques, with a mean average error (MAE) of 5.44% and R2 of 0.88. Further, a comparative analysis shows that, when compared to segmentation, rule-based analysis plays a more essential role in improving the estimation accuracy.

  10. Prefrontal Contributions to Rule-Based and Information-Integration Category Learning

    ERIC Educational Resources Information Center

    Schnyer, David M.; Maddox, W. Todd; Ell, Shawn; Davis, Sarah; Pacheco, Jenni; Verfaellie, Mieke

    2009-01-01

    Previous research revealed that the basal ganglia play a critical role in category learning [Ell, S. W., Marchant, N. L., & Ivry, R. B. (2006). "Focal putamen lesions impair learning in rule-based, but not information-integration categorization tasks." "Neuropsychologia", 44(10), 1737-1751; Maddox, W. T. & Filoteo, J.…

  11. Automated implementation of rule-based expert systems with neural networks for time-critical applications

    NASA Technical Reports Server (NTRS)

    Ramamoorthy, P. A.; Huang, Song; Govind, Girish

    1991-01-01

    In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

  12. Rule-Based Expert Systems in the Command Estimate: An Operational Perspective

    DTIC Science & Technology

    1990-06-01

    11 Chapter 1 End Notes ...... ................. 14 CHAPTER 2. REVIEW OF LITERATURE Review of Literature ...... ................. 15 Rule-based Expert...War Gaming ... ......... 32 Conclusion .......... ...................... 34 Chapter 2 End Notes ........ ................. 35 CHAPTER 3. RESEARCH...39 Selection of Subject Matter Experts ... ......... 42 Systems Analysis of the Command Estimate ... ....... 43 Chapter 3 End Notes ...... ................. 48

  13. CT Image Sequence Analysis for Object Recognition - A Rule-Based 3-D Computer Vision System

    Treesearch

    Dongping Zhu; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman

    1991-01-01

    Research is now underway to create a vision system for hardwood log inspection using a knowledge-based approach. In this paper, we present a rule-based, 3-D vision system for locating and identifying wood defects using topological, geometric, and statistical attributes. A number of different features can be derived from the 3-D input scenes. These features and evidence...

  14. Haunted by a doppelgänger: irrelevant facial similarity affects rule-based judgments.

    PubMed

    von Helversen, Bettina; Herzog, Stefan M; Rieskamp, Jörg

    2014-01-01

    Judging other people is a common and important task. Every day professionals make decisions that affect the lives of other people when they diagnose medical conditions, grant parole, or hire new employees. To prevent discrimination, professional standards require that decision makers render accurate and unbiased judgments solely based on relevant information. Facial similarity to previously encountered persons can be a potential source of bias. Psychological research suggests that people only rely on similarity-based judgment strategies if the provided information does not allow them to make accurate rule-based judgments. Our study shows, however, that facial similarity to previously encountered persons influences judgment even in situations in which relevant information is available for making accurate rule-based judgments and where similarity is irrelevant for the task and relying on similarity is detrimental. In two experiments in an employment context we show that applicants who looked similar to high-performing former employees were judged as more suitable than applicants who looked similar to low-performing former employees. This similarity effect was found despite the fact that the participants used the relevant résumé information about the applicants by following a rule-based judgment strategy. These findings suggest that similarity-based and rule-based processes simultaneously underlie human judgment.

  15. Application of Rule-Based Computer Models to the Evaluation of Combat Training: A Feasibility Study

    DTIC Science & Technology

    1981-07-01

    Rule-Based Computer Models to Final Technical Report the Evaluation of Combat Training: A Feasi- -August 1979-July 1980 7t. AUHAs for theRAC Beha GRNT2...an ac- ceptable level of performance. For example, when talking of a "move to contact," the expected average speed is 15- 25 mph. This kind of evalua

  16. Age affects chunk-based, but not rule-based learning in artificial grammar acquisition.

    PubMed

    Kürten, Julia; De Vries, Meinou H; Kowal, Kristina; Zwitserlood, Pienie; Flöel, Agnes

    2012-07-01

    Explicit learning is well known to decline with age, but divergent results have been reported for implicit learning. Here, we assessed the effect of aging on implicit vs. explicit learning within the same task. Fifty-five young (mean 32 years) and 55 elderly (mean 64 years) individuals were exposed to letter strings generated by an artificial grammar. Subsequently, participants classified novel strings as grammatical or nongrammatical. Acquisition of superficial ("chunk-based") and structural ("rule-based") features of the grammar were analyzed separately. We found that overall classification accuracy was diminished in the elderly, driven by decreased performance on items that required chunk-based knowledge. Performance on items requiring rule-based knowledge was comparable between groups. Results indicate that rule-based and chunk-based learning are differentially affected by age: while rule-based learning, reflecting implicit learning, is preserved, chunk-based learning, which contains at least some explicit learning aspects, declines with age. Our findings may explain divergent results on implicit learning tasks in previous studies on aging. They may also help to better understand compensatory mechanisms during the aging process.

  17. Effectiveness of Visual Imagery versus Rule-Based Strategies in Teaching Spelling to Learning Disabled Students.

    ERIC Educational Resources Information Center

    Darch, Craig; Simpson, Robert G.

    1990-01-01

    Among 28 upper elementary learning-disabled students in a summer remedial program, those that were taught spelling with explicit rule-based strategies out-performed students presented with a visual imagery mnemonic on unit tests, a posttest, and a standardized spelling test. Contains 20 references. (SV)

  18. A rule-based expert system for chemical prioritization using effects-based chemical categories

    EPA Science Inventory

    A rule-based expert system (ES) was developed to predict chemical binding to the estrogen receptor (ER) patterned on the research approaches championed by Gilman Veith to whom this article and journal issue are dedicated. The ERES was built to be mechanistically-transparent and m...

  19. Techniques and implementation of the embedded rule-based expert system using Ada

    NASA Technical Reports Server (NTRS)

    Liberman, Eugene M.; Jones, Robert E.

    1991-01-01

    Ada is becoming an increasingly popular programming language for large Government-funded software projects. Ada with its portability, transportability, and maintainability lends itself well to today's complex programming environment. In addition, expert systems have also assured a growing role in providing human-like reasoning capability and expertise for computer systems. The integration of expert system technology with Ada programming language, specifically a rule-based expert system using an ART-Ada (Automated Reasoning Tool for Ada) system shell is discussed. The NASA Lewis Research Center was chosen as a beta test site for ART-Ada. The test was conducted by implementing the existing Autonomous Power EXpert System (APEX), a Lisp-base power expert system, in ART-Ada. Three components, the rule-based expert system, a graphics user interface, and communications software make up SMART-Ada (Systems fault Management with ART-Ada). The main objective, to conduct a beta test on the ART-Ada rule-based expert system shell, was achieved. The system is operational. New Ada tools will assist in future successful projects. ART-Ada is one such tool and is a viable alternative to the straight Ada code when an application requires a rule-based or knowledge-based approach.

  20. Rule-based approach to operating system selection: RMS vs. UNIX

    SciTech Connect

    Phifer, M.S.; Sadlowe, A.R.; Emrich, M.L.; Gadagkar, H.P.

    1988-10-01

    A rule-based system is under development for choosing computer operating systems. Following a brief historical account, this paper compares and contrasts the essential features of two operating systems highlighting particular applications. ATandT's UNIX System and Datapoint Corporations's Resource Management System (RMS) are used as illustrative examples. 11 refs., 3 figs.

  1. A rule-based expert system for chemical prioritization using effects-based chemical categories

    EPA Science Inventory

    A rule-based expert system (ES) was developed to predict chemical binding to the estrogen receptor (ER) patterned on the research approaches championed by Gilman Veith to whom this article and journal issue are dedicated. The ERES was built to be mechanistically-transparent and m...

  2. Using Rule-Based Computer Programming to Unify Communication Rules Research.

    ERIC Educational Resources Information Center

    Sanford, David L.; Roach, J. W.

    This paper proposes the use of a rule-based computer programming language as a standard for the expression of rules, arguing that the adoption of a standard would enable researchers to communicate about rules in a consistent and significant way. Focusing on the formal equivalence of artificial intelligence (AI) programming to different types of…

  3. Effects of Multimedia on Cognitive Load, Self-Efficacy, and Multiple Rule-Based Problem Solving

    ERIC Educational Resources Information Center

    Zheng, Robert; McAlack, Matthew; Wilmes, Barbara; Kohler-Evans, Patty; Williamson, Jacquee

    2009-01-01

    This study investigates effects of multimedia on cognitive load, self-efficacy and learners' ability to solve multiple rule-based problems. Two hundred twenty-two college students were randomly assigned to interactive and non-interactive multimedia groups. Based on Engelkamp's multimodal theory, the present study investigates the role of…

  4. Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters

    PubMed Central

    Geweniger, Tina; Fischer, Lydia; Kaden, Marika; Lange, Mandy; Villmann, Thomas

    2013-01-01

    We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluation measure for clusterings based on fuzzy vector quantization. PMID:24396342

  5. FSILP: fuzzy-stochastic-interval linear programming for supporting municipal solid waste management.

    PubMed

    Li, Pu; Chen, Bing

    2011-04-01

    Although many studies on municipal solid waste management (MSW management) were conducted under uncertain conditions of fuzzy, stochastic, and interval coexistence, the solution to the conventional linear programming problems of integrating fuzzy method with the other two was inefficient. In this study, a fuzzy-stochastic-interval linear programming (FSILP) method is developed by integrating Nguyen's method with conventional linear programming for supporting municipal solid waste management. The Nguyen's method was used to convert the fuzzy and fuzzy-stochastic linear programming problems into the conventional linear programs, by measuring the attainment values of fuzzy numbers and/or fuzzy random variables, as well as superiority and inferiority between triangular fuzzy numbers/triangular fuzzy-stochastic variables. The developed method can effectively tackle uncertainties described in terms of probability density functions, fuzzy membership functions, and discrete intervals. Moreover, the method can also improve upon the conventional interval fuzzy programming and two-stage stochastic programming approaches, with advantageous capabilities that are easily achieved with fewer constraints and significantly reduces consumption time. The developed model was applied to a case study of municipal solid waste management system in a city. The results indicated that reasonable solutions had been generated. The solution can help quantify the relationship between the change of system cost and the uncertainties, which could support further analysis of tradeoffs between the waste management cost and the system failure risk.

  6. Exact hybrid particle/population simulation of rule-based models of biochemical systems.

    PubMed

    Hogg, Justin S; Harris, Leonard A; Stover, Lori J; Nair, Niketh S; Faeder, James R

    2014-04-01

    Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings

  7. Design of an iterative auto-tuning algorithm for a fuzzy PID controller

    NASA Astrophysics Data System (ADS)

    Saeed, Bakhtiar I.; Mehrdadi, B.

    2012-05-01

    Since the first application of fuzzy logic in the field of control engineering, it has been extensively employed in controlling a wide range of applications. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic terms. However, with the lack of analytical design study it is becoming more difficult to auto-tune controller parameters. Fuzzy logic controller has several parameters that can be adjusted, such as: membership functions, rule-base and scaling gains. Furthermore, it is not always easy to find the relation between the type of membership functions or rule-base and the controller performance. This study proposes a new systematic auto-tuning algorithm to fine tune fuzzy logic controller gains. A fuzzy PID controller is proposed and applied to several second order systems. The relationship between the closed-loop response and the controller parameters is analysed to devise an auto-tuning method. The results show that the proposed method is highly effective and produces zero overshoot with enhanced transient response. In addition, the robustness of the controller is investigated in the case of parameter changes and the results show a satisfactory performance.

  8. Simultaneous structure identification and fuzzy rule generation for Takagi-Sugeno models.

    PubMed

    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.

  9. A PI-fuzzy logic controller for the regulation of blood glucose level in diabetic patients.

    PubMed

    Ibbini, M

    2006-01-01

    This manuscript investigates different fuzzy logic controllers for the regulation of blood glucose level in diabetic patients. While fuzzy logic control is still intuitive and at a very early stage, it has already been implemented in many industrial plants and reported results are very promising. A fuzzy logic control (FLC) scheme was recently proposed for maintaining blood glucose level in diabetics within acceptable limits, and was shown to be more effective with better transient characteristics than conventional techniques. In fact, FLC is based on human expertise and on desired output characteristics, and hence does not require precise mathematical models. This observation makes fuzzy rule-based technique very suitable for biomedical systems where models are, in general, either very complicated or over-simplistic. Another attractive feature of fuzzy techniques is their insensitivity to system parameter variations, as numerical values of physiological parameters are often not precise and usually vary from patient to another. PI and PID controllers are very popular and are efficiently used in many industrial plants. Fuzzy PI and PID controllers behave in a similar fashion to those classical controllers with the obvious advantage that the controller parameters are time dependant on the range of the control variables and consequently, result in a better performance. In this manuscript, a fuzzy PI controller is designed using a simplified design scheme and then subjected to simulations of the two common diabetes disturbances--sudden glucose meal and system parameter variations. The performance of the proposed fuzzy PI controller is compared to that of the conventional PID and optimal techniques and is shown to be superior. Moreover, the proposed fuzzy PI controller is shown to be more effective than the previously proposed FLC, especially with respect to the overshoot and settling time.

  10. Extracting Features for the Linguistic Variables of Fuzzy Rules Using Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Suliman, Azizah; Sulaiman, Md. Nasir; Othman, Mohamed; Wirza, Rahmita

    2008-11-01

    In classifying handwritten characters, the stages prior to the classification phase play a role as major as the classification itself. This research work will be classifying the characters using a syntactical classification method namely fuzzy logic but will use the statistical method of Hidden Markov Model as an approach in extracting features for the linguistic variables of the fuzzy rule-based system. In this paper the feature extraction method will be highlighted and detailed. The HMM Model of a variable to be used in the classification system will be discussed. Experimental results from a few sample images show that the proposed technique is both effective and efficient to be used in extracting features for the linguistic variables of fuzzy rules.

  11. On fuzzy ideals of BL-algebras.

    PubMed

    Meng, Biao Long; Xin, Xiao Long

    2014-01-01

    In this paper we investigate further properties of fuzzy ideals of a BL-algebra. The notions of fuzzy prime ideals, fuzzy irreducible ideals, and fuzzy Gödel ideals of a BL-algebra are introduced and their several properties are investigated. We give a procedure to generate a fuzzy ideal by a fuzzy set. We prove that every fuzzy irreducible ideal is a fuzzy prime ideal but a fuzzy prime ideal may not be a fuzzy irreducible ideal and prove that a fuzzy prime ideal ω is a fuzzy irreducible ideal if and only if ω(0) = 1 and |Im(ω)| = 2. We give the Krull-Stone representation theorem of fuzzy ideals in BL-algebras. Furthermore, we prove that the lattice of all fuzzy ideals of a BL-algebra is a complete distributive lattice. Finally, it is proved that every fuzzy Boolean ideal is a fuzzy Gödel ideal, but the converse implication is not true.

  12. Fuzzy modeling and simulation

    NASA Astrophysics Data System (ADS)

    Pedrycz, Witold

    1993-12-01

    The paradigm of fuzzy modelling entails development of relationships (dependencies) between the linguistic entities defined for system's variables. The key feature of the fuzzy models pertains to their significant flexibility so they could easily be modified to comply with the principle of incompatibility. Considering the existing panoply of fuzzy models one can easily conclude that most of them are embraced under an umbrella of a single conceptual structure. From a functional point of view this structure is perceived as a combination of the two conceptual interfaces and a single processing block aimed at developing calculus of the linguistic labels. The interfaces produce all the links that are necessary to combine the physical (numerical) level of the real-world system with that of a conceptual character realized within the fuzzy model and articulated at the level of the linguistic entities. The presentation will address the main methodological aspects concerning these functional components with a particular emphasis placed on the associated design principles. The main issues dominating the design of the interfaces pertain to the implemented level of information granularity, optimality of linguistic labels, and linguistic-to-numerical transformations. The processing level of the fuzzy modelling will be considered through the use of fuzzy neural networks. These distributed computing structures are highly heterogeneous as they are constructed with the aid of several distinct types of logic-oriented neurons. The advantages of the fuzzy neural networks such as an implicit scheme of knowledge encapsulation that is carried out there will be discussed in detail.

  13. Robust support vector machine-trained fuzzy system.

    PubMed

    Forghani, Yahya; Yazdi, Hadi Sadoghi

    2014-02-01

    Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules. The robust SVM is an extension of SVM for interval-valued data classification. We compare our proposed method with SVM, robust SVM, ISVM-FC (incremental support vector machine-trained fuzzy classifier), BSVM-FC (batch support vector machine-trained fuzzy classifier), SOTFN-SV (a self-organizing TS-type fuzzy network with support vector learning) and SCLSE (a TS-type fuzzy system with subtractive clustering for antecedent parameter tuning and LSE for consequent parameter tuning) by using some real datasets. According to experimental results, the use of proposed approach leads to very low training and testing time with good misclassification rate.

  14. On theoretical pricing of options with fuzzy estimators

    NASA Astrophysics Data System (ADS)

    Chrysafis, Konstantinos A.; Papadopoulos, Basil K.

    2009-01-01

    In this paper we present an application of a new method of constructing fuzzy estimators for the parameters of a given probability distribution function, using statistical data. This application belongs to the financial field and especially to the section of financial engineering. In financial markets there are great fluctuations, thus the element of vagueness and uncertainty is frequent. This application concerns Theoretical Pricing of Options and in particular the Black and Scholes Options Pricing formula. We make use of fuzzy estimators for the volatility of stock returns and we consider the stock price as a symmetric triangular fuzzy number. Furthermore we apply the Black and Scholes formula by using adaptive fuzzy numbers introduced by Thiagarajah et al. [K. Thiagarajah, S.S. Appadoo, A. Thavaneswaran, Option valuation model with adaptive fuzzy numbers, Computers and Mathematics with Applications 53 (2007) 831-841] for the stock price and the volatility and we replace the fuzzy volatility and the fuzzy stock price by possibilistic mean value. We refer to both cases of call and put option prices according to the Black & Scholes model and also analyze the results to Greek parameters. Finally, a numerical example is presented for both methods and a comparison is realized based on the results.

  15. A rule-based algorithm can output valid surgical strategies in the treatment of AIS.

    PubMed

    Phan, Philippe; Ouellet, Jean; Mezghani, Neila; de Guise, Jacques A; Labelle, Hubert

    2015-07-01

    Variability in surgical strategies for the treatment of adolescent idiopathic scoliosis (AIS) has been demonstrated despite the existence of classifications to guide selection of AIS curves to include in fusion. Decision trees and rule-based algorithms have demonstrated their potential to improve reliability of AIS classification because of their systematic approach and they have also been proposed in algorithms for selection of instrumentation levels in scoliosis. Our working hypothesis is that a rule-based algorithm with a knowledge base extracted from the literature can efficiently output surgical strategies alternatives for a given AIS case. Our objective is to develop a rule-based algorithm based on peer-reviewed literature to output alternative surgical strategies for approach and levels of fusion. A literature search of all English Manuscripts published between 2000 and December 2009 with Pubmed and Google scholar electronic search using the following keywords: "adolescent idiopathic scoliosis" and "surgery" alternatively with "levels of fusion" or "approach". All returned abstracts were screened for contents that could contain rules to include in the knowledge base. A dataset of 1,556 AIS cases treated surgically was used to test the surgical strategy rule-based algorithm (SSRBA) and evaluate how many surgical treatments are covered by the algorithm. The SSRBA was programmed using Matlab. Descriptive statistic was used to evaluate the ability of the rule-based algorithm to cover all treatment alternatives. A SSRBA was successfully developed following Lenke classification's concept that the spine is divided into three curve segments [proximal thoracic (PT), main thoracic (MT) and thoracolumbar/lumbar (TL)]. Each of the 1,556 AIS patients in the dataset was ran through the SSRBA. It proposed an average of 3.78 (±2.06) surgical strategies per case. Overall, the SSRBA is able to match the treatment offered by the surgeon in approach and level of fusion 70

  16. A fuzzy logic-based tool to assess beef cattle ranching sustainability in complex environmental systems.

    PubMed

    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.

  17. An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

    PubMed

    Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin

    2015-07-01

    We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

  18. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

    PubMed

    van Ginneken, Bram

    2017-03-01

    Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.

  19. Rule-based mechanisms of learning for intelligent adaptive flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.; Stengel, Robert F.

    1990-01-01

    How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

  20. Development and Deployment of a Rule-Based Expert System for Autonomous Satellite Monitoring

    NASA Astrophysics Data System (ADS)

    Wong, L.; Kronberg, F.; Hopkins, A.; Machi, F.; Eastham, P.

    In compliance with NASA administrator Daniel Goldin's call for faster, cheaper, better NASA projects, the Center for EUV Astrophysics (CEA) in cooperation with NASA Ames Research Center has developed and deployed a partially autonomous satellite-telemetry monitoring system to monitor the health of the Extreme Ultraviolet Explorer (EUVE) payload. Originally, telemetry was monitored on a 24 hour basis by human operators. Using RTworks, a software package from Talarian Corporation, our development team has developed a rule-based, expert system capable of detecting critical EUVE payload anomalies and notifying an anomaly coordinator. This paper discusses the process of capturing and codifying the knowledge of EUVE operations into rules and how our rule-based system is applied in EUVE autonomous operations.

  1. Spatial Queries Entity Recognition and Disambiguation Using Rule-Based Approach

    NASA Astrophysics Data System (ADS)

    Hamzei, E.; Hakimpour, F.; Forati, A.

    2015-12-01

    In the digital world, search engines have been proposed as one of challenging research areas. One of the main issues in search engines studies is query processing, which its aim is to understand user's needs. If unsuitable spatial query processing approach is employed, the results will be associated with high degree of ambiguity. To evade such degree of ambiguity, in this paper we present a new algorithm which depends on rule-based systems to process queries. Our algorithm is implemented in the three basic steps including: deductively iterative splitting the query; finding candidates for the location names, the location types and spatial relationships; and finally checking the relationships logically and conceptually using a rule based system. As we finally present in the paper using our proposed method have two major advantages: the search engines can provide the capability of spatial analysis based on the specific process and secondly because of its disambiguation technique, user reaches the more desirable result.

  2. Automatic Sleep Scoring Based on Modular Rule-Based Reasoning Units and Signal Processing Units

    DTIC Science & Technology

    2007-11-02

    scoring, rule-based reasoning, multi-staged I. INTRODUCTION Integrated analysis on the state of sleep through Polysomnography is crucial for...diagnosis for sleep related disease. But conventional analog-type Polysomnography systems need tremendous amount of papers and much labor of trained expert...In this sense to equip digital Polysomnography and its following automatic analysis system became trend. In the sleep analysis, sleep stage scoring is

  3. A New Rule-Based System for the Construction and Structural Characterization of Artificial Proteins

    NASA Astrophysics Data System (ADS)

    Štambuk, Nikola; Konjevoda, Paško; Gotovac, Nikola

    In this paper, we present a new rule-based system for an artificial protein design incorporating ternary amino acid polarity (polar, nonpolar, and neutral). It may be used to design de novo α and β protein fold structures and mixed class proteins. The targeted molecules are artificial proteins with important industrial and biomedical applications, related to the development of diagnostic-therapeutic peptide pharmaceuticals, antibody mimetics, peptide vaccines, new nanobiomaterials and engineered protein scaffolds.

  4. Feature- versus rule-based generalization in rats, pigeons and humans.

    PubMed

    Maes, Elisa; De Filippo, Guido; Inkster, Angus B; Lea, Stephen E G; De Houwer, Jan; D'Hooge, Rudi; Beckers, Tom; Wills, Andy J

    2015-11-01

    Humans can spontaneously create rules that allow them to efficiently generalize what they have learned to novel situations. An enduring question is whether rule-based generalization is uniquely human or whether other animals can also abstract rules and apply them to novel situations. In recent years, there have been a number of high-profile claims that animals such as rats can learn rules. Most of those claims are quite weak because it is possible to demonstrate that simple associative systems (which do not learn rules) can account for the behavior in those tasks. Using a procedure that allows us to clearly distinguish feature-based from rule-based generalization (the Shanks-Darby procedure), we demonstrate that adult humans show rule-based generalization in this task, while generalization in rats and pigeons was based on featural overlap between stimuli. In brief, when learning that a stimulus made of two components ("AB") predicts a different outcome than its elements ("A" and "B"), people spontaneously abstract an opposites rule and apply it to new stimuli (e.g., knowing that "C" and "D" predict one outcome, they will predict that "CD" predicts the opposite outcome). Rats and pigeons show the reverse behavior-they generalize what they have learned, but on the basis of similarity (e.g., "CD" is similar to "C" and "D", so the same outcome is predicted for the compound stimulus as for the components). Genuinely rule-based behavior is observed in humans, but not in rats and pigeons, in the current procedure.

  5. Fuzzy-probabilistic multi agent system for breast cancer risk assessment and insurance premium assignment.

    PubMed

    Tatari, Farzaneh; Akbarzadeh-T, Mohammad-R; Sabahi, Ahmad

    2012-12-01

    In this paper, we present an agent-based system for distributed risk assessment of breast cancer development employing fuzzy and probabilistic computing. The proposed fuzzy multi agent system consists of multiple fuzzy agents that benefit from fuzzy set theory to demonstrate their soft information (linguistic information). Fuzzy risk assessment is quantified by two linguistic variables of high and low. Through fuzzy computations, the multi agent system computes the fuzzy probabilities of breast cancer development based on various risk factors. By such ranking of high risk and low risk fuzzy probabilities, the multi agent system (MAS) decides whether the risk of breast cancer development is high or low. This information is then fed into an insurance premium adjuster in order to provide preventive decision making as well as to make appropriate adjustment of insurance premium and risk. This final step of insurance analysis also provides a numeric measure to demonstrate the utility of the approach. Furthermore, actual data are gathered from two hospitals in Mashhad during 1 year. The results are then compared with a fuzzy distributed approach.

  6. Shape representation using a fuzzy morphological thinning algorithm

    NASA Astrophysics Data System (ADS)

    Gupta, Madan M.; Knopf, George K.

    1994-10-01

    information of a vaguely defined object into simplified features for a rule-based description of the object shape.

  7. Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

    NASA Technical Reports Server (NTRS)

    Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

    1988-01-01

    Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

  8. Associations between rule-based parenting practices and child screen viewing: A cross-sectional study.

    PubMed

    Kesten, Joanna M; Sebire, Simon J; Turner, Katrina M; Stewart-Brown, Sarah; Bentley, Georgina; Jago, Russell

    2015-01-01

    Child screen viewing (SV) is positively associated with poor health indicators. Interventions addressing rule-based parenting practices may offer an effective means of limiting SV. This study examined associations between rule-based parenting practices (limit and collaborative rule setting) and SV in 6-8-year old children. An online survey of 735 mothers in 2011 assessed: time that children spent engaged in SV activities; and the use of limit and collaborative rule setting. Logistic regression was used to examine the extent to which limit and collaborative rule setting were associated with SV behaviours. 'Always' setting limits was associated with more TV viewing, computer, smartphone and game-console use and a positive association was found between 'always' setting limits for game-console use and multi-SV (in girls). Associations were stronger in mothers of girls compared to mothers of boys. 'Sometimes' setting limits was associated with more TV viewing. There was no association between 'sometimes' setting limits and computer, game-console or smartphone use. There was a negative association between collaborative rule setting and game-console use in boys. Limit setting is associated with greater SV. Collaborative rule setting may be effective for managing boys' game-console use. More research is needed to understand rule-based parenting practices.

  9. A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics.

    PubMed

    Kaya, Aydın; Can, Ahmet Burak

    2015-08-01

    Predicting malignancy of solitary pulmonary nodules from computer tomography scans is a difficult and important problem in the diagnosis of lung cancer. This paper investigates the contribution of nodule characteristics in the prediction of malignancy. Using data from Lung Image Database Consortium (LIDC) database, we propose a weighted rule based classification approach for predicting malignancy of pulmonary nodules. LIDC database contains CT scans of nodules and information about nodule characteristics evaluated by multiple annotators. In the first step of our method, votes for nodule characteristics are obtained from ensemble classifiers by using image features. In the second step, votes and rules obtained from radiologist evaluations are used by a weighted rule based method to predict malignancy. The rule based method is constructed by using radiologist evaluations on previous cases. Correlations between malignancy and other nodule characteristics and agreement ratio of radiologists are considered in rule evaluation. To handle the unbalanced nature of LIDC, ensemble classifiers and data balancing methods are used. The proposed approach is compared with the classification methods trained on image features. Classification accuracy, specificity and sensitivity of classifiers are measured. The experimental results show that using nodule characteristics for malignancy prediction can improve classification results.

  10. Experiments in knowledge refinement for a large rule-based system

    NASA Astrophysics Data System (ADS)

    Harvey, Wilson A., Jr.; Tambe, Milind

    1993-08-01

    Knowledge-refinement is a central problem in the field of expert systems. For rule-based systems, refinement implies the addition, deletion, and modification of rules in the system so as to improve the system's overall performance. The goal of this research effort is to understand the methodology for refining large rule-based systems, as well as to develop tools that will be useful in refining such systems. The vehicle for our investigation is SPAM, a production system (rule-based system) for the interpretation of aerial imagery. Complex and computation-intensive systems like SPAM impose some unique constraints on knowledge refinement. More specifically, the credit/blame assignment problem for locating pieces of knowledge to refine becomes difficult. Given that constraint, we approach the problem in a bottom-up fashion, i.e., begin by refining portions of SPAM's knowledge base and then attempt to understand the interactions between them. We begin by identifying gaps and/or faults in the knowledge base by comparing SPAM's intermediate output to that of an expert, then modifying the knowledge base so that the system's output more accurately matches the expert's output. While this approach leads to some improvements, it also raises some interesting issues concerning the evaluation of refined knowledge at intermediate levels and of interaction between the refinements. This paper presents our initial efforts toward addressing these issues.

  11. Using reduced rule base with Expert System for the diagnosis of disease in hypertension.

    PubMed

    Başçiftçi, Fatih; Eldem, Ayşe

    2013-12-01

    Hypertension, also called the "Silent Killer", is a dangerous and widespread disease that seriously threatens the health of individuals and communities worldwide, often leading to fatal outcomes such as heart attack, stroke, and renal failure. It affects approximately one billion people worldwide with increasing incidence. In Turkey, over 15 million people have hypertension. In this study, a new Medical Expert System (MES) procedure with reduced rule base was developed to determine hypertension. The aim was to determine the disease by taking all symptoms of hypertension into account in the Medical Expert System (7 symptoms, 2(7) = 128 different conditions). In this new MES procedure, instead of checking all the symptoms, the reduced rule bases were used. In order to get the reduced rule bases, the method of two-level simplification of Boolean functions was used. Through the use of this method, instead of assessing 2(7) = 128 individual conditions by taking 7 symptoms of hypertension into account, reduced cases were evaluated. The average rate of success was 97.6 % with the new MES procedure.

  12. Fuzzy control system for a remote focusing microscope

    NASA Technical Reports Server (NTRS)

    Weiss, Jonathan J.; Tran, Luc P.

    1992-01-01

    Space Station Crew Health Care System procedures require the use of an on-board microscope whose slide images will be transmitted for analysis by ground-based microbiologists. Focusing of microscope slides is low on the list of crew priorities, so NASA is investigating the option of telerobotic focusing controlled by the microbiologist on the ground, using continuous video feedback. However, even at Space Station distances, the transmission time lag may disrupt the focusing process, severely limiting the number of slides that can be analyzed within a given bandwidth allocation. Substantial time could be saved if on-board automation could pre-focus each slide before transmission. The authors demonstrate the feasibility of on-board automatic focusing using a fuzzy logic ruled-based system to bring the slide image into focus. The original prototype system was produced in under two months and at low cost. Slide images are captured by a video camera, then digitized by gray-scale value. A software function calculates an index of 'sharpness' based on gray-scale contrasts. The fuzzy logic rule-based system uses feedback to set the microscope's focusing control in an attempt to maximize sharpness. The systems as currently implemented performs satisfactorily in focusing a variety of slide types at magnification levels ranging from 10 to 1000x. Although feasibility has been demonstrated, the system's performance and usability could be improved substantially in four ways: by upgrading the quality and resolution of the video imaging system (including the use of full color); by empirically defining and calibrating the index of image sharpness; by letting the overall focusing strategy vary depending on user-specified parameters; and by fine-tuning the fuzzy rules, set definitions, and procedures used.

  13. Fuzzy control system for a remote focusing microscope

    NASA Astrophysics Data System (ADS)

    Weiss, Jonathan J.; Tran, Luc P.

    1992-01-01

    Space Station Crew Health Care System procedures require the use of an on-board microscope whose slide images will be transmitted for analysis by ground-based microbiologists. Focusing of microscope slides is low on the list of crew priorities, so NASA is investigating the option of telerobotic focusing controlled by the microbiologist on the ground, using continuous video feedback. However, even at Space Station distances, the transmission time lag may disrupt the focusing process, severely limiting the number of slides that can be analyzed within a given bandwidth allocation. Substantial time could be saved if on-board automation could pre-focus each slide before transmission. The authors demonstrate the feasibility of on-board automatic focusing using a fuzzy logic ruled-based system to bring the slide image into focus. The original prototype system was produced in under two months and at low cost. Slide images are captured by a video camera, then digitized by gray-scale value. A software function calculates an index of 'sharpness' based on gray-scale contrasts. The fuzzy logic rule-based system uses feedback to set the microscope's focusing control in an attempt to maximize sharpness. The systems as currently implemented performs satisfactorily in focusing a variety of slide types at magnification levels ranging from 10 to 1000x. Although feasibility has been demonstrated, the system's performance and usability could be improved substantially in four ways: by upgrading the quality and resolution of the video imaging system (including the use of full color); by empirically defining and calibrating the index of image sharpness; by letting the overall focusing strategy vary depending on user-specified parameters; and by fine-tuning the fuzzy rules, set definitions, and procedures used.

  14. Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy Petri nets.

    PubMed

    Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan

    2013-06-01

    The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.

  15. A Hybrid Approach Using Case-Based Reasoning and Rule-Based Reasoning to Support Cancer Diagnosis: A Pilot Study.

    PubMed

    Saraiva, Renata M; Bezerra, João; Perkusich, Mirko; Almeida, Hyggo; Siebra, Clauirton

    2015-01-01

    Recently there has been an increasing interest in applying information technology to support the diagnosis of diseases such as cancer. In this paper, we present a hybrid approach using case-based reasoning (CBR) and rule-based reasoning (RBR) to support cancer diagnosis. We used symptoms, signs, and personal information from patients as inputs to our model. To form specialized diagnoses, we used rules to define the input factors' importance according to the patient's characteristics. The model's output presents the probability of the patient having a type of cancer. To carry out this research, we had the approval of the ethics committee at Napoleão Laureano Hospital, in João Pessoa, Brazil. To define our model's cases, we collected real patient data at Napoleão Laureano Hospital. To define our model's rules and weights, we researched specialized literature and interviewed health professional. To validate our model, we used K-fold cross validation with the data collected at Napoleão Laureano Hospital. The results showed that our approach is an effective CBR system to diagnose cancer.

  16. Fuzzy forecasting based on fuzzy-trend logical relationship groups.

    PubMed

    Chen, Shyi-Ming; Wang, Nai-Yi

    2010-10-01

    In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

  17. Statistical multistate gate tracker with fuzzy intelligent tracking tactics

    NASA Astrophysics Data System (ADS)

    Liu, Zhili

    1990-08-01

    By analyzing the tracking scenario thoroughly, the gate tracking scenario may fall into four states:(Si) Common tracking state:main state in which the target moves smoothly and there is little disturbance of foreground and background. (52) the target unstable state in the gate:the target violent movement and the target existence in strong disturbances. (83)The taret is escaping from the tracking gate. (S4)the target is completely Iost.Fuzzy mathematics is introduced to describe those four states and transforms among them.The finite state fuzzy automachine is formed. Several statistical parameters have been proved to be very effective to describe the four states by mean of fuzzy concepts. The automachine can provide both the fuzzy probabilities of four tracking states and the transform matrix among them. The elements of the matrix which present the transform probabilities among the four states are set up by the combination of statistical parameters. The fuzzy automachine can provide the most probable state decision to guide the multi-state gate tracker to chose the proper tracking tactics at any time. The different tracking algorithm can be applied to different state in order to keep on tracking under both the common tracking situation and uncommon tracking situation6.

  18. Parallel optical fuzzy logic inference using improved fuzzy associative memories

    NASA Astrophysics Data System (ADS)

    Zhang, ShuQun; Karim, Mohammad A.

    1999-10-01

    A new optoelectronic fuzzy inference system is proposed for processing a large number of fuzzy rules in parallel. The proposed system using spatial light modulator implements various membership functions as well as max-min inference. It has the features of easy implementation and large data processing capability. The membership function decomposition method in the improved fuzzy associative memory is used to save both space bandwidth and accommodate multiple-input fuzzy inference.

  19. Heterogeneous fuzzy logic networks: fundamentals and development studies.

    PubMed

    Pedrycz, Witold

    2004-11-01

    The recent trend in the development of neurofuzzy systems has profoundly emphasized the importance of synergy between the fundamentals of fuzzy sets and neural networks. The resulting frameworks of the neurofuzzy systems took advantage of an array of learning mechanisms primarily originating within the theory of neurocomputing and the use of fuzzy models (predominantly rule-based systems) being well established in the realm of fuzzy sets. Ideally, one can anticipate that neurofuzzy systems should fully exploit the linkages between these two technologies while strongly preserving their evident identities (plasticity or learning abilities to be shared by the transparency and full interpretability of the resulting neurofuzzy constructs). Interestingly, this synergy still becomes a target yet to be satisfied. This study is an attempt to address the fundamental interpretability challenge of neurofuzzy systems. Our underlying conjecture is that the transparency of any neurofuzzy system links directly with the logic fabric of the system so the logic fundamentals of the underlying architecture become of primordial relevance. Having this in mind the development of neurofuzzy models hinges on a collection of logic driven processing units named here fuzzy (logic) neurons. These are conceptually simple logic-oriented elements that come with a well-defined semantics and plasticity. Owing to their diversity, such neurons form essential building blocks of the networks. The study revisits the existing categories of logic neurons, provides with their taxonomy, helps understand their functional features and sheds light on their behavior when being treated as computational components of any neurofuzzy architecture. The two main categories of aggregative and reference neurons are deeply rooted in the fundamental operations encountered in the technology of fuzzy sets (including logic operations, linguistic modifiers, and logic reference operations). The developed heterogeneous networks

  20. URC Fuzzy Modeling and Simulation of Gene Regulation

    SciTech Connect

    Sokhansanj, B A; Fitch, J P

    2001-05-01

    Recent technological advances in high-throughput data collection give biologists the ability to study increasingly complex systems. A new methodology is needed to develop and test biological models based on experimental observations and predict the effect of perturbations of the network (e.g. genetic engineering, pharmaceuticals, gene therapy). Diverse modeling approaches have been proposed, in two general categories: modeling a biological pathway as (a) a logical circuit or (b) a chemical reaction network. Boolean logic models can not represent necessary biological details. Chemical kinetics simulations require large numbers of parameters that are very difficult to accurately measure. Based on the way biologists have traditionally thought about systems, we propose that fuzzy logic is a natural language for modeling biology. The Union Rule Configuration (URC) avoids combinatorial explosion in the fuzzy rule base, allowing complex system models. We demonstrate the fuzzy modeling method on the commonly studied lac operon of E. coli. Our goal is to develop a modeling and simulation approach that can be understood and applied by biologists without the need for experts in other fields or ''black-box'' software.

  1. Component Models for Fuzzy Data

    ERIC Educational Resources Information Center

    Coppi, Renato; Giordani, Paolo; D'Urso, Pierpaolo

    2006-01-01

    The fuzzy perspective in statistical analysis is first illustrated with reference to the "Informational Paradigm" allowing us to deal with different types of uncertainties related to the various informational ingredients (data, model, assumptions). The fuzzy empirical data are then introduced, referring to "J" LR fuzzy variables as observed on "I"…

  2. Fuzzy control system design via fuzzy Lyapunov functions.

    PubMed

    Li, J; Zhou, S; Xu, S

    2008-12-01

    This correspondence deals with the problems of analysis and design for a class of continuous-time Takagi-Sugeno fuzzy control systems. Sufficient conditions for the stability of fuzzy control systems are derived based on a fuzzy Lyapunov function. Both parallel and nonparallel distributed compensation controllers are considered. Sufficient conditions for the solvability of the controller design problem are given in the form of linear matrix inequalities. Unlike the fuzzy Lyapunov function approaches reported in the literature, the bound of the time derivatives of the fuzzy basis functions is not required in the proposed approaches. The effectiveness of the proposed approaches is shown through a numerical example.

  3. Measuring Distance of Fuzzy Numbers by Trapezoidal Fuzzy Numbers

    NASA Astrophysics Data System (ADS)

    Hajjari, Tayebeh

    2010-11-01

    Fuzzy numbers and more generally linguistic values are approximate assessments, given by experts and accepted by decision-makers when obtaining value that is more accurate is impossible or unnecessary. Distance between two fuzzy numbers plays an important role in linguistic decision-making. It is reasonable to define a fuzzy distance between fuzzy objects. To achieve this aim, the researcher presents a new distance measure for fuzzy numbers by means of improved centroid distance method. The metric properties are also studied. The advantage is the calculation of the proposed method is far simple than previous approaches.

  4. Compensatory processing during rule-based category learning in older adults.

    PubMed

    Bharani, Krishna L; Paller, Ken A; Reber, Paul J; Weintraub, Sandra; Yanar, Jorge; Morrison, Robert G

    2016-01-01

    Healthy older adults typically perform worse than younger adults at rule-based category learning, but better than patients with Alzheimer's or Parkinson's disease. To further investigate aging's effect on rule-based category learning, we monitored event-related potentials (ERPs) while younger and neuropsychologically typical older adults performed a visual category-learning task with a rule-based category structure and trial-by-trial feedback. Using these procedures, we previously identified ERPs sensitive to categorization strategy and accuracy in young participants. In addition, previous studies have demonstrated the importance of neural processing in the prefrontal cortex and the medial temporal lobe for this task. In this study, older adults showed lower accuracy and longer response times than younger adults, but there were two distinct subgroups of older adults. One subgroup showed near-chance performance throughout the procedure, never categorizing accurately. The other subgroup reached asymptotic accuracy that was equivalent to that in younger adults, although they categorized more slowly. These two subgroups were further distinguished via ERPs. Consistent with the compensation theory of cognitive aging, older adults who successfully learned showed larger frontal ERPs when compared with younger adults. Recruitment of prefrontal resources may have improved performance while slowing response times. Additionally, correlations of feedback-locked P300 amplitudes with category-learning accuracy differentiated successful younger and older adults. Overall, the results suggest that the ability to adapt one's behavior in response to feedback during learning varies across older individuals, and that the failure of some to adapt their behavior may reflect inadequate engagement of prefrontal cortex.

  5. Evaluation of a rule base for decision making in general practice.

    PubMed Central

    Essex, B; Healy, M

    1994-01-01

    BACKGROUND. Decision making in general practice relies heavily on judgmental expertise. It should be possible to codify this expertise into rules and principles. AIM. A study was undertaken to evaluate the effectiveness, of rules from a rule base designed to improve students' and trainees' management decisions relating to patients seen in general practice. METHOD. The rule base was developed after studying decisions about and management of thousands of patients seen in one general practice over an eight year period. Vignettes were presented to 93 fourth year medical students and 179 general practitioner trainees. They recorded their perception and management of each case before and after being presented with a selection of relevant rules. Participants also commented on their level of agreement with each of the rules provided with the vignettes. A panel of five independent assessors then rated as good, acceptable or poor, the participants' perception and management of each case before and after seeing the rules. RESULTS. Exposure to a few selected rules of thumb improved the problem perception and management decisions of both undergraduates and trainees. The degree of improvement was not related to previous experience or to the stated level of agreement with the proposed rules. The assessors identified difficulties students and trainees experienced in changing their perceptions and management decisions when the rules suggested options they had not considered. CONCLUSION. The rules developed to improve decision making skills in general practice are effective when used with vignettes. The next phase is to transform the rule base into an expert system to train students and doctors to acquire decision making skills. It could also be used to provide decision support when confronted with difficult management decisions in general practice. PMID:8204334

  6. Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

    PubMed Central

    Chylek, Lily A.; Harris, Leonard A.; Tung, Chang-Shung; Faeder, James R.; Lopez, Carlos F.

    2013-01-01

    Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and post-translational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation). PMID:24123887

  7. Compensatory Processing During Rule-Based Category Learning in Older Adults

    PubMed Central

    Bharani, Krishna L.; Paller, Ken A.; Reber, Paul J.; Weintraub, Sandra; Yanar, Jorge; Morrison, Robert G.

    2016-01-01

    Healthy older adults typically perform worse than younger adults at rule-based category learning, but better than patients with Alzheimer's or Parkinson's disease. To further investigate aging's effect on rule-based category learning, we monitored event-related potentials (ERPs) while younger and neuropsychologically typical older adults performed a visual category-learning task with a rule-based category structure and trial-by-trial feedback. Using these procedures, we previously identified ERPs sensitive to categorization strategy and accuracy in young participants. In addition, previous studies have demonstrated the importance of neural processing in the prefrontal cortex and the medial temporal lobe for this task. In this study, older adults showed lower accuracy and longer response times than younger adults, but there were two distinct subgroups of older adults. One subgroup showed near-chance performance throughout the procedure, never categorizing accurately. The other subgroup reached asymptotic accuracy that was equivalent to that in younger adults, although they categorized more slowly. These two subgroups were further distinguished via ERPs. Consistent with the compensation theory of cognitive aging, older adults who successfully learned showed larger frontal ERPs when compared with younger adults. Recruitment of prefrontal resources may have improved performance while slowing response times. Additionally, correlations of feedback-locked P300 amplitudes with category-learning accuracy differentiated successful younger and older adults. Overall, the results suggest that the ability to adapt one's behavior in response to feedback during learning varies across older individuals, and that the failure of some to adapt their behavior may reflect inadequate engagement of prefrontal cortex. PMID:26422522

  8. Specification, annotation, visualization and simulation of a large rule-based model for ERBB receptor signaling

    PubMed Central

    2012-01-01

    Background Mathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions. Results Here, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map. Conclusions With the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions

  9. Description of a Rule-based System for the i2b2 Challenge in Natural Language Processing for Clinical Data

    PubMed Central

    Childs, Lois C.; Enelow, Robert; Simonsen, Lone; Heintzelman, Norris H.; Kowalski, Kimberly M.; Taylor, Robert J.

    2009-01-01

    The Obesity Challenge, sponsored by Informatics for Integrating Biology and the Bedside (i2b2), a National Center for Biomedical Computing, asked participants to build software systems that could “read” a patient's clinical discharge summary and replicate the judgments of physicians in evaluating presence or absence of obesity and 15 comorbidities. The authors describe their methodology and discuss the results of applying Lockheed Martin's rule-based natural language processing (NLP) capability, ClinREAD. We tailored ClinREAD with medical domain expertise to create assigned default judgments based on the most probable results as defined in the ground truth. It then used rules to collect evidence similar to the evidence that the human judges likely relied upon, and applied a logic module to weigh the strength of all evidence collected to arrive at final judgments. The Challenge results suggest that rule-based systems guided by human medical expertise are capable of solving complex problems in machine processing of medical text. PMID:19390103

  10. Rule-based medical device adaptation for the digital operating room.

    PubMed

    Franke, Stefan; Neumuth, Thomas

    2015-08-01

    A workflow-driven cooperative operating room needs to be established in order to successfully unburden the surgeon and the operating room staff very time-consuming information-seeking and configuration tasks. We propose an approach towards the integration of intraoperative surgical workflow management and integration technologies. The concept of rule-based behavior is adapted to situation-aware medical devices. A prototype was implemented and experiments with sixty recorded brain tumor removal procedures were conducted to test the proposed approach. An analysis of the recordings indicated numerous applications, such as automatic display configuration, room light adaptation and pre-configuration of medical devices and systems.

  11. A conceptual model to empower software requirements conflict detection and resolution with rule-based reasoning

    NASA Astrophysics Data System (ADS)

    Ahmad, Sabrina; Jalil, Intan Ermahani A.; Ahmad, Sharifah Sakinah Syed

    2016-08-01

    It is seldom technical issues which impede the process of eliciting software requirements. The involvement of multiple stakeholders usually leads to conflicts and therefore the need of conflict detection and resolution effort is crucial. This paper presents a conceptual model to further improve current efforts. Hence, this paper forwards an improved conceptual model to assist the conflict detection and resolution effort which extends the model ability and improves overall performance. The significant of the new model is to empower the automation of conflicts detection and its severity level with rule-based reasoning.

  12. A rule-based approach to model checking of UML state machines

    NASA Astrophysics Data System (ADS)

    Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz

    2016-12-01

    In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.

  13. Rule-based induction method for haplotype comparison and identification of candidate disease loci.

    PubMed

    Karinen, Sirkku; Saarinen, Silva; Lehtonen, Rainer; Rastas, Pasi; Vahteristo, Pia; Aaltonen, Lauri A; Hautaniemi, Sampsa

    2012-03-19

    There is a need for methods that are able to identify rare variants that cause low or moderate penetrance disease susceptibility. To answer this need, we introduce a rule-based haplotype comparison method, Haplous, which identifies haplotypes within multiple samples from phased genotype data and compares them within and between sample groups. We demonstrate that Haplous is able to accurately identify haplotypes that are identical by descent, exclude common haplotypes in the studied population and select rare haplotypes from the data. Our analysis of three families with multiple individuals affected by lymphoma identified several interesting haplotypes shared by distantly related patients.

  14. Rule-based induction method for haplotype comparison and identification of candidate disease loci

    PubMed Central

    2012-01-01

    There is a need for methods that are able to identify rare variants that cause low or moderate penetrance disease susceptibility. To answer this need, we introduce a rule-based haplotype comparison method, Haplous, which identifies haplotypes within multiple samples from phased genotype data and compares them within and between sample groups. We demonstrate that Haplous is able to accurately identify haplotypes that are identical by descent, exclude common haplotypes in the studied population and select rare haplotypes from the data. Our analysis of three families with multiple individuals affected by lymphoma identified several interesting haplotypes shared by distantly related patients. PMID:22429919

  15. A Rule-Based Expert System as an Integrated Resource in an Outpatient Clinic Information System

    PubMed Central

    Wilton, Richard

    1990-01-01

    A rule-based expert system can be integrated in a useful way into a microcomputer-based clinical information system by using symmetric data-communication methods and intuitive user-interface design. To users of the computer system, the expert system appears as one of several distributed information resources, among which are database management systems and a gateway to a mainframe computing system. Transparent access to the expert system is based on the use of both commercial and public-domain data-communication standards.

  16. HIV-GRADE: a publicly available, rules-based drug resistance interpretation algorithm integrating bioinformatic knowledge.

    PubMed

    Obermeier, Martin; Pironti, Alejandro; Berg, Thomas; Braun, Patrick; Däumer, Martin; Eberle, Josef; Ehret, Robert; Kaiser, Rolf; Kleinkauf, Niels; Korn, Klaus; Kücherer, Claudia; Müller, Harm; Noah, Christian; Stürmer, Martin; Thielen, Alexander; Wolf, Eva; Walter, Hauke

    2012-01-01

    Genotypic drug resistance testing provides essential information for guiding treatment in HIV-infected patients. It may either be used for identifying patients with transmitted drug resistance or to clarify reasons for treatment failure and to check for remaining treatment options. While different approaches for the interpretation of HIV sequence information are already available, no other available rules-based systems specifically have looked into the effects of combinations of drugs. HIV-GRADE (Genotypischer Resistenz Algorithmus Deutschland) was planned as a countrywide approach to establish standardized drug resistance interpretation in Germany and also to introduce rules for estimating the influence of mutations on drug combinations. The rules for HIV-GRADE are taken from the literature, clinical follow-up data and from a bioinformatics-driven interpretation system (geno2pheno([resistance])). HIV-GRADE presents the option of seeing the rules and results of other drug resistance algorithms for a given sequence simultaneously. The HIV-GRADE rules-based interpretation system was developed by the members of the HIV-GRADE registered society. For continuous updates, this expert committee meets twice a year to analyze data from various sources. Besides data from clinical studies and the centers involved, published correlations for mutations with drug resistance and genotype-phenotype correlation data information from the bioinformatic models of geno2pheno are used to generate the rules for the HIV-GRADE interpretation system. A freely available online tool was developed on the basis of the Stanford HIVdb rules interpretation tool using the algorithm specification interface. Clinical validation of the interpretation system was performed on the data of treatment episodes consisting of sequence information, antiretroviral treatment and viral load, before and 3 months after treatment change. Data were analyzed using multiple linear regression. As the developed online

  17. Distinct pathways for rule-based retrieval and spatial mapping of memory representations in hippocampal neurons

    PubMed Central

    Navawongse, Rapeechai; Eichenbaum, Howard

    2013-01-01

    Hippocampal neurons encode events within the context in which they occurred, a fundamental feature of episodic memory. Here we explored the sources of event and context information represented by hippocampal neurons during the retrieval of object associations in rats. Temporary inactivation of the medial prefrontal cortex differentially reduced the selectivity of rule-based object associations represented by hippocampal neuronal firing patterns but did not affect spatial firing patterns. By contrast, inactivation of the medial entorhinal cortex resulted in a pervasive reorganization of hippocampal mappings of spatial context and events. These results suggest distinct and cooperative prefrontal and medial temporal mechanisms in memory representation. PMID:23325238

  18. Traditional Versus Rule-Based Programming Techniques: Application To The Control Of Optional Flight Information

    NASA Astrophysics Data System (ADS)

    Ricks, Wendell R.; Abbott, Kathy H.

    1987-05-01

    To the software design community, the concern over the costs associated with a program's execution time and implementation is great. It is always desirable, and sometimes imperative, that the proper programming technique is chosen which minimizes all costs for a given application or type of application. This paper describes a study that compared the cost-related factors associated with traditional programming techniques to rale-based programming techniques for a specific application. The results of this study favored the traditional approach regarding execution efficiency, but favored the rule-based approach regarding programmer productivity (implementation ease). Although this study examined a specific application, the resuli:s should be widely applicable.

  19. Distributed fuzzy system modeling

    SciTech Connect

    Pedrycz, W.; Chi Fung Lam, P.; Rocha, A.F.

    1995-05-01

    The paper introduces and studies an idea of distributed modeling treating it as a new paradigm of fuzzy system modeling and analysis. This form of modeling is oriented towards developing individual (local) fuzzy models for specific modeling landmarks (expressed as fuzzy sets) and determining the essential logical relationships between these local models. The models themselves are implemented in the form of logic processors being regarded as specialized fuzzy neural networks. The interaction between the processors is developed either in an inhibitory or excitatory way. In more descriptive way, the distributed model can be sought as a collection of fuzzy finite state machines with their individual local first or higher order memories. It is also clarified how the concept of distributed modeling narrows down a gap between purely numerical (quantitative) models and the qualitative ones originated within the realm of Artificial Intelligence. The overall architecture of distributed modeling is discussed along with the detailed learning schemes. The results of extensive simulation experiments are provided as well. 17 refs.

  20. Towards autonomous fuzzy control

    NASA Technical Reports Server (NTRS)

    Shenoi, Sujeet; Ramer, Arthur

    1993-01-01

    The efficient implementation of on-line adaptation in real time is an important research problem in fuzzy control. The goal is to develop autonomous self-organizing controllers employing system-independent control meta-knowledge which enables them to adjust their control policies depending on the systems they control and the environments in which they operate. An autonomous fuzzy controller would continuously observe system behavior while implementing its control actions and would use the outcomes of these actions to refine its control policy. It could be designed to lie dormant when its control actions give rise to adequate performance characteristics but could rapidly and autonomously initiate real-time adaptation whenever its performance degrades. Such an autonomous fuzzy controller would have immense practical value. It could accommodate individual variations in system characteristics and also compensate for degradations in system characteristics caused by wear and tear. It could also potentially deal with black-box systems and control scenarios. On-going research in autonomous fuzzy control is reported. The ultimate research objective is to develop robust and relatively inexpensive autonomous fuzzy control hardware suitable for use in real time environments.

  1. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    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.

  2. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    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.

  3. Predicting the relatiave vulnerability of near-coastal species to climate change using a rule-based ecoinformatics approach

    EPA Science Inventory

    Background/Questions/Methods Near-coastal species are threatened by multiple climate change drivers, including temperature increases, ocean acidification, and sea level rise. To identify vulnerable habitats, geographic regions, and species, we developed a sequential, rule-based...

  4. Predicting the relatiave vulnerability of near-coastal species to climate change using a rule-based ecoinformatics approach

    EPA Science Inventory

    Background/Questions/Methods Near-coastal species are threatened by multiple climate change drivers, including temperature increases, ocean acidification, and sea level rise. To identify vulnerable habitats, geographic regions, and species, we developed a sequential, rule-based...

  5. Crop parameters estimation by fuzzy inference system using X-band scatterometer data

    NASA Astrophysics Data System (ADS)

    Pandey, Abhishek; Prasad, R.; Singh, V. P.; Jha, S. K.; Shukla, K. K.

    2013-03-01

    Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.

  6. Fuzzy Diagnostic System for Oleo-Pneumatic Drive Mechanism of High-Voltage Circuit Breakers

    PubMed Central

    2013-01-01

    Many oil-based high-voltage circuit breakers are still in use in national power networks of developing countries, like those in Eastern Europe. Changing these breakers with new more reliable ones is not an easy task, due to their implementing costs. The acting device, called oleo-pneumatic mechanism (MOP), presents the highest fault rate from all components of circuit breaker. Therefore, online predictive diagnosis and early detection of the MOP fault tendencies are very important for their good functioning state. In this paper, fuzzy logic approach is used for the diagnosis of MOP-type drive mechanisms. Expert rules are generated to estimate the MOP functioning state, and a fuzzy system is proposed for predictive diagnosis. The fuzzy inputs give information about the number of starts and time of functioning per hour, in terms of short-term components, and their mean values. Several fuzzy systems were generated, using different sets of membership functions and rule bases, and their output performances are studied. Simulation results are presented based on an input data set, which contains hourly records of operating points for a time horizon of five years. The fuzzy systems work well, making an early detection of the MOP fault tendencies. PMID:24319349

  7. Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control.

    PubMed

    Mahfouf, M; Abbod, M F; Linkens, D A

    2003-01-01

    Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms.

  8. PC based speed control of dc motor using fuzzy logic controller

    SciTech Connect

    Mandal, S.K.; Kanphade, R.D.; Lavekar, K.P.

    1998-07-01

    The dc motor is extensively used as constant speed drive in textile mills, paper mills, printing press, etc.. If the load and supply voltage are time varying, the speed will be changed. Since last few decades the conventional PID controllers are used to maintain the constant speed by controlling the duty ratio of Chopper. Generally, four quadrant chopper is used for regenerative braking and reverse motoring operation. Fuzzy Logic is newly introduced in control system. Fuzzy Control is based on Fuzzy Logic, a logical system which is too much closer in spirit to human thinking and natural language. The Fuzzy Logic Controller (FLC) provides a linguistic control strategy based on knowledge base of the system. Firstly, the machine is started very smoothly from zero to reference speed in the proposed scheme by increasing the duty ratio. Then change and rate of change of speed (dN, dN/dt), change and rate of change input voltage (dV, dV/dt) and load current are input to FLC. The new value of duty ratio is determined from the Fuzzy rule base and defuzzification method. The chopper will be 'ON' according to new duty ratio to maintain the constant speed. The dynamic and steady state performance of the proposed system is better than conventional control system. In this paper mathematical simulation and experimental implementation are carried out to investigate the drive performance.

  9. Fuzzy diagnostic system for oleo-pneumatic drive mechanism of high-voltage circuit breakers.

    PubMed

    Nicolau, Viorel

    2013-01-01

    Many oil-based high-voltage circuit breakers are still in use in national power networks of developing countries, like those in Eastern Europe. Changing these breakers with new more reliable ones is not an easy task, due to their implementing costs. The acting device, called oleo-pneumatic mechanism (MOP), presents the highest fault rate from all components of circuit breaker. Therefore, online predictive diagnosis and early detection of the MOP fault tendencies are very important for their good functioning state. In this paper, fuzzy logic approach is used for the diagnosis of MOP-type drive mechanisms. Expert rules are generated to estimate the MOP functioning state, and a fuzzy system is proposed for predictive diagnosis. The fuzzy inputs give information about the number of starts and time of functioning per hour, in terms of short-term components, and their mean values. Several fuzzy systems were generated, using different sets of membership functions and rule bases, and their output performances are studied. Simulation results are presented based on an input data set, which contains hourly records of operating points for a time horizon of five years. The fuzzy systems work well, making an early detection of the MOP fault tendencies.

  10. A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System.

    PubMed

    Tang, Yongchuan; Zhou, Deyun; Jiang, Wen

    2016-01-01

    In order to realize the stability control of the planar inverted pendulum system, which is a typical multi-variable and strong coupling system, a new fuzzy-evidential controller based on fuzzy inference and evidential reasoning is proposed. Firstly, for each axis, a fuzzy nine-point controller for the rod and a fuzzy nine-point controller for the cart are designed. Then, in order to coordinate these two controllers of each axis, a fuzzy-evidential coordinator is proposed. In this new fuzzy-evidential controller, the empirical knowledge for stabilization of the planar inverted pendulum system is expressed by fuzzy rules, while the coordinator of different control variables in each axis is built incorporated with the dynamic basic probability assignment (BPA) in the frame of fuzzy inference. The fuzzy-evidential coordinator makes the output of the control variable smoother, and the control effect of the new controller is better compared with some other work. The experiment in MATLAB shows the effectiveness and merit of the proposed method.

  11. A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System

    PubMed Central

    Tang, Yongchuan; Zhou, Deyun

    2016-01-01

    In order to realize the stability control of the planar inverted pendulum system, which is a typical multi-variable and strong coupling system, a new fuzzy-evidential controller based on fuzzy inference and evidential reasoning is proposed. Firstly, for each axis, a fuzzy nine-point controller for the rod and a fuzzy nine-point controller for the cart are designed. Then, in order to coordinate these two controllers of each axis, a fuzzy-evidential coordinator is proposed. In this new fuzzy-evidential controller, the empirical knowledge for stabilization of the planar inverted pendulum system is expressed by fuzzy rules, while the coordinator of different control variables in each axis is built incorporated with the dynamic basic probability assignment (BPA) in the frame of fuzzy inference. The fuzzy-evidential coordinator makes the output of the control variable smoother, and the control effect of the new controller is better compared with some other work. The experiment in MATLAB shows the effectiveness and merit of the proposed method. PMID:27482707

  12. Reconfigurable fuzzy cell

    NASA Technical Reports Server (NTRS)

    Salazar, George A. (Inventor)

    1993-01-01

    This invention relates to a reconfigurable fuzzy cell comprising a digital control programmable gain operation amplifier, an analog-to-digital converter, an electrically erasable PROM, and 8-bit counter and comparator, and supporting logic configured to achieve in real-time fuzzy systems high throughput, grade-of-membership or membership-value conversion of multi-input sensor data. The invention provides a flexible multiplexing-capable configuration, implemented entirely in hardware, for effectuating S-, Z-, and PI-membership functions or combinations thereof, based upon fuzzy logic level-set theory. A membership value table storing 'knowledge data' for each of S-, Z-, and PI-functions is contained within a nonvolatile memory for storing bits of membership and parametric information in a plurality of address spaces. Based upon parametric and control signals, analog sensor data is digitized and converted into grade-of-membership data. In situ learn and recognition modes of operation are also provided.

  13. Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework.

    PubMed

    Papageorgiou, Elpiniki; Stylios, Chrysostomos; Groumpos, Peter

    2007-01-01

    Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy Cognitive Maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and/or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques.

  14. Rule Based Category Learning in Patients with Parkinson’s Disease

    PubMed Central

    Price, Amanda; Filoteo, J. Vincent; Maddox, W. Todd

    2009-01-01

    Measures of explicit rule-based category learning are commonly used in neuropsychological evaluation of individuals with Parkinson’s disease (PD) and the pattern of PD performance on these measures tends to be highly varied. We review the neuropsychological literature to clarify the manner in which PD affects the component processes of rule-based category learning and work to identify and resolve discrepancies within this literature. In particular, we address the manner in which PD and its common treatments affect the processes of rule generation, maintenance, shifting and selection. We then integrate the neuropsychological research with relevant neuroimaging and computational modeling evidence to clarify the neurobiological impact of PD on each process. Current evidence indicates that neurochemical changes associated with PD primarily disrupt rule shifting, and may disturb feedback-mediated learning processes that guide rule selection. Although surgical and pharmacological therapies remediate this deficit, it appears that the same treatments may contribute to impaired rule generation, maintenance and selection processes. These data emphasize the importance of distinguishing between the impact of PD and its common treatments when considering the neuropsychological profile of the disease. PMID:19428385

  15. A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems

    NASA Technical Reports Server (NTRS)

    Sartori, Michael A.; Passino, Kevin M.; Antsaklis, Panos J.

    1992-01-01

    In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.

  16. Rules-based detection of discrepancies between TSH and free T4 results.

    PubMed

    Mitchell, Douglas R; Parvin, Curtis A; Gronowski, Ann M

    2003-06-01

    Analytical errors in clinical laboratory testing are unavoidable. Recent reports have suggested the idea of "physiological profiling" which uses several results from a given patient to identify clinically unlikely results. The objective of this study was to establish rules-based criteria for identifying physiologically unlikely TSH and free T(4) (fT(4)) results. For a 30-month period, all samples with fT(4) concentrations >2 ng/dl and TSH concentrations >0.1 micro IU/ml were investigated. Among 7918 plasma samples for which both TSH and fT(4) concentrations were measured, 18 (0.23%) had fT(4) and TSH exceeding the investigated limits. Of these, two were due to heterophile antibody interference (with the TSH assay), one was proven to be due to random error, four could be explained by the patients' conditions, three were from infants <1 week of age, and the remaining eight were unresolved, primarily due to insufficient sample, discarded samples, and inability to obtain patient histories. This study defined a rules-based alert system for clinically unlikely combinations of TSH and fT(4) results. This pilot study demonstrates that this system is capable of detecting at least two different types of laboratory errors that would have otherwise gone undetected.

  17. Automated detection of pain from facial expressions: a rule-based approach using AAM

    NASA Astrophysics Data System (ADS)

    Chen, Zhanli; Ansari, Rashid; Wilkie, Diana J.

    2012-02-01

    In this paper, we examine the problem of using video analysis to assess pain, an important problem especially for critically ill, non-communicative patients, and people with dementia. We propose and evaluate an automated method to detect the presence of pain manifested in patient videos using a unique and large collection of cancer patient videos captured in patient homes. The method is based on detecting pain-related facial action units defined in the Facial Action Coding System (FACS) that is widely used for objective assessment in pain analysis. In our research, a person-specific Active Appearance Model (AAM) based on Project-Out Inverse Compositional Method is trained for each patient individually for the modeling purpose. A flexible representation of the shape model is used in a rule-based method that is better suited than the more commonly used classifier-based methods for application to the cancer patient videos in which pain-related facial actions occur infrequently and more subtly. The rule-based method relies on the feature points that provide facial action cues and is extracted from the shape vertices of AAM, which have a natural correspondence to face muscular movement. In this paper, we investigate the detection of a commonly used set of pain-related action units in both the upper and lower face. Our detection results show good agreement with the results obtained by three trained FACS coders who independently reviewed and scored the action units in the cancer patient videos.

  18. Organizational Knowledge Transfer Using Ontologies and a Rule-Based System

    NASA Astrophysics Data System (ADS)

    Okabe, Masao; Yoshioka, Akiko; Kobayashi, Keido; Yamaguchi, Takahira

    In recent automated and integrated manufacturing, so-called intelligence skill is becoming more and more important and its efficient transfer to next-generation engineers is one of the urgent issues. In this paper, we propose a new approach without costly OJT (on-the-job training), that is, combinational usage of a domain ontology, a rule ontology and a rule-based system. Intelligence skill can be decomposed into pieces of simple engineering rules. A rule ontology consists of these engineering rules as primitives and the semantic relations among them. A domain ontology consists of technical terms in the engineering rules and the semantic relations among them. A rule ontology helps novices get the total picture of the intelligence skill and a domain ontology helps them understand the exact meanings of the engineering rules. A rule-based system helps domain experts externalize their tacit intelligence skill to ontologies and also helps novices internalize them. As a case study, we applied our proposal to some actual job at a remote control and maintenance office of hydroelectric power stations in Tokyo Electric Power Co., Inc. We also did an evaluation experiment for this case study and the result supports our proposal.

  19. The relevance of a rules-based maize marketing policy: an experimental case study of Zambia.

    PubMed

    Abbink, Klaus; Jayne, Thomas S; Moller, Lars C

    2011-01-01

    Strategic interaction between public and private actors is increasingly recognised as an important determinant of agricultural market performance in Africa and elsewhere. Trust and consultation tends to positively affect private activity while uncertainty of government behaviour impedes it. This paper reports on a laboratory experiment based on a stylised model of the Zambian maize market. The experiment facilitates a comparison between discretionary interventionism and a rules-based policy in which the government pre-commits itself to a future course of action. A simple precommitment rule can, in theory, overcome the prevailing strategic dilemma by encouraging private sector participation. Although this result is also borne out in the economic experiment, the improvement in private sector activity is surprisingly small and not statistically significant due to irrationally cautious choices by experimental governments. Encouragingly, a rules-based policy promotes a much more stable market outcome thereby substantially reducing the risk of severe food shortages. These results underscore the importance of predictable and transparent rules for the state's involvement in agricultural markets.

  20. Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree.

    PubMed

    Karar, Mohamed Esmail; El-Khafif, Sahar H; El-Brawany, Mohamed A

    2017-04-01

    In order to assist the diagnosis procedure of heart sound signals, this paper presents a new automated method for classifying the heart status using a rule-based classification tree into normal and three abnormal cases; namely the aortic valve stenosis, aortic insufficient, and ventricular septum defect. The developed method includes three main steps as follows. First, one cycle of the heart sound signals is automatically detected and segmented based on time properties of the heart signals. Second, the segmented cycle is preprocessed with the discrete wavelet transform and then largest Lyapunov exponents are calculated to generate the dynamical features of heart sound time series. Finally, a rule-based classification tree is fed by these Lyapunov exponents to give the final decision of the heart health status. The developed method has been tested successfully on twenty-two datasets of normal heart sounds and murmurs with success rate of 95.5%. The resulting error can be easily corrected by modifying the classification rules; consequently, the accuracy of automated heart sounds diagnosis is further improved.

  1. Seizure prediction in intracranial EEG: a patient-specific rule-based approach.

    PubMed

    Aarabi, Ardalan; He, Bin

    2011-01-01

    In this study, we report our development of a patient-specific rule-based seizure prediction system. Five univariate and one bivariate nonlinear measures were extracted from non-overlapping 10-second segments of intracranial EEG (iEEG) data recorded using both depth electrodes in the brain and subdural electrodes over the cortical surface. Nonlinear features representing the specific characteristic properties of EEG signal were then integrated spatio-temporally in a way to predict to predict seizure with high sensitivity. The present system was tested on 58 hours of iEEG data containing ten seizures recorded in two patients with medically intractable focal epilepsy. Within a prediction horizon of 30 and 60 minutes, our method showed an average sensitivity of 90% and 96.5% with an average false prediction rate of 0.06/h and 0.055/h, respectively. The present results suggest that such a rule-based system can become potentially a useful approach for predicting seizures prior to onset.

  2. RB-ARD: A proof of concept rule-based abort

    NASA Technical Reports Server (NTRS)

    Smith, Richard; Marinuzzi, John

    1987-01-01

    The Abort Region Determinator (ARD) is a console program in the space shuttle mission control center. During shuttle ascent, the Flight Dynamics Officer (FDO) uses the ARD to determine the possible abort modes and make abort calls for the crew. The goal of the Rule-based Abort region Determinator (RB/ARD) project was to test the concept of providing an onboard ARD for the shuttle or an automated ARD for the mission control center (MCC). A proof of concept rule-based system was developed on a LMI Lambda computer using PICON, a knowdedge-based system shell. Knowdedge derived from documented flight rules and ARD operation procedures was coded in PICON rules. These rules, in conjunction with modules of conventional code, enable the RB-ARD to carry out key parts of the ARD task. Current capabilities of the RB-ARD include: continuous updating of the available abort mode, recognition of a limited number of main engine faults and recommendation of safing actions. Safing actions recommended by the RB-ARD concern the Space Shuttle Main Engine (SSME) limit shutdown system and powerdown of the SSME Ac buses.

  3. A Rule Based Approach to ISS Interior Volume Control and Layout

    NASA Technical Reports Server (NTRS)

    Peacock, Brian; Maida, Jim; Fitts, David; Dory, Jonathan

    2001-01-01

    Traditional human factors design involves the development of human factors requirements based on a desire to accommodate a certain percentage of the intended user population. As the product is developed human factors evaluation involves comparison between the resulting design and the specifications. Sometimes performance metrics are involved that allow leniency in the design requirements given that the human performance result is satisfactory. Clearly such approaches may work but they give rise to uncertainty and negotiation. An alternative approach is to adopt human factors design rules that articulate a range of each design continuum over which there are varying outcome expectations and interactions with other variables, including time. These rules are based on a consensus of human factors specialists, designers, managers and customers. The International Space Station faces exactly this challenge in interior volume control, which is based on anthropometric, performance and subjective preference criteria. This paper describes the traditional approach and then proposes a rule-based alternative. The proposed rules involve spatial, temporal and importance dimensions. If successful this rule-based concept could be applied to many traditional human factors design variables and could lead to a more effective and efficient contribution of human factors input to the design process.

  4. Integration of object-oriented knowledge representation with the CLIPS rule based system

    NASA Technical Reports Server (NTRS)

    Logie, David S.; Kamil, Hasan

    1990-01-01

    The paper describes a portion of the work aimed at developing an integrated, knowledge based environment for the development of engineering-oriented applications. An Object Representation Language (ORL) was implemented in C++ which is used to build and modify an object-oriented knowledge base. The ORL was designed in such a way so as to be easily integrated with other representation schemes that could effectively reason with the object base. Specifically, the integration of the ORL with the rule based system C Language Production Systems (CLIPS), developed at the NASA Johnson Space Center, will be discussed. The object-oriented knowledge representation provides a natural means of representing problem data as a collection of related objects. Objects are comprised of descriptive properties and interrelationships. The object-oriented model promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects. Data is inherited through an object network via the relationship links. Together, the two schemes complement each other in that the object-oriented approach efficiently handles problem data while the rule based knowledge is used to simulate the reasoning process. Alone, the object based knowledge is little more than an object-oriented data storage scheme; however, the CLIPS inference engine adds the mechanism to directly and automatically reason with that knowledge. In this hybrid scheme, the expert system dynamically queries for data and can modify the object base with complete access to all the functionality of the ORL from rules.

  5. Criterial Noise Effects on Rule-Based Category Learning: The Impact of Delayed Feedback

    PubMed Central

    Ell, Shawn; Ing, A. David; Maddox, W. Todd

    2009-01-01

    Variability in the representation of the decision criterion is assumed in many category learning models yet few studies have directly examined its impact. On each trial, criterial noise should result in drift in the criterion and will negatively impact categorization accuracy, particularly in rule-based categorization tasks where learning depends upon the maintenance and manipulation of decision criteria. The results of three experiments test this hypothesis and examine the impact of working memory on slowing the drift rate. Experiment 1 examined the effect of drift by inserting a 5 s delay between the categorization response and the delivery of corrective feedback, and working memory demand was manipulated by varying the number of decision criteria to be learned. Delayed feedback adversely affected performance, but only when working memory demand was high. Experiment 2 built upon a classic finding in the absolute identification literature and demonstrated that distributing the criteria across multiple dimensions decreases the impact of drift during the delay. Experiment 3 confirmed that the effect of drift during the delay is moderated by working memory. These results provide important insights into the interplay between criterial noise and working memory as well as providing important constraints for models of rule-based category learning. PMID:19633342

  6. Rule-based topology system for spatial databases to validate complex geographic datasets

    NASA Astrophysics Data System (ADS)

    Martinez-Llario, J.; Coll, E.; Núñez-Andrés, M.; Femenia-Ribera, C.

    2017-06-01

    A rule-based topology software system providing a highly flexible and fast procedure to enforce integrity in spatial relationships among datasets is presented. This improved topology rule system is built over the spatial extension Jaspa. Both projects are open source, freely available software developed by the corresponding author of this paper. Currently, there is no spatial DBMS that implements a rule-based topology engine (considering that the topology rules are designed and performed in the spatial backend). If the topology rules are applied in the frontend (as in many GIS desktop programs), ArcGIS is the most advanced solution. The system presented in this paper has several major advantages over the ArcGIS approach: it can be extended with new topology rules, it has a much wider set of rules, and it can mix feature attributes with topology rules as filters. In addition, the topology rule system can work with various DBMSs, including PostgreSQL, H2 or Oracle, and the logic is performed in the spatial backend. The proposed topology system allows users to check the complex spatial relationships among features (from one or several spatial layers) that require some complex cartographic datasets, such as the data specifications proposed by INSPIRE in Europe and the Land Administration Domain Model (LADM) for Cadastral data.

  7. A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems

    NASA Technical Reports Server (NTRS)

    Sartori, Michael A.; Passino, Kevin M.; Antsaklis, Panos J.

    1992-01-01

    In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.

  8. AQM router design for TCP network via input constrained fuzzy control of time-delay affine Takagi-Sugeno fuzzy models

    NASA Astrophysics Data System (ADS)

    Chang, Wen-Jer; Meng, Yu-Teh; Tsai, Kuo-Hui

    2012-12-01

    In this article, Takagi-Sugeno (T-S) fuzzy control theory is proposed as a key tool to design an effective active queue management (AQM) router for the transmission control protocol (TCP) networks. The probability control of packet marking in the TCP networks is characterised by an input constrained control problem in this article. By modelling the TCP network into a time-delay affine T-S fuzzy model, an input constrained fuzzy control methodology is developed in this article to serve the AQM router design. The proposed fuzzy control approach, which is developed based on the parallel distributed compensation technique, can provide smaller probability of dropping packets than previous AQM design schemes. Lastly, a numerical simulation is provided to illustrate the usefulness and effectiveness of the proposed design approach.

  9. A fuzzy hill-climbing algorithm for the development of a compact associative classifier

    NASA Astrophysics Data System (ADS)

    Mitra, Soumyaroop; Lam, Sarah S.

    2012-02-01

    Classification, a data mining technique, has widespread applications including medical diagnosis, targeted marketing, and others. Knowledge discovery from databases in the form of association rules is one of the important data mining tasks. An integrated approach, classification based on association rules, has drawn the attention of the data mining community over the last decade. While attention has been mainly focused on increasing classifier accuracies, not much efforts have been devoted towards building interpretable and less complex models. This paper discusses the development of a compact associative classification model using a hill-climbing approach and fuzzy sets. The proposed methodology builds the rule-base by selecting rules which contribute towards increasing training accuracy, thus balancing classification accuracy with the number of classification association rules. The results indicated that the proposed associative classification model can achieve competitive accuracies on benchmark datasets with continuous attributes and lend better interpretability, when compared with other rule-based systems.

  10. Fuzzy Logic Controller Based on Observed Signals and a Genetic Algorithm Application with STATCOM for Power System Stabilization

    NASA Astrophysics Data System (ADS)

    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.

  11. Haplotype estimation from fuzzy genotypes using penalized likelihood.

    PubMed

    Uh, Hae-Won; Eilers, Paul H C

    2011-01-01

    The Composite Link Model is a generalization of the generalized linear model in which expected values of observed counts are constructed as a sum of generalized linear components. When combined with penalized likelihood, it provides a powerful and elegant way to estimate haplotype probabilities from observed genotypes. Uncertain ("fuzzy") genotypes, like those resulting from AFLP scores, can be handled by adding an extra layer to the model. We describe the model and the estimation algorithm. We apply it to a data set of accurate human single nucleotide polymorphism (SNP) and to a data set of fuzzy tomato AFLP scores.

  12. Adaptive process control using fuzzy logic and genetic algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  13. Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  14. Diagnosis of the industrial systems by fuzzy classification.

    PubMed

    Toscano, R; Lyonnet, P

    2003-04-01

    The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parametrization method which is not necessarily based on an iterative training. This approach can be seen as a pre-parametrization which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a dc motor showing the effectiveness of this method.

  15. Fast Fuzzy Arithmetic Operations

    NASA Technical Reports Server (NTRS)

    Hampton, Michael; Kosheleva, Olga

    1997-01-01

    In engineering applications of fuzzy logic, the main goal is not to simulate the way the experts really think, but to come up with a good engineering solution that would (ideally) be better than the expert's control, In such applications, it makes perfect sense to restrict ourselves to simplified approximate expressions for membership functions. If we need to perform arithmetic operations with the resulting fuzzy numbers, then we can use simple and fast algorithms that are known for operations with simple membership functions. In other applications, especially the ones that are related to humanities, simulating experts is one of the main goals. In such applications, we must use membership functions that capture every nuance of the expert's opinion; these functions are therefore complicated, and fuzzy arithmetic operations with the corresponding fuzzy numbers become a computational problem. In this paper, we design a new algorithm for performing such operations. This algorithm is applicable in the case when negative logarithms - log(u(x)) of membership functions u(x) are convex, and reduces computation time from O(n(exp 2))to O(n log(n)) (where n is the number of points x at which we know the membership functions u(x)).

  16. Games with fuzzy parameters

    NASA Astrophysics Data System (ADS)

    Messaoud, Deghdak

    2010-11-01

    In this paper, we study the existence of equilibrium in non-cooperative game with fuzzy parameters. We generalize te results of Larbani and Kacher(2008, 2009) in infinite dimentional spaces. The proof is based on the Browder-Fan fixed point theorem.

  17. Feedback can be superior to observational training for both rule-based and information-integration category structures.

    PubMed

    Edmunds, C E R; Milton, Fraser; Wills, Andy J

    2015-01-01

    The effects of two different types of training on rule-based and information-integration category learning were investigated in two experiments. In observational training, a category label is presented, followed by an example of that category and the participant's response. In feedback training, the stimulus is presented, and the participant assigns it to a category and then receives feedback about the accuracy of that decision. Ashby, Maddox, and Bohil (2002. Observational versus feedback training in rule-based and information-integration category learning. Memory & Cognition, 30, 666-677) reported that feedback training was superior to observational training when learning information-integration category structures, but that training type had little effect on the acquisition of rule-based category structures. These results were argued to support the COVIS (competition between verbal and implicit systems) dual-process account of category learning. However, a number of nonessential differences between their rule-based and information-integration conditions complicate interpretation of these findings. Experiment 1 controlled between-category structures for participant error rates, category separation, and the number of stimulus dimensions relevant to the categorization. Under these more controlled conditions, rule-based and information-integration category structures both benefited from feedback training to a similar degree. Experiment 2 maintained this difference in training type when learning a rule-based category that had otherwise been matched, in terms of category overlap and overall performance, with the rule-based categories used in Ashby et al. These results indicate that differences in dimensionality between the category structures in Ashby et al. is a more likely explanation for the interaction between training type and category structure than the dual-system explanation that they offered.

  18. Fuzzy logic in control systems: Fuzzy logic controller. I, II

    NASA Technical Reports Server (NTRS)

    Lee, Chuen Chien

    1990-01-01

    Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.

  19. Quantitative analysis of properties and spatial relations of fuzzy image regions

    NASA Technical Reports Server (NTRS)

    Krishnapuram, Raghu; Keller, James M.; Ma, Yibing

    1992-01-01

    Properties of objects and spatial relations between objects play an important role in rule-based approaches for high-level vision. The partial presence or absence of such properties and relationships can supply both positive and negative evidence for region labeling hypotheses. Similarly, fuzzy labeling of a region can generate new hypotheses pertaining to the properties of the region, its relation to the neighboring regions, and finally, the labels of the neighboring regions. In this paper, we present a unified methodology to characterize properties and spatial relationships of object regions in a digital image. The proposed methods can be used to arrive at more meaningful decisions about the contents of the scene.

  20. Process capability analysis using fuzzy sets theory

    NASA Astrophysics Data System (ADS)

    Munoz, Jairo

    1993-12-01

    If tolerances are defined by crisp numerical values, several vehicles based on probabilities and classical statistics exist to conduct a process capability analysis. If data are categorical, however, and if they are obtained from subjective evaluations, the existing methods are inappropriate. Furthermore, if specifications are set in lexical terms or are loosely defined, current approaches are impossible to implement. This paper applies fuzzy logic theory to study process capability in the presence of uncertainty and categorical data. Examples are discussed using TIG welding experimental data.

  1. Choosing goals, not rules: deciding among rule-based action plans.

    PubMed

    Klaes, Christian; Westendorff, Stephanie; Chakrabarti, Shubhodeep; Gail, Alexander

    2011-05-12

    In natural situations, movements are often directed toward locations different from that of the evoking sensory stimulus. Movement goals must then be inferred from the sensory cue based on rules. When there is uncertainty about the rule that applies for a given cue, planning a movement involves both choosing the relevant rule and computing the movement goal based on that rule. Under these conditions, it is not clear whether primates compute multiple movement goals based on all possible rules before choosing an action, or whether they first choose a rule and then only represent the movement goal associated with that rule. Supporting the former hypothesis, we show that neurons in the frontoparietal reach areas of monkeys simultaneously represent two different rule-based movement goals, which are biased by the monkeys' choice preferences. Apparently, primates choose between multiple behavioral options by weighing against each other the movement goals associated with each option.

  2. Rule-Based vs. Behavior-Based Self-Deployment for Mobile Wireless Sensor Networks

    PubMed Central

    Urdiales, Cristina; Aguilera, Francisco; González-Parada, Eva; Cano-García, Jose; Sandoval, Francisco

    2016-01-01

    In mobile wireless sensor networks (MWSN), nodes are allowed to move autonomously for deployment. This process is meant: (i) to achieve good coverage; and (ii) to distribute the communication load as homogeneously as possible. Rather than optimizing deployment, reactive algorithms are based on a set of rules or behaviors, so nodes can determine when to move. This paper presents an experimental evaluation of both reactive deployment approaches: rule-based and behavior-based ones. Specifically, we compare a backbone dispersion algorithm with a social potential fields algorithm. Most tests are done under simulation for a large number of nodes in environments with and without obstacles. Results are validated using a small robot network in the real world. Our results show that behavior-based deployment tends to provide better coverage and communication balance, especially for a large number of nodes in areas with obstacles. PMID:27399709

  3. Rule-based and lightly supervised methods to predict emotions in suicide notes.

    PubMed

    Pedersen, Ted

    2012-01-01

    This paper describes the Duluth systems that participated in the Sentiment Analysis track of the i2b2/VA/Cincinnati Children's 2011 Challenge. The top Duluth system was a rule-based approach derived through manual corpus analysis and the use of measures of association to identify significant ngrams. This performed in the median range of systems, attaining an F-measure of 0.45. The second system was automatically derived from the most frequent bigrams unique to one or two emotions. It achieved an F-measure of 0.36. The third system was the union of the first two, and reached an F-measure of 0.44.

  4. Modeling for (physical) biologists: an introduction to the rule-based approach

    PubMed Central

    Chylek, Lily A; Harris, Leonard A; Faeder, James R; Hlavacek, William S

    2015-01-01

    Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions. PMID:26178138

  5. Rule-Based vs. Behavior-Based Self-Deployment for Mobile Wireless Sensor Networks.

    PubMed

    Urdiales, Cristina; Aguilera, Francisco; González-Parada, Eva; Cano-García, Jose; Sandoval, Francisco

    2016-07-07

    In mobile wireless sensor networks (MWSN), nodes are allowed to move autonomously for deployment. This process is meant: (i) to achieve good coverage; and (ii) to distribute the communication load as homogeneously as possible. Rather than optimizing deployment, reactive algorithms are based on a set of rules or behaviors, so nodes can determine when to move. This paper presents an experimental evaluation of both reactive deployment approaches: rule-based and behavior-based ones. Specifically, we compare a backbone dispersion algorithm with a social potential fields algorithm. Most tests are done under simulation for a large number of nodes in environments with and without obstacles. Results are validated using a small robot network in the real world. Our results show that behavior-based deployment tends to provide better coverage and communication balance, especially for a large number of nodes in areas with obstacles.

  6. A Rule-Based Modeling for the Description of Flexible and Self-healing Business Processes

    NASA Astrophysics Data System (ADS)

    Boukhebouze, Mohamed; Amghar, Youssef; Benharkat, Aïcha-Nabila; Maamar, Zakaria

    In this paper we discuss the importance of ensuring that business processes are label robust and agile at the same time robust and agile. To this end, we consider reviewing the way business processes are managed. For instance we consider offering a flexible way to model processes so that changes in regulations are handled through some self-healing mechanisms. These changes may raise exceptions at run-time if not properly reflected on these processes. To this end we propose a new rule based model that adopts the ECA rules and is built upon formal tools. The business logic of a process can be summarized with a set of rules that implement an organization’s policies. Each business rule is formalized using our ECAPE formalism (Event-Condition-Action-Post condition- post Event). This formalism allows translating a process into a graph of rules that is analyzed in terms of reliably and flexibility.

  7. A rule-based expert system for generating control displays at the Advanced Photon Source

    SciTech Connect

    Coulter, K.J.

    1993-11-01

    The integration of a rule-based expert system for generating screen displays for controlling and monitoring instrumentation under the Experimental Physics and Industrial Control System (EPICS) is presented. The expert system is implemented using CLIPS, an expert system shell from the Software Technology Branch at Lyndon B. Johnson Space Center. The user selects the hardware input and output to be displayed and the expert system constructs a graphical control screen appropriate for the data. Such a system provides a method for implementing a common look and feel for displays created by several different users and reduces the amount of time required to create displays for new hardware configurations. Users are able to modify the displays as needed using the EPICS display editor tool.

  8. The diagnosis of microcytic anemia by a rule-based expert system using VP-Expert.

    PubMed

    O'Connor, M L; McKinney, T

    1989-09-01

    We describe our experience in creating a rule-based expert system for the interpretation of microcytic anemia using the expert system development tool, VP-Expert, running on an IBM personal computer. VP-Expert processes data (complete blood cell count results, age, and sex) according to a set of user-written logic rules (our program) to reach conclusions as to the following causes of microcytic anemia: alpha- and beta-thalassemia trait, iron deficiency, and anemia of chronic disease. Our expert system was tested using previously interpreted complete blood cell count data. In most instances, there was good agreement between the expert system and its pathologist-author, but many discrepancies were found in the interpretation of anemia of chronic disease. We conclude that VP-Expert has a useful level of power and flexibility, yet is simple enough that individuals with modest programming experience can create their own expert systems. Limitations of such expert systems are discussed.

  9. Modeling for (physical) biologists: an introduction to the rule-based approach

    NASA Astrophysics Data System (ADS)

    Chylek, Lily A.; Harris, Leonard A.; Faeder, James R.; Hlavacek, William S.

    2015-07-01

    Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions.

  10. Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

    NASA Astrophysics Data System (ADS)

    Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

    2017-09-01

    This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

  11. An Unsupervised Rule-Based Method to Populate Ontologies from Text

    NASA Astrophysics Data System (ADS)

    Motta, Eduardo; Siqueira, Sean; Andreatta, Alexandre

    An increasing amount of information is available on the web and usually is expressed as text. Semantic information is implicit in these texts, since they are mainly intended for human consumption and interpretation. Because unstructured information is not easily handled automatically, an information extraction process has to be used to identify concepts and establish relations among them. Ontologies are an appropriate way to represent structured knowledge bases, enabling sharing, reuse and inference. In this paper, an information extraction process is used for populating a domain ontology. It targets Brazilian Portuguese texts from a biographical dictionary of music, which requires specific tools due to some language unique aspects. An unsupervised rule-based method is proposed. Through this process, latent concepts and relations expressed in natural language can be extracted and represented as an ontology, allowing new uses and visualizations of the content, such as semantically browsing and inferring new knowledge.

  12. A rule-based seizure prediction method for focal neocortical epilepsy

    PubMed Central

    Aarabi, Ardalan; He, Bin

    2012-01-01

    Objective In the present study, we have developed a novel patient-specific rule-based seizure prediction system for focal neocortical epilepsy. Methods Five univariate measures including correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, and largest Lyapunov exponent as well as one bivariate measure, nonlinear interdependence, were extracted from non-overlapping 10-second segments of intracranial electroencephalogram (iEEG) data recorded using electrodes implanted deep in the brain and/or placed on the cortical surface. The spatio-temporal information was then integrated by using rules established based on patient-specific changes observed in the period prior to a seizure sample for each patient. The system was tested on 316 h of iEEG data containing 49 seizures recorded in eleven patients with medically intractable focal neocortical epilepsy. Results For seizure occurrence periods of 30 and 50 min our method showed an average sensitivity of 79.9% and 90.2% with an average false prediction rate of 0.17 and 0.11/h, respectively. In terms of sensitivity and false prediction rate, the system showed superiority to random and periodical predictors. Conclusions The nonlinear analysis of iEEG in the period prior to seizures revealed patient-specific spatio-temporal changes that were significantly different from those observed within baselines in the majority of the seizures analyzed in this study. Significance The present results suggest that the patient specific rule-based approach may become a potentially useful approach for predicting seizures prior to onset. PMID:22361267

  13. Semantic classification of diseases in discharge summaries using a context-aware rule-based classifier.

    PubMed

    Solt, Illés; Tikk, Domonkos; Gál, Viktor; Kardkovács, Zsolt T

    2009-01-01

    OBJECTIVE Automated and disease-specific classification of textual clinical discharge summaries is of great importance in human life science, as it helps physicians to make medical studies by providing statistically relevant data for analysis. This can be further facilitated if, at the labeling of discharge summaries, semantic labels are also extracted from text, such as whether a given disease is present, absent, questionable in a patient, or is unmentioned in the document. The authors present a classification technique that successfully solves the semantic classification task. DESIGN The authors introduce a context-aware rule-based semantic classification technique for use on clinical discharge summaries. The classification is performed in subsequent steps. First, some misleading parts are removed from the text; then the text is partitioned into positive, negative, and uncertain context segments, then a sequence of binary classifiers is applied to assign the appropriate semantic labels. Measurement For evaluation the authors used the documents of the i2b2 Obesity Challenge and adopted its evaluation measures: F(1)-macro and F(1)-micro for measurements. RESULTS On the two subtasks of the Obesity Challenge (textual and intuitive classification) the system performed very well, and achieved a F(1)-macro = 0.80 for the textual and F(1)-macro = 0.67 for the intuitive tasks, and obtained second place at the textual and first place at the intuitive subtasks of the challenge. CONCLUSIONS The authors show in the paper that a simple rule-based classifier can tackle the semantic classification task more successfully than machine learning techniques, if the training data are limited and some semantic labels are very sparse.

  14. Neural substrates of similarity and rule-based strategies in judgment

    PubMed Central

    von Helversen, Bettina; Karlsson, Linnea; Rasch, Björn; Rieskamp, Jörg

    2014-01-01

    Making accurate judgments is a core human competence and a prerequisite for success in many areas of life. Plenty of evidence exists that people can employ different judgment strategies to solve identical judgment problems. In categorization, it has been demonstrated that similarity-based and rule-based strategies are associated with activity in different brain regions. Building on this research, the present work tests whether solving two identical judgment problems recruits different neural substrates depending on people's judgment strategies. Combining cognitive modeling of judgment strategies at the behavioral level with functional magnetic resonance imaging (fMRI), we compare brain activity when using two archetypal judgment strategies: a similarity-based exemplar strategy and a rule-based heuristic strategy. Using an exemplar-based strategy should recruit areas involved in long-term memory processes to a larger extent than a heuristic strategy. In contrast, using a heuristic strategy should recruit areas involved in the application of rules to a larger extent than an exemplar-based strategy. Largely consistent with our hypotheses, we found that using an exemplar-based strategy led to relatively higher BOLD activity in the anterior prefrontal and inferior parietal cortex, presumably related to retrieval and selective attention processes. In contrast, using a heuristic strategy led to relatively higher activity in areas in the dorsolateral prefrontal and the temporal-parietal cortex associated with cognitive control and information integration. Thus, even when people solve identical judgment problems, different neural substrates can be recruited depending on the judgment strategy involved. PMID:25360099

  15. The effects of age on associative and rule-based causal learning and generalization.

    PubMed

    Mutter, Sharon A; Plumlee, Leslie F

    2014-06-01

    We assessed how age influences associative and rule-based processes in causal learning using the Shanks and Darby (1998) concurrent patterning discrimination task. In Experiment 1, participants were divided into groups based on their learning performance after 6 blocks of training trials. High discrimination mastery young adults learned the patterning discrimination more rapidly and accurately than moderate mastery young adults. They were also more likely to induce the patterning rule and use this rule to generate predictions for novel cues, whereas moderate mastery young adults were more likely to use cue similarity as the basis for their predictions. Like moderate mastery young adults, older adults used similarity-based generalization for novel cues, but they did not achieve the same level of patterning discrimination. In Experiment 2, young and older adults were trained to the same learning criterion. Older adults again showed deficits in patterning discrimination and, in contrast to young adults, even when they reported awareness of the patterning rule, they used only similarity-based generalization in their predictions for novel cues. These findings suggest that it is important to consider how the ability to code or use cue representations interacts with the requirements of the causal learning task. In particular, age differences in causal learning seem to be greatest for tasks that require rapid coding of configural representations to control associative interference between similar cues. Configural coding may also be related to the success of rule-based processes in these types of learning tasks. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  16. On the intuitionistic fuzzy topological spaces

    NASA Astrophysics Data System (ADS)

    Saadati, Reza; Park, Jin Han

    2006-01-01

    In this paper, we define precompact set in intuitionistic fuzzy metric spaces and prove that any subset of an intuitionistic fuzzy metric space is compact if and only if it is precompact and complete. Also we define topologically complete intuitionistic fuzzy metrizable spaces and prove that any $G_{\\delta }$ set in a complete intuitionistic fuzzy metric spaces is a topologically complete intuitionistic fuzzy metrizable space and vice versa. Finally, we define intuitionistic fuzzy normed spaces and fuzzy boundedness for linear operators and so we prove that every finite dimensional intuitionistic fuzzy normed space is complete.

  17. Bipolar-value fuzzy soft lie subalgebras

    NASA Astrophysics Data System (ADS)

    Zhang, Xiong-wei

    2017-09-01

    In this paper, the notions of bipolar-value fuzzy soft Lie subalgebras are given, some of their properties are studied. Furthermore, we shall define the definition of bipolar-value fuzzy soft homomorphism of bipolar-value fuzzy soft Lie subalgebras and show that the theorem of bipolar-value fuzzy soft homomorphic pre-image of bipolar-value fuzzy soft Lie subalgebra, we shall give a counterexample which the image of a bipolar-value fuzzy soft Lie subalgebra under bipolar-value fuzzy soft homomorphism needs not be bipolar-value fuzzy soft Lie subalgebra.

  18. Probability 1/e

    ERIC Educational Resources Information Center

    Koo, Reginald; Jones, Martin L.

    2011-01-01

    Quite a number of interesting problems in probability feature an event with probability equal to 1/e. This article discusses three such problems and attempts to explain why this probability occurs with such frequency.

  19. Probability 1/e

    ERIC Educational Resources Information Center

    Koo, Reginald; Jones, Martin L.

    2011-01-01

    Quite a number of interesting problems in probability feature an event with probability equal to 1/e. This article discusses three such problems and attempts to explain why this probability occurs with such frequency.

  20. Fuzzy pharmacology: theory and applications.

    PubMed

    Sproule, Beth A; Naranjo, Claudio A; Türksen, I Burhan

    2002-09-01

    Fuzzy pharmacology is a term coined to represent the application of fuzzy logic and fuzzy set theory to pharmacological problems. Fuzzy logic is the science of reasoning, thinking and inference that recognizes and uses the real world phenomenon that everything is a matter of degree. It is an extension of binary logic that is able to deal with complex systems because it does not require crisp definitions and distinctions for the system components. In pharmacology, fuzzy modeling has been used for the mechanical control of drug delivery in surgical settings, and work has begun evaluating its use in other pharmacokinetic and pharmacodynamic applications. Fuzzy pharmacology is an emerging field that, based on these initial explorations, warrants further investigation.

  1. Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

    SciTech Connect

    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.

  2. Adding memory processing behaviors to the fuzzy behaviorist-based navigation of mobile robots

    SciTech Connect

    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.

  3. Adaptive fuzzy logic restriction rules for error correction and safe stimulation patterns during functional electrical stimulation.

    PubMed

    Hansen, M; Haugland, M K

    2001-01-01

    Adaptive restriction rules based on fuzzy logic have been developed to eliminate errors and to increase stimulation safety in the foot-drop correction application, specifically when using adaptive logic networks to provide a stimulation control signal based on neural activity recorded from peripheral sensory nerve branches. The fuzzy rules were designed to increase flexibility and offer easier customization, compared to earlier versions of restriction rules. The rules developed quantified the duration of swing and stance phases into states of accepting or rejecting new transitions, based on the cyclic nature of gait and statistics on the current gait patterns. The rules were easy to custom design for a specific application, using linguistic terms to model the actions of the rules. The rules were tested using pre-recorded gait data processed through a gait event detector and proved to reduce detection delay and the number of errors, compared to conventional rules.

  4. Multilayer cellular neural network and fuzzy C-mean classifiers: comparison and performance analysis

    NASA Astrophysics Data System (ADS)

    Trujillo San-Martin, Maite; Hlebarov, Vejen; Sadki, Mustapha

    2004-11-01

    Neural Networks and Fuzzy systems are considered two of the most important artificial intelligent algorithms which provide classification capabilities obtained through different learning schemas which capture knowledge and process it according to particular rule-based algorithms. These methods are especially suited to exploit the tolerance for uncertainty and vagueness in cognitive reasoning. By applying these methods with some relevant knowledge-based rules extracted using different data analysis tools, it is possible to obtain a robust classification performance for a wide range of applications. This paper will focus on non-destructive testing quality control systems, in particular, the study of metallic structures classification according to the corrosion time using a novel cellular neural network architecture, which will be explained in detail. Additionally, we will compare these results with the ones obtained using the Fuzzy C-means clustering algorithm and analyse both classifiers according to its classification capabilities.

  5. Stock and option portfolio using fuzzy logic approach

    NASA Astrophysics Data System (ADS)

    Sumarti, Novriana; Wahyudi, Nanang

    2014-03-01

    Fuzzy Logic in decision-making process has been widely implemented in various problems in industries. It is the theory of imprecision and uncertainty that was not based on probability theory. Fuzzy Logic adds values of degree between absolute true and absolute false. It starts with and builds on a set of human language rules supplied by the user. The fuzzy systems convert these rules to their mathematical equivalents. This could simplify the job of the system designer and the computer, and results in much more accurate representations of the way systems behave in the real world. In this paper we examine the decision making process of stock and option trading by the usage of MACD (Moving Average Convergence Divergence) technical analysis and Option Pricing with Fuzzy Logic approach. MACD technical analysis is for the prediction of the trends of underlying stock prices, such as bearish (going downward), bullish (going upward), and sideways. By using Fuzzy C-Means technique and Mamdani Fuzzy Inference System, we define the decision output where the value of MACD is high then decision is "Strong Sell", and the value of MACD is Low then the decision is "Strong Buy". We also implement the fuzzification of the Black-Scholes option-pricing formula. The stock and options methods are implemented on a portfolio of one stock and its options. Even though the values of input data, such as interest rates, stock price and its volatility, cannot be obtain accurately, these fuzzy methods can give a belief degree of the calculated the Black-Scholes formula so we can make the decision on option trading. The results show the good capability of the methods in the prediction of stock price trends. The performance of the simulated portfolio for a particular period of time also shows good return.

  6. Accuracy enhancement in a fuzzy expert decision making system through appropriate determination of membership functions and its application in a medical diagnostic decision making system.

    PubMed

    Das, Suddhasattwa; Roy Chowdhury, Shubhajit; Saha, Hiranmay

    2012-06-01

    The paper attempts to improve the accuracy of a fuzzy expert decision making system by tuning the parameters of type-2 sigmoid membership functions of fuzzy input variables and hence determining the most appropriate type-1 membership function. The current work mathematically models the variability of human decision making process using type-2 fuzzy sets. Moreover, an index of accuracy of a fuzzy expert system has been proposed and determined analytically. It has also been ascertained that there exists only one rule in the rule base whose associated mapping for the ith linguistic variable maps to the same value as the maximum value of the membership function for the ith linguistic variable. The improvement in decision making accuracy was successfully verified in a medical diagnostic decision making system for renal diagnostic applications. Based on the accuracy estimations applied over a set of pathophysiological parameters, viz. body mass index, glucose, urea, creatinine, systolic and diastolic blood pressure, appropriate type-1 fuzzy sets of these parameters have been determined assuming normal distribution of type-1 membership function values in type-2 fuzzy sets. The type-1 fuzzy sets so determined have been used to develop an FPGA based smart processor. Using the processor, renal diagnosis of patients has been performed with an accuracy of 98.75%.

  7. An Application of Fuzzy Fault Tree Analysis to Uncontained Events of an Areo-Engine Rotor

    NASA Astrophysics Data System (ADS)

    Li, Yanfeng; Huang, Hong-Zhong; Zhu, Shun-Peng; Liu, Yu; Xiao, Ning-Cong

    2012-12-01

    Fault tree analysis is an important tool for system reliability analysis. Fuzzy fault tree analysis of uncontained events for aero-engine rotor is performed in this article. In addition, a new methodology based on fuzzy set theory is also used in fault tree analysis to quantify the failure probabilities of basic events. The theory of fuzzy fault tree is introduced firstly. Then the fault tree for uncontained events of an aero-engine rotor is established, in which the descending method is used to determine the minimal cut sets. Furthermore, the interval representation and calculation strategy is presented by using the symmetrical L-R type fuzzy number to describe the failure probability, and the resulting fault tree is analyzed quantitatively in the case study.

  8. Fuzzy-Contextual Contrast Enhancement.

    PubMed

    Parihar, Anil; Verma, Om; Khanna, Chintan

    2017-02-08

    This paper presents contrast enhancement algorithms based on fuzzy contextual information of the images. We introduce fuzzy similarity index and fuzzy contrast factor to capture the neighborhood characteristics of a pixel. A new histogram, using fuzzy contrast factor of each pixel is developed, and termed as the fuzzy dissimilarity histogram (FDH). A cumulative distribution function (CDF) is formed with normalized values of FDH and used as a transfer function to obtain the contrast enhanced image. The algorithm gives good contrast enhancement and preserves the natural characteristic of the image. In order to develop a contextual intensity transfer function, we introduce a fuzzy membership function based on fuzzy similarity index and coefficient of variation of the image. The contextual intensity transfer function is designed using the fuzzy membership function to achieve final contrast enhanced image. The overall algorithm is referred as the fuzzy contextual contrast-enhancement (FCCE) algorithm. The proposed algorithms are compared with conventional and state-of-art contrast enhancement algorithms. The quantitative and visual assessment of the results is performed. The results of quantitative measures are statistically analyzed using t-test. The exhaustive experimentation and analysis show the proposed algorithm efficiently enhances contrast and yields in natural visual quality images.

  9. Risk analysis with a fuzzy-logic approach of a complex installation

    NASA Astrophysics Data System (ADS)

    Peikert, Tim; Garbe, Heyno; Potthast, Stefan

    2016-09-01

    This paper introduces a procedural method based on fuzzy logic to analyze systematic the risk of an electronic system in an intentional electromagnetic environment (IEME). The method analyzes the susceptibility of a complex electronic installation with respect to intentional electromagnetic interference (IEMI). It combines the advantages of well-known techniques as fault tree analysis (FTA), electromagnetic topology (EMT) and Bayesian networks (BN) and extends the techniques with an approach to handle uncertainty. This approach uses fuzzy sets, membership functions and fuzzy logic to handle the uncertainty with probability functions and linguistic terms. The linguistic terms add to the risk analysis the knowledge from experts of the investigated system or environment.

  10. Optimum seismic structural design based on random vibration and fuzzy graded damages

    NASA Technical Reports Server (NTRS)

    Cheng, Franklin Y.; Ou, Jin-Ping

    1990-01-01

    This paper presents the fuzzy dynamical reliability and failure probability as well as the basic principles and the analytical method of loss assessment for nonlinear seismic steel structures. Also presented is the optimization formulation and a numerical example for double objectives, initial construction cost and expected failure loss, and dynamical reliability constraints. The earthquake ground motion is based on a stationary filtered non-white noise and the fuzzy damage grade is described by damage index.

  11. ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-level Rule-based Models in Cell Biology.

    PubMed

    Bittig, Arne; Uhrmacher, Adelinde

    2016-08-03

    Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

  12. Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China

    NASA Astrophysics Data System (ADS)

    Zhang, Nannan; Zhou, Kefa; Du, Xishihui

    2017-04-01

    Mineral prospectivity mapping (MPM) is a multi-step process that ranks promising target areas for further exploration. Fuzzy logic and fuzzy analytical hierarchy process (AHP) are knowledge-driven MPM approaches. In this study, both approaches were used for data processing, based on which MPM was performed for porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang. The results of the two methods were then compared. The two methods combined expert experience and the Studentized contrast (S(C)) values of the weights-of-evidence approach to calculate the weights of 15 layers, and these layers were then integrated by the gamma operator (γ). Through prediction-area (P-A) plot analysis, the optimal γ for fuzzy logic and fuzzy AHP was determined as 0.95 and 0.93, respectively. The thresholds corresponding to different levels of metallogenic probability were defined via concentration-area (C-A) fractal analysis. The prediction performances of the two methods were compared on this basis. The results showed that in MPM based on fuzzy logic, the area under the receiver operating characteristic (ROC) curve was 0.806 and 81.48% of the known deposits were predicted, whereas in MPM based on fuzzy AHP, the area under the ROC curve was 0.862 and 92.59% of the known deposits were predicted. Therefore, prediction based on fuzzy AHP is more accurate and can provide directions for future prospecting.

  13. Emergent fuzzy geometry and fuzzy physics in four dimensions

    NASA Astrophysics Data System (ADS)

    Ydri, Badis; Rouag, Ahlam; Ramda, Khaled

    2017-03-01

    A detailed Monte Carlo calculation of the phase diagram of bosonic mass-deformed IKKT Yang-Mills matrix models in three and six dimensions with quartic mass deformations is given. Background emergent fuzzy geometries in two and four dimensions are observed with a fluctuation given by a noncommutative U (1) gauge theory very weakly coupled to normal scalar fields. The geometry, which is determined dynamically, is given by the fuzzy spheres SN2 and SN2 × SN2 respectively. The three and six matrix models are effectively in the same universality class. For example, in two dimensions the geometry is completely stable, whereas in four dimensions the geometry is stable only in the limit M ⟶ ∞, where M is the mass of the normal fluctuations. The behaviors of the eigenvalue distribution in the two theories are also different. We also sketch how we can obtain a stable fuzzy four-sphere SN2 × SN2 in the large N limit for all values of M as well as models of topology change in which the transition between spheres of different dimensions is observed. The stable fuzzy spheres in two and four dimensions act precisely as regulators which is the original goal of fuzzy geometry and fuzzy physics. Fuzzy physics and fuzzy field theory on these spaces are briefly discussed.

  14. Fuzzy learning under and about an unfamiliar fuzzy teacher

    NASA Technical Reports Server (NTRS)

    Dasarathy, Belur V.

    1992-01-01

    This study addresses the problem of optimal parametric learning in unfamiliar fuzzy environments. Prior studies in the domain of unfamiliar environments, which employed either crisp or fuzzy approaches to model the uncertainty or imperfectness of the learning environment, assumed that the training sample labels provided by the unfamiliar teacher were crisp, even if not perfect. Here, the more realistic problem of fuzzy learning under an unfamiliar teacher who provides only fuzzy (instead of crisp) labels, is tackled by expanding the previously defined fuzzy membership concepts to include an additional component representative of the fuzziness of the teacher. The previously studied scenarios, namely, crisp and fuzzy learning under (crisp) unfamiliar teacher, can be looked upon as special cases of this new methodology. As under the earlier studies, the estimated membership functions can then be deployed during the ensuing classification decision phase to judiciously take into account the imperfectness of the learning environment. The study also offers some insight into the properties of several of these fuzzy membership function estimators by examining their behavior under certain specific scenarios.

  15. Fuzzy learning under and about an unfamiliar fuzzy teacher

    NASA Technical Reports Server (NTRS)

    Dasarathy, Belur V.

    1992-01-01

    This study addresses the problem of optimal parametric learning in unfamiliar fuzzy environments. Prior studies in the domain of unfamiliar environments, which employed either crisp or fuzzy approaches to model the uncertainty or imperfectness of the learning environment, assumed that the training sample labels provided by the unfamiliar teacher were crisp, even if not perfect. Here, the more realistic problem of fuzzy learning under an unfamiliar teacher who provides only fuzzy (instead of crisp) labels, is tackled by expanding the previously defined fuzzy membership concepts to include an additional component representative of the fuzziness of the teacher. The previously studied scenarios, namely, crisp and fuzzy learning under (crisp) unfamiliar teacher, can be looked upon as special cases of this new methodology. As under the earlier studies, the estimated membership functions can then be deployed during the ensuing classification decision phase to judiciously take into account the imperfectness of the learning environment. The study also offers some insight into the properties of several of these fuzzy membership function estimators by examining their behavior under certain specific scenarios.

  16. Fuzzy Logic Controller for Hemodialysis Machine Based on Human Body Model

    PubMed Central

    Nafisi, Vahid Reza; Eghbal, Manouchehr; Motlagh, Mohammad Reza Jahed; Yavari, Fatemeh

    2011-01-01

    Fuzzy controllers are being used in various control schemes. The aim of this study is to adjust the hemodialysis machine parameters by utilizing a fuzzy logic controller (FLC) so that patient's hemodynamic condition remains stable during hemodialysis treatment. For this purpose, a comprehensive mathematical model of the arterial pressure response during hemodialysis, including hemodynamic, osmotic, and regulatory phenomena has been used. The multi-input multi-output (MIMO) fuzzy logic controller receives three parameters from the model (heart rate, arterial blood pressure, and relative blood volume) as input. According to the changes in the controller input values and its rule base, the outputs change so that the patient's hemodynamic condition remains stable. The results of the simulations illustrate that applying the controller can improve the stability of a patient's hemodynamic condition during hemodialysis treatment and it also decreases the treatment time. Furthermore, by using fuzzy logic, there is no need to have prior knowledge about the system under control and the FLC is compatible with different patients. PMID:22606657

  17. A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

    PubMed Central

    Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

    2014-01-01

    The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic. PMID:25136690

  18. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

    PubMed

    Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

    2012-04-01

    This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

  19. Likelihood-fuzzy analysis of parotid gland shrinkage in radiotherapy patients.

    PubMed

    Pota, Marco; Scalco, Elisa; Sanguineti, Giuseppe; Belli, Maria Luisa; Cattaneo, Giovanni Mauro; Esposito, Massimo; Rizzo, Giovanna

    2014-01-01

    In head-and-neck radiotherapy, an early detection of patients who will undergo parotid glands shrinkage during the treatment is of primary importance, since this condition has been found to be associated with acute toxicity. In this work, a recently proposed approach, here named Likelihood-Fuzzy Analysis, based on both statistical learning and Fuzzy Logic, is proposed to support the identification of early predictors of parotid shrinkage from Computed Tomography images acquired during radiotherapy. For this purpose, a set of textural image parameters was extracted and considered as candidate of parotid shrinkage prediction; for all these parameters and combinations of maximum three of them, a fuzzy rule base was extracted, gaining very good results in terms of accuracy, sensitivity and specificity. The performance of classification was also compared to a classical Fisher's Linear Discriminant Analysis and found to provide better results. Moreover, the use of Fuzzy Logic allowed obtaining an interpretable description of the relations between textural features and the shrinkage process.

  20. On the Development of a Hierarchical Fuzzy Classification Framework for Industrial and Environmental Applications

    NASA Astrophysics Data System (ADS)

    Gadaras, Ioannis; Mikhailov, Ludmil; Batzias, Athanassios

    2009-08-01

    The aim of this paper is to present the functionality and performance of a general fuzzy classification method that extracts fuzzy rules directly from labeled numerical data using a supervised learning approach. The division of the input space is executed without the use of any a priori knowledge about the data distribution, allows the overlapping of different pattern classes and iteratively aims to reduce the generated classification conflict. The feature space division is affected by two variables that control the extension of the overlapping area and assign an appropriate class to the intersection by affecting the nature and value of the rule weights. The dimensional characteristics of the generated partitions automatically provide the shape and size of the membership functions for each fuzzy feature, and are consequently used for the generation of the equivalent fuzzy rules. This way, the hierarchical input partitioning results to a hierarchically structured rule base that can be used for future classification purposes. Last, an industrial case example for quality control with environmental implications is presented for sake of comparison, when less accuracy is adequate and only offline control is required.

  1. Fusion techniques of fuzzy systems and neural networks, and fuzzy systems and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Takagi, Hideyuki

    1993-12-01

    This paper overviews four combinations of fuzzy logic, neural networks and genetic algorithms: (1) neural networks to auto-design fuzzy systems, (2) employing fuzzy rule structure to construct structured neural networks, (3) genetic algorithms to auto-design fuzzy systems, and (4) a fuzzy knowledge-based system to control genetic parameter dynamically.

  2. Intelligent synthesis of neuromusculoskeletal signals using fuzzy expert critics

    NASA Astrophysics Data System (ADS)

    Winters, Jack M.

    1996-05-01

    The problem of synthesizing large amounts of sensor data into a meaningful form represents one of the key challenges in making effective use of smart sensors/actuators that are distributed throughout a structure. This paper develops an engineering approach for addressing this problem, focusing on how large sets of neuromusculoskeletal measurements can be synthesized with approximate reasoning by experts and trained human observation to help extract and prioritize the most salient diagnostic findings, given a reasonably large set of strategic performance tasks. A key objective is to create an environment for intimate human- computer interaction, that optimally uses the capabilities of each. The best of two key conceptual frameworks are synthesized: initial design via rule-based fuzzy expert critic modules, followed by a gradual transition toward fuzzy neuro-optimization and neuro- classification modules. It is suggested that this provides a more reasonable approach not only for interactive near-real-time medical diagnosis assisted by a 'smart' computer, but also for developing the types of robust adaptive critics needed for advanced studies of principles underlying neuromotor control and skill acquisition.

  3. A hierarchical structure for representing and learning fuzzy rules

    NASA Technical Reports Server (NTRS)

    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.

  4. A quantum probability perspective on borderline vagueness.

    PubMed

    Blutner, Reinhard; Pothos, Emmanuel M; Bruza, Peter

    2013-10-01

    The term "vagueness" describes a property of natural concepts, which normally have fuzzy boundaries, admit borderline cases, and are susceptible to Zeno's sorites paradox. We will discuss the psychology of vagueness, especially experiments investigating the judgment of borderline cases and contradictions. In the theoretical part, we will propose a probabilistic model that describes the quantitative characteristics of the experimental finding and extends Alxatib's and Pelletier's () theoretical analysis. The model is based on a Hopfield network for predicting truth values. Powerful as this classical perspective is, we show that it falls short of providing an adequate coverage of the relevant empirical results. In the final part, we will argue that a substantial modification of the analysis put forward by Alxatib and Pelletier and its probabilistic pendant is needed. The proposed modification replaces the standard notion of probabilities by quantum probabilities. The crucial phenomenon of borderline contradictions can be explained then as a quantum interference phenomenon.

  5. Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention.

    PubMed

    Lobach, David F; Johns, Ellis B; Halpenny, Barbara; Saunders, Toni-Ann; Brzozowski, Jane; Del Fiol, Guilherme; Berry, Donna L; Braun, Ilana M; Finn, Kathleen; Wolfe, Joanne; Abrahm, Janet L; Cooley, Mary E

    2016-11-08

    Management of uncontrolled symptoms is an important component of quality cancer care. Clinical guidelines are available for optimal symptom management, but are not often integrated into the front lines of care. The use of clinical decision support (CDS) at the point-of-care is an innovative way to incorporate guideline-based symptom management into routine cancer care. The objective of this study was to develop and evaluate a rule-based CDS system to enable management of multiple symptoms in lung cancer patients at the point-of-care. This study was conducted in three phases involving a formative evaluation, a system evaluation, and a contextual evaluation of clinical use. In Phase 1, we conducted iterative usability testing of user interface prototypes with patients and health care providers (HCPs) in two thoracic oncology clinics. In Phase 2, we programmed complex algorithms derived from clinical practice guidelines into a rules engine that used Web services to communicate with the end-user application. Unit testing of algorithms was conducted using a stack-traversal tree-spanning methodology to identify all possible permutations of pathways through each algorithm, to validate accuracy. In Phase 3, we evaluated clinical use of the system among patients and HCPs in the two clinics via observations, structured interviews, and questionnaires. In Phase 1, 13 patients and 5 HCPs engaged in two rounds of formative testing, and suggested improvements leading to revisions until overall usability scores met a priori benchmarks. In Phase 2, symptom management algorithms contained between 29 and 1425 decision nodes, resulting in 19 to 3194 unique pathways per algorithm. Unit testing required 240 person-hours, and integration testing required 40 person-hours. In Phase 3, both patients and HCPs found the system usable and acceptable, and offered suggestions for improvements. A rule-based CDS system for complex symptom management was systematically developed and tested. The

  6. Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention

    PubMed Central

    Johns, Ellis B; Halpenny, Barbara; Saunders, Toni-Ann; Brzozowski, Jane; Del Fiol, Guilherme; Berry, Donna L; Braun, Ilana M; Finn, Kathleen; Wolfe, Joanne; Abrahm, Janet L; Cooley, Mary E

    2016-01-01

    Background Management of uncontrolled symptoms is an important component of quality cancer care. Clinical guidelines are available for optimal symptom management, but are not often integrated into the front lines of care. The use of clinical decision support (CDS) at the point-of-care is an innovative way to incorporate guideline-based symptom management into routine cancer care. Objective The objective of this study was to develop and evaluate a rule-based CDS system to enable management of multiple symptoms in lung cancer patients at the point-of-care. Methods This study was conducted in three phases involving a formative evaluation, a system evaluation, and a contextual evaluation of clinical use. In Phase 1, we conducted iterative usability testing of user interface prototypes with patients and health care providers (HCPs) in two thoracic oncology clinics. In Phase 2, we programmed complex algorithms derived from clinical practice guidelines into a rules engine that used Web services to communicate with the end-user application. Unit testing of algorithms was conducted using a stack-traversal tree-spanning methodology to identify all possible permutations of pathways through each algorithm, to validate accuracy. In Phase 3, we evaluated clinical use of the system among patients and HCPs in the two clinics via observations, structured interviews, and questionnaires. Results In Phase 1, 13 patients and 5 HCPs engaged in two rounds of formative testing, and suggested improvements leading to revisions until overall usability scores met a priori benchmarks. In Phase 2, symptom management algorithms contained between 29 and 1425 decision nodes, resulting in 19 to 3194 unique pathways per algorithm. Unit testing required 240 person-hours, and integration testing required 40 person-hours. In Phase 3, both patients and HCPs found the system usable and acceptable, and offered suggestions for improvements. Conclusions A rule-based CDS system for complex symptom management

  7. Using rule-based shot dose assignment in model-based MPC applications

    NASA Astrophysics Data System (ADS)

    Bork, Ingo; Buck, Peter; Wang, Lin; Müller, Uwe

    2014-10-01

    Shrinking feature sizes and the need for tighter CD (Critical Dimension) control require the introduction of new technologies in mask making processes. One of those methods is the dose assignment of individual shots on VSB (Variable Shaped Beam) mask writers to compensate CD non-linearity effects and improve dose edge slope. Using increased dose levels only for most critical features, generally only for the smallest CDs on a mask, the change in mask write time is minimal while the increase in image quality can be significant. This paper describes a method combining rule-based shot dose assignment with model-based shot size correction. This combination proves to be very efficient in correcting mask linearity errors while also improving dose edge slope of small features. Shot dose assignment is based on tables assigning certain dose levels to a range of feature sizes. The dose to feature size assignment is derived from mask measurements in such a way that shape corrections are kept to a minimum. For example, if a 50nm drawn line on mask results in a 45nm chrome line using nominal dose, a dose level is chosen which is closest to getting the line back on target. Since CD non-linearity is different for lines, line-ends and contacts, different tables are generated for the different shape categories. The actual dose assignment is done via DRC rules in a pre-processing step before executing the shape correction in the MPC engine. Dose assignment to line ends can be restricted to critical line/space dimensions since it might not be required for all line ends. In addition, adding dose assignment to a wide range of line ends might increase shot count which is undesirable. The dose assignment algorithm is very flexible and can be adjusted based on the type of layer and the best balance between accuracy and shot count. These methods can be optimized for the number of dose levels available for specific mask writers. The MPC engine now needs to be able to handle different dose

  8. Automatic construction of rule-based ICD-9-CM coding systems

    PubMed Central

    Farkas, Richárd; Szarvas, György

    2008-01-01

    Background In this paper we focus on the problem of automatically constructing ICD-9-CM coding systems for radiology reports. ICD-9-CM codes are used for billing purposes by health institutes and are assigned to clinical records manually following clinical treatment. Since this labeling task requires expert knowledge in the field of medicine, the process itself is costly and is prone to errors as human annotators have to consider thousands of possible codes when assigning the right ICD-9-CM labels to a document. In this study we use the datasets made available for training and testing automated ICD-9-CM coding systems by the organisers of an International Challenge on Classifying Clinical Free Text Using Natural Language Processing in spring 2007. The challenge itself was dominated by entirely or partly rule-based systems that solve the coding task using a set of hand crafted expert rules. Since the feasibility of the construction of such systems for thousands of ICD codes is indeed questionable, we decided to examine the problem of automatically constructing similar rule sets that turned out to achieve a remarkable accuracy in the shared task challenge. Results Our results are very promising in the sense that we managed to achieve comparable results with purely hand-crafted ICD-9-CM classifiers. Our best model got a 90.26% F measure on the training dataset and an 88.93% F measure on the challenge test dataset, using the micro-averaged Fβ=1 measure, the official evaluation metric of the International Challenge on Classifying Clinical Free Text Using Natural Language Processing. This result would have placed second in the challenge, with a hand-crafted system achieving slightly better results. Conclusions Our results demonstrate that hand-crafted systems – which proved to be successful in ICD-9-CM coding – can be reproduced by replacing several laborious steps in their construction with machine learning models. These hybrid systems preserve the favourable

  9. Postoperative vomiting in pediatric oncologic patients: prediction by a fuzzy logic model.

    PubMed

    Bassanezi, Betina S B; de Oliveira-Filho, Antônio G; Jafelice, Rosana S M; Bustorff-Silva, Joaquim M; Udelsmann, Artur

    2013-01-01

    To report a fuzzy logic mathematical model to predict postoperative vomiting (POV) in pediatric oncologic patients and compare with preexisting scores. Although POV has a high incidence in children and may decrease parental satisfaction after surgeries, there is only one specific score that predicts POV in children: the Eberhart's score. In this study, we report a fuzzy model that intends to predict the probability of POV in pediatric oncologic patients. Fuzzy logic is a mathematical theory that recognizes more than simple true and false values and takes into account levels of continuous variables such as age or duration of the surgery. The fuzzy model tries to account for subjectiveness in the variables. Preoperative potential risk factors for POV in 198 children (0-19 year old) with malignancies were collected and analyzed. Data analysis was performed with the chi-square test and logistic regression to evaluate probable risk factors for POV. A system based on fuzzy logic was developed with the risk factors found in the logistic regression, and a computational interface was created to calculate the probability of POV. The model showed a good performance in predicting POV. After the analysis, the model was compared with Eberhart's score in the same population and showed a better performance. The fuzzy score can predict the chance of POV in children with cancer with good accuracy, allowing better planning for postoperative prophylaxis of vomiting. The computational interface is available for free download at the internet and is very easy to use. © 2012 Blackwell Publishing Ltd.

  10. Fuzzy logic and adaptive neuro-fuzzy inference system for characterization of contaminant exposure through selected biomarkers in African catfish.

    PubMed

    Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C

    2013-03-01

    This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies.

  11. Proteomics of a fuzzy organelle: interphase chromatin

    PubMed Central

    Kustatscher, Georg; Hégarat, Nadia; Wills, Karen L H; Furlan, Cristina; Bukowski-Wills, Jimi-Carlo; Hochegger, Helfrid; Rappsilber, Juri

    2014-01-01

    Chromatin proteins mediate replication, regulate expression, and ensure integrity of the genome. So far, a comprehensive inventory of interphase chromatin has not been determined. This is largely due to its heterogeneous and dynamic composition, which makes conclusive biochemical purification difficult, if not impossible. As a fuzzy organelle, it defies classical organellar proteomics and cannot be described by a single and ultimate list of protein components. Instead, we propose a new approach that provides a quantitative assessment of a protein's probability to function in chromatin. We integrate chromatin composition over a range of different biochemical and biological conditions. This resulted in interphase chromatin probabilities for 7635 human proteins, including 1840 previously uncharacterized proteins. We demonstrate the power of our large-scale data-driven annotation during the analysis of cyclin-dependent kinase (CDK) regulation in chromatin. Quantitative protein ontologies may provide a general alternative to list-based investigations of organelles and complement Gene Ontology. PMID:24534090

  12. Development of a rule-based algorithm for rice cultivation mapping using Landsat 8 time series

    NASA Astrophysics Data System (ADS)

    Karydas, Christos G.; Toukiloglou, Pericles; Minakou, Chara; Gitas, Ioannis Z.

    2015-06-01

    In the framework of ERMES project (FP7 66983), an algorithm for mapping rice cultivation extents using mediumhigh resolution satellite data was developed. ERMES (An Earth obseRvation Model based RicE information Service) aims to develop a prototype of downstream service for rice yield modelling based on a combination of Earth Observation and in situ data. The algorithm was designed as a set of rules applied on a time series of Landsat 8 images, acquired throughout the rice cultivation season of 2014 from the plain of Thessaloniki, Greece. The rules rely on the use of spectral indices, such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Seasonal Wetness Index (NSWI), extracted from the Landsat 8 dataset. The algorithm is subdivided into two phases: a) a hard classification phase, resulting in a binary map (rice/no-rice), where pixels are judged according to their performance in all the images of the time series, while index thresholds were defined after a trial and error approach; b) a soft classification phase, resulting in a fuzzy map, by assigning scores to the pixels which passed (as `rice') the first phase. Finally, a user-defined threshold of the fuzzy score will discriminate rice from no-rice pixels in the output map. The algorithm was tested in a subset of Thessaloniki plain against a set of selected field data. The results indicated an overall accuracy of the algorithm higher than 97%. The algorithm was also applied in a study are in Spain (Valencia) and a preliminary test indicated a similar performance, i.e. about 98%. Currently, the algorithm is being modified, so as to map rice extents early in the cultivation season (by the end of June), with a view to contribute more substantially to the rice yield prediction service of ERMES. Both algorithm modes (late and early) are planned to be tested in extra Mediterranean study areas, in Greece, Italy, and Spain.

  13. Fuzzy Logic in Medicine and Bioinformatics

    PubMed Central

    Torres, Angela; Nieto, Juan J.

    2006-01-01

    The purpose of this paper is to present a general view of the current applications of fuzzy logic in medicine and bioinformatics. We particularly review the medical literature using fuzzy logic. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes). PMID:16883057

  14. On Fuzzy Ideals of BL-Algebras

    PubMed Central

    Xin, Xiao Long

    2014-01-01

    In this paper we investigate further properties of fuzzy ideals of a BL-algebra. The notions of fuzzy prime ideals, fuzzy irreducible ideals, and fuzzy Gödel ideals of a BL-algebra are introduced and their several properties are investigated. We give a procedure to generate a fuzzy ideal by a fuzzy set. We prove that every fuzzy irreducible ideal is a fuzzy prime ideal but a fuzzy prime ideal may not be a fuzzy irreducible ideal and prove that a fuzzy prime ideal ω is a fuzzy irreducible ideal if and only if ω(0) = 1 and |Im⁡(ω)| = 2. We give the Krull-Stone representation theorem of fuzzy ideals in BL-algebras. Furthermore, we prove that the lattice of all fuzzy ideals of a BL-algebra is a complete distributive lattice. Finally, it is proved that every fuzzy Boolean ideal is a fuzzy Gödel ideal, but the converse implication is not true. PMID:24892085

  15. Representation of Fuzzy Symmetric Relations

    DTIC Science & Technology

    1986-03-19

    Std Z39-18 REPRESENTATION OF FUZZY SYMMETRIC RELATIONS L. Valverde Dept. de Matematiques i Estadistica Universitat Politecnica de Catalunya Avda...REPRESENTATION OF FUZZY SYMMETRIC RELATIONS L. "Valverde* Dept. de Matematiques i Estadistica Universitat Politecnica de Catalunya Avda. Diagonal, 649

  16. Teaching Machines to Think Fuzzy

    ERIC Educational Resources Information Center

    Technology Teacher, 2004

    2004-01-01

    Fuzzy logic programs for computers make them more human. Computers can then think through messy situations and make smart decisions. It makes computers able to control things the way people do. Fuzzy logic has been used to control subway trains, elevators, washing machines, microwave ovens, and cars. Pretty much all the human has to do is push one…

  17. Teaching Machines to Think Fuzzy

    ERIC Educational Resources Information Center

    Technology Teacher, 2004

    2004-01-01

    Fuzzy logic programs for computers make them more human. Computers can then think through messy situations and make smart decisions. It makes computers able to control things the way people do. Fuzzy logic has been used to control subway trains, elevators, washing machines, microwave ovens, and cars. Pretty much all the human has to do is push one…

  18. Fuzzy sets and data analysis.

    NASA Astrophysics Data System (ADS)

    di Gesù, V.

    Methods and their applications to data analysis problems in fuzzy-sets theory are presented. Fuzzy-sets theory seems to be a powerful tool to model uncertainty and vagueness present in the data and to represent the human thinking in a more natural way.

  19. Overcoming rule-based rigidity and connectionist limitations through massively-parallel case-based reasoning

    NASA Technical Reports Server (NTRS)

    Barnden, John; Srinivas, Kankanahalli

    1990-01-01

    Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificial intelligence presents major problems as well. A promising way out of this impasse is to build neural net models that accomplish massively parallel case-based reasoning. Case-based reasoning, which has received much attention recently, is essentially the same as analogy-based reasoning, and avoids many of the problems leveled at traditional artificial intelligence. Further problems are avoided by doing many strands of case-based reasoning in parallel, and by implementing the whole system as a neural net. In addition, such a system provides an approach to some aspects of the problems of noise, uncertainty and novelty in reasoning systems. The current neural net system (Conposit), which performs standard rule-based reasoning, is being modified into a massively parallel case-based reasoning version.

  20. Rule-Based Learning Explains Visual Perceptual Learning and Its Specificity and Transfer

    PubMed Central

    Zhang, Jun-Yun; Zhang, Gong-Liang; Xiao, Lu-Qi; Klein, Stanley A.; Levi, Dennis M.

    2010-01-01

    Visual perceptual learning models, as constrained by orientation and location specificities, propose that learning either reflects changes in V1 neuronal tuning or reweighting specific V1 inputs in either the visual cortex or higher areas. Here we demonstrate that, with a training-plus-exposure procedure, in which observers are trained at one orientation and either simultaneously or subsequently passively exposed to a second transfer orientation, perceptual learning can completely transfer to the second orientation in tasks known to be orientation-specific. However, transfer fails if exposure precedes the training. These results challenge the existing specific perceptual learning models by suggesting a more general perceptual learning process. We propose a rule-based learning model to explain perceptual learning and its specificity and transfer. In this model, a decision unit in high-level brain areas learns the rules of reweighting the V1 inputs through training. However, these rules cannot be applied to a new orientation/location because the decision unit cannot functionally connect to the new V1 inputs that are unattended or even suppressed after training at a different orientation/location, which leads to specificity. Repeated orientation exposure or location training reactivates these inputs to establish the functional connections and enable the transfer of learning. PMID:20844128

  1. Rule-based learning of regular past tense in children with specific language impairment.

    PubMed

    Smith-Lock, Karen M

    2015-01-01

    The treatment of children with specific language impairment was used as a means to investigate whether a single- or dual-mechanism theory best conceptualizes the acquisition of English past tense. The dual-mechanism theory proposes that regular English past-tense forms are produced via a rule-based process whereas past-tense forms of irregular verbs are stored in the lexicon. Single-mechanism theories propose that both regular and irregular past-tense verbs are stored in the lexicon. Five 5-year-olds with specific language impairment received treatment for regular past tense. The children were tested on regular past-tense production and third-person singular "s" twice before treatment and once after treatment, at eight-week intervals. Treatment consisted of one-hour play-based sessions, once weekly, for eight weeks. Crucially, treatment focused on different lexical items from those in the test. Each child demonstrated significant improvement on the untreated past-tense test items after treatment, but no improvement on the untreated third-person singular "s". Generalization to untreated past-tense verbs could not be attributed to a frequency effect or to phonological similarity of trained and tested items. It is argued that the results are consistent with a dual-mechanism theory of past-tense inflection.

  2. ConsPred: a rule-based (re-)annotation framework for prokaryotic genomes.

    PubMed

    Weinmaier, Thomas; Platzer, Alexander; Frank, Jeroen; Hellinger, Hans-Jörg; Tischler, Patrick; Rattei, Thomas

    2016-11-01

    The rapidly growing number of available prokaryotic genome sequences requires fully automated and high-quality software solutions for their initial and re-annotation. Here we present ConsPred, a prokaryotic genome annotation framework that performs intrinsic gene predictions, homology searches, predictions of non-coding genes as well as CRISPR repeats and integrates all evidence into a consensus annotation. ConsPred achieves comprehensive, high-quality annotations based on rules and priorities, similar to decision-making in manual curation and avoids conflicting predictions. Parameters controlling the annotation process are configurable by the user. ConsPred has been used in the institutions of the authors for longer than 5 years and can easily be extended and adapted to specific needs. The ConsPred algorithm for producing a consensus from the varying scores of multiple gene prediction programs approaches manual curation in accuracy. Its rule-based approach for choosing final predictions avoids overriding previous manual curations. ConsPred is implemented in Java, Perl and Shell and is freely available under the Creative Commons license as a stand-alone in-house pipeline or as an Amazon Machine Image for cloud computing, see https://sourceforge.net/projects/conspred/. thomas.rattei@univie.ac.atSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  3. Transfer in Rule-Based Category Learning Depends on the Training Task.

    PubMed

    Kattner, Florian; Cox, Christopher R; Green, C Shawn

    2016-01-01

    While learning is often highly specific to the exact stimuli and tasks used during training, there are cases where training results in learning that generalizes more broadly. It has been previously argued that the degree of specificity can be predicted based upon the learning solution(s) dictated by the particular demands of the training task. Here we applied this logic in the domain of rule-based categorization learning. Participants were presented with stimuli corresponding to four different categories and were asked to perform either a category discrimination task (which permits learning specific rule to discriminate two categories) or a category identification task (which does not permit learning a specific discrimination rule). In a subsequent transfer stage, all participants were asked to discriminate stimuli belonging to two of the categories which they had seen, but had never directly discriminated before (i.e., this particular discrimination was omitted from training). As predicted, learning in the category-discrimination tasks tended to be specific, while the category-identification task produced learning that transferred to the transfer discrimination task. These results suggest that the discrimination and identification tasks fostered the acquisition of different category representations which were more or less generalizable.

  4. A rule-based classification from a region-growing segmentation of airborne lidar

    NASA Astrophysics Data System (ADS)

    Martínez, Jorge; Rivera, Francisco F.; Cabaleiro, José C.; Vilariño, David L.; Pena, Tomás. F.; Miranda B., David

    2016-10-01

    Light Detection and Ranging (LiDAR) has attracted the interest of the research community in many fields, including object classification of the earth surface. In this paper we present an object-based classification method for airborne LiDAR that distinguishes three main classes (buildings, vegetation and ground) based only on LiDAR information. The key components of our proposal are the following: First, the LiDAR point cloud is stored in an octree for its efficient processing and the normal vector of each point is estimated using an adaptive neighborhood algorithm. Then, the points are segmented using a two-phase region growing algorithm where planar and non-planar objects are handled differently. The utilization of an epicenter point is introduced to allow regions to expand without losing homogeneity. Finally, a ruled-based procedure is performed to classify the segmented clusters. In order to evaluate our approach, a building detection was carried out, and results were obtained in terms of accuracy and computational time.

  5. Multiobjective reservoir operating rules based on cascade reservoir input variable selection method

    NASA Astrophysics Data System (ADS)

    Yang, Guang; Guo, Shenglian; Liu, Pan; Li, Liping; Xu, Chongyu

    2017-04-01

    The input variable selection in multiobjective cascade reservoir operation is an important and difficult task. To address this problem, this study proposes the cascade reservoir input variable selection (CIS) method that searches for the most valuable input variables for decision making in multiple-objectivity cascade reservoir operations. From a case study of Hanjiang cascade reservoirs in China, we derive reservoir operating rules based on the combination of CIS and Gaussian radial basis functions (RBFs) methods and optimize the rules through Pareto-archived dynamically dimensioned search (PA-DDS) with two objectives: to maximize both power generation and water supply. We select the most effective input variables and evaluate their impacts on cascade reservoir operations. From the simulated trajectories of reservoir water level, power generation, and water supply, we analyze the multiobjective operating rules with several input variables. The results demonstrate that the CIS method performs well in the selection of input variables for the cascade reservoir operation, and the RBFs method can fully express the nonlinear operating rules for cascade reservoirs. We conclude that the CIS method is an effective and stable approach to identifying the most valuable information from a large number of candidate input variables. While the reservoir storage state is the most valuable information for the Hanjiang cascade reservoir multiobjective operation, the reservoir inflow is the most effective input variable for the single-objective operation of Danjiangkou.

  6. Scalable rule-based modelling of allosteric proteins and biochemical networks.

    PubMed

    Ollivier, Julien F; Shahrezaei, Vahid; Swain, Peter S

    2010-11-04

    Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.

  7. Robust Unsupervised Arousal Rating: A Rule-Based Framework with Knowledge-Inspired Vocal Features

    PubMed Central

    Bone, Daniel; Lee, Chi-Chun; Narayanan, Shrikanth

    2015-01-01

    Studies in classifying affect from vocal cues have produced exceptional within-corpus results, especially for arousal (activation or stress); yet cross-corpora affect recognition has only recently garnered attention. An essential requirement of many behavioral studies is affect scoring that generalizes across different social contexts and data conditions. We present a robust, unsupervised (rule-based) method for providing a scale-continuous, bounded arousal rating operating on the vocal signal. The method incorporates just three knowledge-inspired features chosen based on empirical and theoretical evidence. It constructs a speaker’s baseline model for each feature separately, and then computes single-feature arousal scores. Lastly, it advantageously fuses the single-feature arousal scores into a final rating without knowledge of the true affect. The baseline data is preferably labeled as neutral, but some initial evidence is provided to suggest that no labeled data is required in certain cases. The proposed method is compared to a state-of-the-art supervised technique which employs a high-dimensional feature set. The proposed framework achieves highly-competitive performance with additional benefits. The measure is interpretable, scale-continuous as opposed to discrete, and can operate without any affective labeling. An accompanying Matlab tool is made available with the paper. PMID:25705327

  8. A rule-based kinetic model of RNA polymerase II C-terminal domain phosphorylation

    PubMed Central

    Aitken, Stuart; Alexander, Ross D.; Beggs, Jean D.

    2013-01-01

    The complexity of many RNA processing pathways is such that a conventional systems modelling approach is inadequate to represent all the molecular species involved. We demonstrate that rule-based modelling permits a detailed model of a complex RNA signalling pathway to be defined. Phosphorylation of the RNA polymerase II (RNAPII) C-terminal domain (CTD; a flexible tail-like extension of the largest subunit) couples pre-messenger RNA capping, splicing and 3′ end maturation to transcriptional elongation and termination, and plays a central role in integrating these processes. The phosphorylation states of the serine residues of many heptapeptide repeats of the CTD alter along the coding region of genes as a function of distance from the promoter. From a mechanistic perspective, both the changes in phosphorylation and the location at which they take place on the genes are a function of the time spent by RNAPII in elongation as this interval provides the opportunity for the kinases and phosphatases to interact with the CTD. On this basis, we synthesize the available data to create a kinetic model of the action of the known kinases and phosphatases to resolve the phosphorylation pathways and their kinetics. PMID:23804443

  9. RFID sensor-tags feeding a context-aware rule-based healthcare monitoring system.

    PubMed

    Catarinucci, Luca; Colella, Riccardo; Esposito, Alessandra; Tarricone, Luciano; Zappatore, Marco

    2012-12-01

    Along with the growing of the aging population and the necessity of efficient wellness systems, there is a mounting demand for new technological solutions able to support remote and proactive healthcare. An answer to this need could be provided by the joint use of the emerging Radio Frequency Identification (RFID) technologies and advanced software choices. This paper presents a proposal for a context-aware infrastructure for ubiquitous and pervasive monitoring of heterogeneous healthcare-related scenarios, fed by RFID-based wireless sensors nodes. The software framework is based on a general purpose architecture exploiting three key implementation choices: ontology representation, multi-agent paradigm and rule-based logic. From the hardware point of view, the sensing and gathering of context-data is demanded to a new Enhanced RFID Sensor-Tag. This new device, de facto, makes possible the easy integration between RFID and generic sensors, guaranteeing flexibility and preserving the benefits in terms of simplicity of use and low cost of UHF RFID technology. The system is very efficient and versatile and its customization to new scenarios requires a very reduced effort, substantially limited to the update/extension of the ontology codification. Its effectiveness is demonstrated by reporting both customization effort and performance results obtained from validation in two different healthcare monitoring contexts.

  10. Space communications scheduler: A rule-based approach to adaptive deadline scheduling

    NASA Technical Reports Server (NTRS)

    Straguzzi, Nicholas

    1990-01-01

    Job scheduling is a deceptively complex subfield of computer science. The highly combinatorial nature of the problem, which is NP-complete in nearly all cases, requires a scheduling program to intelligently transverse an immense search tree to create the best possible schedule in a minimal amount of time. In addition, the program must continually make adjustments to the initial schedule when faced with last-minute user requests, cancellations, unexpected device failures, quests, cancellations, unexpected device failures, etc. A good scheduler must be quick, flexible, and efficient, even at the expense of generating slightly less-than-optimal schedules. The Space Communication Scheduler (SCS) is an intelligent rule-based scheduling system. SCS is an adaptive deadline scheduler which allocates modular communications resources to meet an ordered set of user-specified job requests on board the NASA Space Station. SCS uses pattern matching techniques to detect potential conflicts through algorithmic and heuristic means. As a result, the system generates and maintains high density schedules without relying heavily on backtracking or blind search techniques. SCS is suitable for many common real-world applications.

  11. A rule-based expert system for chemical prioritization using effects-based chemical categories.

    PubMed

    Schmieder, P K; Kolanczyk, R C; Hornung, M W; Tapper, M A; Denny, J S; Sheedy, B R; Aladjov, H

    2014-01-01

    A rule-based expert system (ES) was developed to predict chemical binding to the estrogen receptor (ER) patterned on the research approaches championed by Gilman Veith to whom this article and journal issue are dedicated. The ERES was built to be mechanistically transparent and meet the needs of a specific application, i.e. predict for all chemicals within two well-defined inventories (industrial chemicals used as pesticide inerts and antimicrobial pesticides). These chemicals all lack structural features associated with high affinity binders and thus any binding should be low affinity. Similar to the high-quality fathead minnow database upon which Veith QSARs were built, the ERES was derived from what has been termed gold standard data, systematically collected in assays optimized to detect even low affinity binding and maximizing confidence in the negatives determinations. The resultant logic-based decision tree ERES, determined to be a robust model, contains seven major nodes with multiple effects-based chemicals categories within each. Predicted results are presented in the context of empirical data within local chemical structural groups facilitating informed decision-making. Even using optimized detection assays, the ERES applied to two inventories of >600 chemicals resulted in only ~5% of the chemicals predicted to bind ER.

  12. Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems

    NASA Technical Reports Server (NTRS)

    Hinchey, Mike; Rash, James; Erickson, John; Gracanin, Denis; Rouff, Chris

    2010-01-01

    Mathematically sound techniques are used to view a knowledge-based system (KBS) as a set of processes executing in parallel and being enabled in response to specific rules being fired. The set of processes can be manipulated, examined, analyzed, and used in a simulation. The tool that embodies this technology may warn developers of errors in their rules, but may also highlight rules (or sets of rules) in the system that are underspecified (or overspecified) and need to be corrected for the KBS to operate as intended. The rules embodied in a KBS specify the allowed situations, events, and/or results of the system they describe. In that sense, they provide a very abstract specification of a system. The system is implemented through the combination of the system specification together with an appropriate inference engine, independent of the algorithm used in that inference engine. Viewing the rule base as a major component of the specification, and choosing an appropriate specification notation to represent it, reveals how additional power can be derived from an approach to the knowledge-base system that involves analysis, simulation, and verification. This innovative approach requires no special knowledge of the rules, and allows a general approach where standardized analysis, verification, simulation, and model checking techniques can be applied to the KBS.

  13. Trainable rule-based network for irradiation field recognition in Agfa's ADC system

    NASA Astrophysics Data System (ADS)

    Dewaele, Piet; Ibison, Michael; Vuylsteke, Pieter

    1996-04-01

    The irradiation field is the diagnostic region of radiograph which has been exposed directly to x rays and has not been shielded from the source by x-ray opaque material. Such material may have been placed to shield vulnerable regions of the patient from unnecessary exposure, and also to partition the radiograph into sub-images, thereby permitting multiple exposures on the same plate. In this paper we describe an image analysis method to automatically detect and locate the irradiation field of a digital radiograph. The aim is to prepare the radiographic image for contrast-enhancement processing which is driven only by exposure data from the diagnostically useful part of the radiograph. The algorithm is broadly edge-based, and comprises four stages: (1) edge detection, culminating in a set of straight-line edge segments as non-iconic data structures; (2) clustering (where possible) these segments into longer lines in accordance with colinearity constraints and the like; (3) model matching by a trainable rule- based network to identify irradiation field boundaries; (4) masking, which acts upon the input image to blank out the regions not in the irradiation field. The network has been trained on a database of 2100 images, and has been tested in clinical use, delivering an accuracy for recognition of the irradiation field of better than 99%.

  14. Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes

    NASA Astrophysics Data System (ADS)

    Ianni, Giovambattista; Krennwallner, Thomas; Martello, Alessandra; Polleres, Axel

    RDF Schema (RDFS) as a lightweight ontology language is gaining popularity and, consequently, tools for scalable RDFS inference and querying are needed. SPARQL has become recently a W3C standard for querying RDF data, but it mostly provides means for querying simple RDF graphs only, whereas querying with respect to RDFS or other entailment regimes is left outside the current specification. In this paper, we show that SPARQL faces certain unwanted ramifications when querying ontologies in conjunction with RDF datasets that comprise multiple named graphs, and we provide an extension for SPARQL that remedies these effects. Moreover, since RDFS inference has a close relationship with logic rules, we generalize our approach to select a custom ruleset for specifying inferences to be taken into account in a SPARQL query. We show that our extensions are technically feasible by providing benchmark results for RDFS querying in our prototype system GiaBATA, which uses Datalog coupled with a persistent Relational Database as a back-end for implementing SPARQL with dynamic rule-based inference. By employing different optimization techniques like magic set rewriting our system remains competitive with state-of-the-art RDFS querying systems.

  15. Transfer in Rule-Based Category Learning Depends on the Training Task

    PubMed Central

    Kattner, Florian; Cox, Christopher R.; Green, C. Shawn

    2016-01-01

    While learning is often highly specific to the exact stimuli and tasks used during training, there are cases where training results in learning that generalizes more broadly. It has been previously argued that the degree of specificity can be predicted based upon the learning solution(s) dictated by the particular demands of the training task. Here we applied this logic in the domain of rule-based categorization learning. Participants were presented with stimuli corresponding to four different categories and were asked to perform either a category discrimination task (which permits learning specific rule to discriminate two categories) or a category identification task (which does not permit learning a specific discrimination rule). In a subsequent transfer stage, all participants were asked to discriminate stimuli belonging to two of the categories which they had seen, but had never directly discriminated before (i.e., this particular discrimination was omitted from training). As predicted, learning in the category-discrimination tasks tended to be specific, while the category-identification task produced learning that transferred to the transfer discrimination task. These results suggest that the discrimination and identification tasks fostered the acquisition of different category representations which were more or less generalizable. PMID:27764221

  16. The study on dynamic cadastral coding rules based on kinship relationship

    NASA Astrophysics Data System (ADS)

    Xu, Huan; Liu, Nan; Liu, Renyi; Lu, Jingfeng

    2007-06-01

    Cadastral coding rules are an important supplement to the existing national and local standard specifications for building cadastral database. After analyzing the course of cadastral change, especially the parcel change with the method of object-oriented analysis, a set of dynamic cadastral coding rules based on kinship relationship corresponding to the cadastral change is put forward and a coding format composed of street code, block code, father parcel code, child parcel code and grandchild parcel code is worked out within the county administrative area. The coding rule has been applied to the development of an urban cadastral information system called "ReGIS", which is not only able to figure out the cadastral code automatically according to both the type of parcel change and the coding rules, but also capable of checking out whether the code is spatiotemporally unique before the parcel is stored in the database. The system has been used in several cities of Zhejiang Province and got a favorable response. This verifies the feasibility and effectiveness of the coding rules to some extent.

  17. Application of a stopping rule based on total treatment failures: the postoperative Crohn's disease trial.

    PubMed

    Steinhart, A H; O'Rourke, K; Wolff, B G; McLeod, R S

    1992-05-01

    The Postoperative Crohn's Disease Trial (PCDT), a placebo-controlled randomized trial of Rowasa I in the prevention of postoperative recurrence of Crohn's disease, is used as an example of how a stopping rule based on total endpoint occurrences can provide considerable advantage over standard fixed sample size methods. It can be used when the primary outcome is occurrence or time to occurrence and does not raise the troublesome issues regarding the unblinding of group differences that other sequential methods create. The main advantage of the total endpoint stopping rule is that it provides set power. Standard fixed sample size designs provide a given power only on average. The power actually achieved in a particular fixed sample size trial is largely determined by the overall observed rate of endpoint occurrences. This claim about the total endpoint stopping rule is well established in the statistical literature and, as well as outlining the mathematical details in an Appendix, we use computer simulation of the PCDT to demonstrate that use of the stopping rule will allow termination of the trial while maintaining power and type I error at a predetermined level.

  18. A Rules-Based Service for Suggesting Visualizations to Analyze Earth Science Phenomena.

    NASA Astrophysics Data System (ADS)

    Prabhu, A.; Zednik, S.; Fox, P. A.; Ramachandran, R.; Maskey, M.; Shie, C. L.; Shen, S.

    2016-12-01

    Current Earth Science Information Systems lack support for new or interdisciplinary researchers, who may be unfamiliar with the domain vocabulary or the breadth of relevant data available. We need to evolve the current information systems, to reduce the time required for data preparation, processing and analysis. This can be done by effectively salvaging the "dark" resources in Earth Science. We assert that Earth science metadata assets are dark resources, information resources that organizations collect, process, and store for regular business or operational activities but fail to utilize for other purposes. In order to effectively use these dark resources, especially for data processing and visualization, we need a combination of domain, data product and processing knowledge, i.e. a knowledge base from which specific data operations can be performed. In this presentation, we describe a semantic, rules based approach to provide i.e. a service to visualize Earth Science phenomena, based on the data variables extracted using the "dark" metadata resources. We use Jena rules to make assertions about compatibility between a phenomena and various visualizations based on multiple factors. We created separate orthogonal rulesets to map each of these factors to the various phenomena. Some of the factors we have considered include measurements, spatial resolution and time intervals. This approach enables easy additions and deletions based on newly obtained domain knowledge or phenomena related information and thus improving the accuracy of the rules service overall.

  19. Designing caption production rules based on face, text, and motion detection

    NASA Astrophysics Data System (ADS)

    Chapdelaine, C.; Beaulieu, M.; Gagnon, L.

    2008-02-01

    Producing off-line captions for the deaf and hearing impaired people is a labor-intensive task that can require up to 18 hours of production per hour of film. Captions are placed manually close to the region of interest but it must avoid masking human faces, texts or any moving objects that might be relevant to the story flow. Our goal is to use image processing techniques to reduce the off-line caption production process by automatically placing the captions on the proper consecutive frames. We implemented a computer-assisted captioning software tool which integrates detection of faces, texts and visual motion regions. The near frontal faces are detected using a cascade of weak classifier and tracked through a particle filter. Then, frames are scanned to perform text spotting and build a region map suitable for text recognition. Finally, motion mapping is based on the Lukas-Kanade optical flow algorithm and provides MPEG-7 motion descriptors. The combined detected items are then fed to a rule-based algorithm to determine the best captions localization for the related sequences of frames. This paper focuses on the defined rules to assist the human captioners and the results of a user evaluation for this approach.

  20. Interaction of cytotoxic agents: a rule-based system for computer-assisted cell survival analysis.

    PubMed

    Gentile, F P; Chiatti, L; Mauro, F; Briganti, G; Floridi, A; Benassi, M

    1992-01-01

    The actual effectiveness of environmental noxious agents or anticancer drugs can be fully determined only by knowing if the effects (in the present case, the cytotoxic effects) induced by a given agent are enhanced by exposure to another (or other) agent(s). Given a certain combination of agents, it is possible to distinguish three types of interaction: (a) zero interaction or additivity; (b) positive interaction or synergism; and (c) negative interaction or antagonism. In this work, the methodological problems involved in evaluating the type and level of interaction between biologically active agents are discussed and an "intelligent" approach to the problem is proposed. In particular, a prototype of a computer-assisted rule based system, named CISA (Cytotoxic Interaction and Survival Analysis), designed in a KES environment (Knowledge Engineering System) and implemented on a personal computer, is described. By constructing isoboles based on experimental cell survival data and taking into account the relative confidence intervals, the system can indicate the appropriate combinations of dosages to be tested and finally determine the type and level of interaction. The system, which represents an attempt to administer "intelligently" the experimental data, is therefore able to identify the best strategy of analysis, to carry out the data processing and to offer suggestions to the investigator about the usefulness of the data and the planning of further experiments.

  1. A self-organized, distributed, and adaptive rule-based induction system.

    PubMed

    Rojanavasu, Pornthep; Dam, Hai Huong; Abbass, Hussein A; Lokan, Chris; Pinngern, Ouen

    2009-03-01

    Learning classifier systems (LCSs) are rule-based inductive learning systems that have been widely used in the field of supervised and reinforcement learning over the last few years. This paper employs sUpervised Classifier System (UCS), a supervised learning classifier system, that was introduced in 2003 for classification tasks in data mining. We present an adaptive framework of UCS on top of a self-organized map (SOM) neural network. The overall classification problem is decomposed adaptively and in real time by the SOM into subproblems, each of which is handled by a separate UCS. The framework is also tested with replacing UCS by a feedforward artificial neural network (ANN). Experiments on several synthetic and real data sets, including a very large real data set, show that the accuracy of classifications in the proposed distributed environment is as good or better than in the nondistributed environment, and execution is faster. In general, each UCS attached to a cell in the SOM has a much smaller population size than a single UCS working on the overall problem; since each data instance is exposed to a smaller population size than in the single population approach, the throughput of the overall system increases. The experiments show that the proposed framework can decompose a problem adaptively into subproblems, maintaining or improving accuracy and increasing speed.

  2. Reliability and performance evaluation of systems containing embedded rule-based expert systems

    NASA Technical Reports Server (NTRS)

    Beaton, Robert M.; Adams, Milton B.; Harrison, James V. A.

    1989-01-01

    A method for evaluating the reliability of real-time systems containing embedded rule-based expert systems is proposed and investigated. It is a three stage technique that addresses the impact of knowledge-base uncertainties on the performance of expert systems. In the first stage, a Markov reliability model of the system is developed which identifies the key performance parameters of the expert system. In the second stage, the evaluation method is used to determine the values of the expert system's key performance parameters. The performance parameters can be evaluated directly by using a probabilistic model of uncertainties in the knowledge-base or by using sensitivity analyses. In the third and final state, the performance parameters of the expert system are combined with performance parameters for other system components and subsystems to evaluate the reliability and performance of the complete system. The evaluation method is demonstrated in the context of a simple expert system used to supervise the performances of an FDI algorithm associated with an aircraft longitudinal flight-control system.

  3. Vibration suppression control of smart piezoelectric rotating truss structure by parallel neuro-fuzzy control with genetic algorithm tuning

    NASA Astrophysics Data System (ADS)

    Lin, J.; Zheng, Y. B.

    2012-07-01

    The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped-free-free-free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional-derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.

  4. Automatic de-identification of French clinical records: comparison of rule-based and machine-learning approaches.

    PubMed

    Grouin, Cyril; Zweigenbaum, Pierre

    2013-01-01

    In this paper, we present a comparison of two approaches to automatically de-identify medical records written in French: a rule-based system and a machine-learning based system using a conditional random fields (CRF) formalism. Both systems have been designed to process nine identifiers in a corpus of medical records in cardiology. We performed two evaluations: first, on 62 documents in cardiology, and on 10 documents in foetopathology - produced by optical character recognition (OCR) - to evaluate the robustness of our systems. We achieved a 0.843 (rule-based) and 0.883 (machine-learning) exact match overall F-measure in cardiology. While the rule-based system allowed us to achieve good results on nominative (first and last names) and numerical data (dates, phone numbers, and zip codes), the machine-learning approach performed best on more complex categories (postal addresses, hospital names, medical devices, and towns). On the foetopathology corpus, although our systems have not been designed for this corpus and despite OCR character recognition errors, we obtained promising results: a 0.681 (rule-based) and 0.638 (machine-learning) exact-match overall F-measure. This demonstrates that existing tools can be applied to process new documents of lower quality.

  5. Assessment of Matrix Multiplication Learning with a Rule-Based Analytical Model--"A Bayesian Network Representation"

    ERIC Educational Resources Information Center

    Zhang, Zhidong

    2016-01-01

    This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model,…

  6. Rule-based mapping of fire-adapted vegetation and fire regimes for the Monongahela National Forest

    Treesearch

    Melissa A. Thomas-Van Gundy; Gregory J. Nowacki; Thomas M. Schuler

    2007-01-01

    A rule-based approach was employed in GIS to map fire-adapted vegetation and fire regimes within the proclamation boundary of the Monongahela National Forest. Spatial analyses and maps were generated using ArcMap 9.1. The resulting fireadaptation scores were then categorized into standard fire regime groups. Fire regime group V (200+ yrs) was the most common, assigned...

  7. Using an empirical and rule-based modeling approach to map cause of disturbance in U.S

    Treesearch

    Todd A. Schroeder; Gretchen G. Moisen; Karen Schleeweis; Chris Toney; Warren B. Cohen; Zhiqiang Yang; Elizabeth A. Freeman

    2015-01-01

    Recently completing over a decade of research, the NASA/NACP funded North American Forest Dynamics (NAFD) project has led to several important advancements in the way U.S. forest disturbance dynamics are mapped at regional and continental scales. One major contribution has been the development of an empirical and rule-based modeling approach which addresses two of the...

  8. g.infer: A GRASS GIS module for rule-based data-driven classification and workflow control.

    NASA Astrophysics Data System (ADS)

    Löwe, Peter

    2013-04-01

    This poster describes the internal architecture of the new GRASS GIS module g.infer [1] and demonstrates application scenarios . The new module for GRASS GIS Version 6.x and 7.x enables rule-based analysis and workflow management via data-driven inference processes based on the C Language Integrated Production System (CLIPS) [2]. g.infer uses the pyClips module [3] to provide an Python-based environment for CLIPS within the GRASS GIS environment for rule-based knowledge engineering. Application scenarios range from rule-based classification tasks, event-driven workflow-control to complex simulations for tasks such as Soil Erosion Monitoring and Disaster Early Warning [4]. References: [1] Löwe P.: Introducing the new GRASS module g.infer for data-driven rule-based applications, Vol.8 2012-08, Geoinformatics FCE CTU, ISSN 1802-2669 [2] http://clipsrules.sourceforge.net/ [3] http://pyclips.sourceforge.net/web/ [4] Löwe P.: A Spatial Decision Support System for Radar-metereology Data in South Africa, Transactions in GIS 2004, (2): 235-244

  9. Computerized lung nodule detection on thoracic CT images: combined rule-based and statistical classifier for false-positive reduction

    NASA Astrophysics Data System (ADS)

    Gurcan, Metin N.; Petrick, Nicholas; Sahiner, Berkman; Chan, Heang-Ping; Cascade, Philip N.; Kazerooni, Ella A.; Hadjiiski, Lubomir M.

    2001-07-01

    We are developing a computer-aided diagnosis (CAD) system for lung nodule detection on thoracic helical computed tomography (CT) images. In the first stage of this CAD system, lung regions are identified and suspicious structures are segmented. These structures may include true lung nodules or normal structures that consist mainly of vascular structures. We have designed rule-based classifiers to distinguish nodules and normal structures using 2D and 3D features. After rule-based classification, linear discriminant analysis (LDA) is used to further reduce the number of false positive (FP) objects. We have performed a preliminary study using CT images from 17 patients with 31 lung nodules. When only LDA classification was applied to the segmented objects, the sensitivity was 84% (26/31) with 2.53 (1549/612) FP objects per slice. When the LDA followed the rule-based classifier, the number of FP objects per slice decreased to 1.75 (1072/612) at the same sensitivity. These preliminary results demonstrate the feasibility of our approach for nodule detection and FP reduction on CT images. The inclusion of rule-based classification leads to an improvement in detection accuracy for the CAD system.

  10. Dependent Measure and Time Constraints Modulate the Competition between Conflicting Feature-Based and Rule-Based Generalization Processes

    ERIC Educational Resources Information Center

    Cobos, Pedro L.; Gutiérrez-Cobo, María J.; Morís, Joaquín; Luque, David

    2017-01-01

    In our study, we tested the hypothesis that feature-based and rule-based generalization involve different types of processes that may affect each other producing different results depending on time constraints and on how generalization is measured. For this purpose, participants in our experiments learned cue-outcome relationships that followed…

  11. Ability-Grouping and Academic Inequality: Evidence from Rule-Based Student Assignments. NBER Working Paper No. 14911

    ERIC Educational Resources Information Center

    Jackson, C. Kirabo

    2009-01-01

    In Trinidad and Tobago students are assigned to secondary schools after fifth grade based on achievement tests, leading to large differences in the school environments to which students of differing initial levels of achievement are exposed. Using both a regression discontinuity design and rule-based instrumental variables to address…

  12. Probability and Relative Frequency

    NASA Astrophysics Data System (ADS)

    Drieschner, Michael

    2016-01-01

    The concept of probability seems to have been inexplicable since its invention in the seventeenth century. In its use in science, probability is closely related with relative frequency. So the task seems to be interpreting that relation. In this paper, we start with predicted relative frequency and show that its structure is the same as that of probability. I propose to call that the `prediction interpretation' of probability. The consequences of that definition are discussed. The "ladder"-structure of the probability calculus is analyzed. The expectation of the relative frequency is shown to be equal to the predicted relative frequency. Probability is shown to be the most general empirically testable prediction.

  13. A unified approach for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties

    NASA Astrophysics Data System (ADS)

    Lü, Hui; Shangguan, Wen-Bin; Yu, Dejie

    2017-09-01

    Automotive brake systems are always subjected to various types of uncertainties and two types of random-fuzzy uncertainties may exist in the brakes. In this paper, a unified approach is proposed for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties. In the proposed approach, two uncertainty analysis models with mixed variables are introduced to model the random-fuzzy uncertainties. The first one is the random and fuzzy model, in which random variables and fuzzy variables exist simultaneously and independently. The second one is the fuzzy random model, in which uncertain parameters are all treated as random variables while their distribution parameters are expressed as fuzzy numbers. Firstly, the fuzziness is discretized by using α-cut technique and the two uncertainty analysis models are simplified into random-interval models. Afterwards, by temporarily neglecting interval uncertainties, the random-interval models are degraded into random models, in which the expectations, variances, reliability indexes and reliability probabilities of system stability functions are calculated. And then, by reconsidering the interval uncertainties, the bounds of the expectations, variances, reliability indexes and reliability probabilities are computed based on Taylor series expansion. Finally, by recomposing the analysis results at each α-cut level, the fuzzy reliability indexes and probabilities can be obtained, by which the brake squeal instability can be evaluated. The proposed approach gives a general framework to deal with both types of random-fuzzy uncertainties that may exist in the brakes and its effectiveness is demonstrated by numerical examples. It will be a valuable supplement to the systematic study of brake squeal considering uncertainty.

  14. What Are Probability Surveys?

    EPA Pesticide Factsheets

    The National Aquatic Resource Surveys (NARS) use probability-survey designs to assess the condition of the nation’s waters. In probability surveys (also known as sample-surveys or statistical surveys), sampling sites are selected randomly.

  15. Evolution and Probability.

    ERIC Educational Resources Information Center

    Bailey, David H.

    2000-01-01

    Some of the most impressive-sounding criticisms of the conventional theory of biological evolution involve probability. Presents a few examples of how probability should and should not be used in discussing evolution. (ASK)

  16. Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions

    NASA Astrophysics Data System (ADS)

    Tsaur, Ruey-Chyn

    2015-02-01

    In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.

  17. Dependent Probability Spaces

    ERIC Educational Resources Information Center

    Edwards, William F.; Shiflett, Ray C.; Shultz, Harris

    2008-01-01

    The mathematical model used to describe independence between two events in probability has a non-intuitive consequence called dependent spaces. The paper begins with a very brief history of the development of probability, then defines dependent spaces, and reviews what is known about finite spaces with uniform probability. The study of finite…

  18. Water stress and foliar boron application altered seed nutrition in near-isogenic cotton lines expressing fuzzy and fuzzless seed phenotypes

    USDA-ARS?s Scientific Manuscript database

    Previous research, conducted under well-watered conditions without fertilizer application showed that fuzzy cottonseed trait resulted in cottonseed nutrient differences between fuzzy (F) and fuzzless (N) cottonseed, probably due to the involvement of carbon metabolism and energy use in fuzz fiber de...

  19. A comparison of rule-based and machine learning approaches for classifying patient portal messages.

    PubMed

    Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Rosenbloom, S Trent; Jackson, Gretchen Purcell

    2017-09-01

    Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard

  20. Perceptual Learning Improves Adult Amblyopic Vision Through Rule-Based Cognitive Compensation

    PubMed Central

    Zhang, Jun-Yun; Cong, Lin-Juan; Klein, Stanley A.; Levi, Dennis M.; Yu, Cong

    2014-01-01

    Purpose. We investigated whether perceptual learning in adults with amblyopia could be enabled to transfer completely to an orthogonal orientation, which would suggest that amblyopic perceptual learning results mainly from high-level cognitive compensation, rather than plasticity in the amblyopic early visual brain. Methods. Nineteen adults (mean age = 22.5 years) with anisometropic and/or strabismic amblyopia were trained following a training-plus-exposure (TPE) protocol. The amblyopic eyes practiced contrast, orientation, or Vernier discrimination at one orientation for six to eight sessions. Then the amblyopic or nonamblyopic eyes were exposed to an orthogonal orientation via practicing an irrelevant task. Training was first performed at a lower spatial frequency (SF), then at a higher SF near the cutoff frequency of the amblyopic eye. Results. Perceptual learning was initially orientation specific. However, after exposure to the orthogonal orientation, learning transferred to an orthogonal orientation completely. Reversing the exposure and training order failed to produce transfer. Initial lower SF training led to broad improvement of contrast sensitivity, and later higher SF training led to more specific improvement at high SFs. Training improved visual acuity by 1.5 to 1.6 lines (P < 0.001) in the amblyopic eyes with computerized tests and a clinical E acuity chart. It also improved stereoacuity by 53% (P < 0.001). Conclusions. The complete transfer of learning suggests that perceptual learning in amblyopia may reflect high-level learning of rules for performing a visual discrimination task. These rules are applicable to new orientations to enable learning transfer. Therefore, perceptual learning may improve amblyopic vision mainly through rule-based cognitive compensation. PMID:24550359