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1

Structure identification of generalized adaptive neuro-fuzzy inference systems

This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs). The structure of GANFIS consists of a number of generalized radial basis function (GRBF) units. The radial basis functions are irregularly distributed in the form of hyper-patches in the input-output space. The minimum number of GRBF units is selected based on a heuristic using

Mohammad Fazle Azeem; Madasu Hanmandlu; Nesar Ahmad

2003-01-01

2

A new learning algorithm for a fully connected neuro-fuzzy inference system.

A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence. PMID:25291730

Chen, C L Philip; Wang, Jing; Wang, Chi-Hsu; Chen, Long

2014-10-01

3

Adaptive neuro fuzzy inference system for profiling of the atmosphere

NASA Astrophysics Data System (ADS)

Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropical station Gadanki (13.5° N, 79.2° E), India. The observations of brightness temperatures recorded by Radiometrics Multichannel Microwave Radiometer MP3000 for the period of June-September 2011 are used to model profiles of atmospheric parameters up to 10 km. The ultimate goal of this work is to use the ANFIS forecast model to retrieve atmospheric profiles accurately during the wet season of the Indian monsoon (JJAS) season and during heavy rainfall associated with tropical convections. The comparison analysis of the ANFIS model retrieval of temperature and relative humidity (RH) profiles with GPS-radiosonde observations and profiles retrieved using the Artificial Neural Network (ANN) algorithm indicates that errors in the ANFIS model are less even in the wet season, and retrievals using ANFIS are more reliable, making this technique the standard. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 99% for temperature profiles for both techniques and therefore both techniques are successful in the retrieval of temperature profiles. However, in the case of RH the retrieval using ANFIS is found to be better. The comparison of mean absolute error (MAE), root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and RH profiles using ANN and ANFIS also indicates that profiles retrieved using ANFIS are significantly better compared to the ANN technique. The error analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the retrievals substantially; however, retrieval of RH by both techniques (ANN and ANFIS) has limited success.

Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

2014-03-01

4

Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival

Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival Hazlina Hamdan for breast cancer. I. INTRODUCTION Breast cancer is one of the most common cancers to afflict the female population. It is estimated that one in nine women in the UK will develop breast cancer at some point

Aickelin, Uwe

5

Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems

The aim of this study is to demonstrate the usefulness of an adaptive neuro-fuzzy inference system (ANFIS) for the modelling of ground-coupled heat pump (GCHP) system. The GCHP system connected to a test room with 16.24m2 floor area in F?rat University, Elaz?? (38.41°N, 39.14°E), Turkey, was designed and constructed. The heating and cooling loads of the test room were 2.5

Hikmet Esen; Mustafa Inalli; Abdulkadir Sengur; Mehmet Esen

2008-01-01

6

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique. PMID:12662634

Kim, J; Kasabov, N

1999-11-01

7

NASA Astrophysics Data System (ADS)

SummaryIn this study, an adaptive neuro fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors including average monthly water bill, population, number of households, gross national product, monthly average temperature observed, monthly total rainfall, monthly average humidity observed and inflation rate. Water consumption modeling in this way will be more consistent than doing it using a single variable as more effective parameter could be incorporated. The ANFIS system is applied to modeling monthly water consumptions of Izmir, Turkey. The results indicated that ANFIS can be successfully applied for monthly water consumption modeling.

Yurdusev, Mehmet Ali; Firat, Mahmut

2009-02-01

8

Training Hybrid Neuro-Fuzzy System to Infer Permeability in Wells on Maracaibo Lake, Venezuela

The high accuracy on inferrring of rocks properties, such as permeability ($k$), is a very useful study in the analysis of wells. This has led to development and use of empirical equations like Tixier, Timur, among others. In order to improve the inference of permeability we used a hybrid Neuro-Fuzzy System (NFS). The NFS allowed us to infer permeability of well, from data of porosity ($\\phi$) and water saturation ($Sw$). The work was performed with data from wells VCL-1021 (P21) and VCL-950 (P50), Block III, Maracaibo Lake, Venezuela. We evaluated the NFS equations ($k_{P50,i}(\\phi_i,Sw_i)$) with neighboring well data ($P21$), in order to verify the validity of the equations in the area. We have used ANFIS in MatLab.

Hurtado, Nuri; Torres, Julio

2014-01-01

9

Design of a biped locomotion controller based on adaptive neuro-fuzzy inference systems

NASA Astrophysics Data System (ADS)

This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface.

Shieh, M.-Y.; Chang, K.-H.; Lia, Y.-S.

2008-02-01

10

The main topic in this work was the development of a hybrid intelligent system for the hourly load forecasting in a time period\\u000a of 7 days ahead, using a combination of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. The hourly load\\u000a forecasting was accomplished in two steps: in the first one, two ANNs are used to forecast the total

Ronaldo R. B. De Aquino; Geane B. Silva; Milde M. S. Lira; Aida A. Ferreira; Manoel A. Carvalho Jr; Otoni Nóbrega Neto; Josinaldo B. De Oliveira

2007-01-01

11

Visual servoing system based on ANFIS (adaptive neuro fuzzy inference system)

NASA Astrophysics Data System (ADS)

Research in this visual servoing field in the past few decades has produced remarkable results, leading to many exciting expectations as well as new challenges. However, because of the complicated calculation of the inverse Jacobian, it is difficult to implement in real time. Therefore, instead of using the inverse Jacobian, this paper employs the ANFIS (Adaptive Neuro Fuzzy Inference System) approach for visual servo control of a robot manipulator. It is based on visual feedback and no prior information about the kinematics of robot and the camera calibration are unnecessary. Firstly, to efficiently control a manipulator, 3D space is divided into two 2D spaces. And then, we acquire training data from each 2D space and ANFIS is learned by the training data. We categorize the robot movement into two kinds of actions. That is, TOWARD action is performed, in the xy plane, by joint 1 and APPROACH action is performed, in the plane orthogonal to the xy plane, by joint 2 and joint 3. The time varying object can be tracked by controlling both actions in each plane and the simulation results show the validation of our approach.

Choi, Gyu-Jong; Lee, Kyoung-Soo; Ahn, Doo-Sung

2001-10-01

12

NASA Astrophysics Data System (ADS)

Compressional-wave (Vp) data are key information for estimation of rock physical properties and formation evaluation in hydrocarbon reservoirs. However, the absence of Vp will significantly delay the application of specific risk-assessment approaches for reservoir exploration and development procedures. Since Vp is affected by several factors such as lithology, porosity, density, and etc., it is difficult to model their non-linear relationships using conventional approaches. In addition, currently available techniques are not efficient for Vp prediction, especially in carbonates. There is a growing interest in incorporating advanced technologies for an accurate prediction of lacking data in wells. The objectives of this study, therefore, are to analyze and predict Vp as a function of some conventional well logs by two approaches; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). Also, the significant impact of selected input parameters on response variable will be investigated. A total of 2156 data points from a giant Middle Eastern carbonate reservoir, derived from conventional well logs and Dipole Sonic Imager (DSI) log were utilized in this study. The quality of the prediction was quantified in terms of the mean squared error (MSE), correlation coefficient (R-square), and prediction efficiency error (PEE). Results show that the ANFIS outperforms MLR with MSE of 0.0552, R-square of 0.964, and PEE of 2%. It is posited that porosity has a significant impact in predicting Vp in the investigated carbonate reservoir.

Zoveidavianpoor, Mansoor; Samsuri, Ariffin; Shadizadeh, Seyed Reza

2013-02-01

13

NASA Astrophysics Data System (ADS)

This paper aims to study the relationship between large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neuro-fuzzy inference system (ANFIS) was used in this study to predict rainfall in the period between April and June in Khorasan Razavi Province. We first analyzed the relationship between average regional rainfall and the changes in synoptic patterns including sea-level pressure, sea-level pressure difference, sea-level temperature, temperature difference between sea level and the 1,000-hPa level, the temperature of the 700-hPa level, the thickness between the 500- and 1,000-hPa levels, the relative humidity at the 300-hPa level, and precipitable water content. We have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the ANFIS in the period 1970-1997 has been taught. Finally, we forecast the rainfall for the period 1998-2007. The results show that the ANFIS can predict the rainfall with reasonable accuracy.

Fallah-Ghalhary, Gholam Abbas; Habibi-Nokhandan, Majid; Mousavi-Baygi, Mohammad; Khoshhal, Javad; Shaemi Barzoki, Akbar

2010-07-01

14

NASA Astrophysics Data System (ADS)

Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

Gowtham, K. N.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.

2011-04-01

15

Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

Akhavan, P.; Karimi, M.; Pahlavani, P.

2014-10-01

16

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

2014-01-01

17

In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. PMID:24793823

Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise

2014-11-01

18

A Neuro-fuzzy Adaptive Power System Stabilizer Using Genetic Algorithms

This article presents the design technique of an adaptive power system stabilizer using adaptive neuro-fuzzy inference systems trained via data obtained from genetic algorithms. The parameters of a standard power system stabilizer are tuned using adaptive neuro-fuzzy inference systems to achieve a certain damping ratio and settling time at all load points within a wide region of operation. The overall

M. A. Awadallah; H. M. Soliman

2009-01-01

19

This study’s aim is to develop diverse Artificial Intelligence-based (AI-based) thermal control logics and to compare their performances for identifying potentials as an advanced thermal control method in buildings. Towards that aim, three AI-based control logics have been developed: i) Fuzzy-based control; ii) ANFIS-based (Adaptive Neuro-Fuzzy Inference System-based) control; and iii) ANN-based (Artificial Neural Network-based) control. The last-mentioned two were

Jin Woo Moon; Sung Kwon Jung; Youngchul Kim; Seung-Hoon Han

2011-01-01

20

It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The results indicate that the modified neuro-fuzzy model applied to the flood prediction seems to have reached encouraging results for the river basin under examination. The comparison of the modified neuro-fuzzy predictions with the observed data was satisfactory, where the error resulted from the testing period was varied between 2.632% and 5.560%. Thus, this program may also serve as a tool for real-time flood monitoring and process control. PMID:17302300

Aqil, M; Kita, I; Yano, A; Nishiyama, S

2006-01-01

21

NASA Astrophysics Data System (ADS)

Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.

Teimouri, Reza; Sohrabpoor, Hamed

2013-12-01

22

NASA Astrophysics Data System (ADS)

Estimation of pan evaporation ( E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient ( r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions ( r = 0.97, RMSE = 0.81 mm day-1, MAE = 0.63 mm day-1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.

Tabari, Hossein; Hosseinzadeh Talaee, P.; Abghari, Hirad

2012-05-01

23

NASA Astrophysics Data System (ADS)

The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the prediction capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest prediction capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

Tien Bui, Dieu; Pradhan, Biswajeet; Lofman, Owe; Revhaug, Inge; Dick, Oystein B.

2012-08-01

24

temperature is named Current Correction Temperature Control (CCTC). It manipulates the fuel cell current the fuel cell current and thereby the amount of excess hydrogen sent from the fuel cell to the burner. The system is used as a battery charger and the fuel cell current can therefore be different to the reference

Andreasen, SÃ¸ren Juhl

25

Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department. PMID:24052927

Azeez, Dhifaf; Ali, Mohd Alauddin Mohd; Gan, Kok Beng; Saiboon, Ismail

2013-01-01

26

Short term wind power forecasting using adaptive neuro-fuzzy inference systems

As the global political will to address climate change gains momentum, the issues associated with integrating an increasing penetration of wind power into power systems need to be addressed. This paper summarises the current trends in wind power and how it is accepted into electricity markets. The need for accurate short term wind power forecasting is highlighted with particular reference

Peter L. Johnson; Michael Negnevitsky; Kashem M. Muttaqi

2007-01-01

27

NASA Astrophysics Data System (ADS)

A spectrophotometric method for the simultaneous determination of Al(III), Co(II) and Ni(II) using Alizarin Red S as a chelating agent was developed. The parameters controlling the behavior of the system were investigated and optimum conditions were selected. The presence of non-linearities was checked using Mallows augmented partial residual plots. To take into account these non-linearities, a principal component analysis-adaptive neuro-fuzzy inference systems (PC-ANFISs) method was used for the analysis of ternary mixtures of Al(III), Co(II) and Ni(II) over the range of 0.05-0.90, 0.05-4.05 and 0.05-0.95 ?g mL -1, respectively. Absorbance data were collected between 370 and 700 nm. The method was applied to accurately and simultaneously determines the content of metal ions in several synthetic mixtures.

Goodarzi, Mohammad; Olivieri, Alejandro C.; Freitas, Matheus P.

2009-08-01

28

This work evaluated artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modelling methods to estimate organic carbon removal using the correlation among the past information of influent and effluent parameters in a full-scale aerobic biological wastewater treatment plant. Model development focused on providing an adaptive, useful, practical and alternative methodology for modelling of organic carbon removal. For both models, measured and predicted effluent COD concentrations were strongly correlated with determination coefficients over 0.96. The errors associated with the prediction of effluent COD by the ANFIS modelling appeared to be within the error range of analytical measurements. The results overall indicated that the ANFIS modelling approach may be suitable to describe the relationship between wastewater quality parameters and may have application potential for performance prediction and control of aerobic biological processes in wastewater treatment plants. PMID:19759450

Civelekoglu, G; Yigit, N O; Diamadopoulos, E; Kitis, M

2009-01-01

29

Intelligent Transformer Monitoring System Utilizing Neuro-Fuzzy

Intelligent Transformer Monitoring System Utilizing Neuro-Fuzzy Technique Approach Intelligent Substation Final Project Report Power Systems Engineering Research Center A National Science Foundation Industry/University Cooperative Research Center since 1996 PSERC #12;Power Systems Engineering Research

30

NASA Astrophysics Data System (ADS)

In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

Ajay Kumar, M.; Srikanth, N. V.

2014-03-01

31

Neuro-fuzzy approaches for identification and control of nonlinear systems

Neural networks and fuzzy inference systems are becoming well recognized tools of designing an identifier\\/controller capable of perceiving the operating environment and imitating human operator with high performance. The motivation behind the use of neuro-fuzzy approaches is based on the complexity of real life systems, ambiguities on sensory information or time varying nature of the system under investigation. In this

M. Onder Efe; Okyay Kaynak

1999-01-01

32

NASA Astrophysics Data System (ADS)

The battery management system (BMS) of a battery-electric road vehicle must ensure an optimal operation of the electrochemical storage system to guarantee for durability and reliability. In particular, the BMS must provide precise information about the battery's state-of-functionality, i.e. how much dis-/charging power can the battery accept at current state and condition while at the same time preventing it from operating outside its safe operating area. These critical limits have to be calculated in a predictive manner, which serve as a significant input factor for the supervising vehicle energy management (VEM). The VEM must provide enough power to the vehicle's drivetrain for certain tasks and especially in critical driving situations. Therefore, this paper describes a new approach which can be used for state-of-available-power estimation with respect to lowest/highest cell voltage prediction using an adaptive neuro-fuzzy inference system (ANFIS). The estimated voltage for a given time frame in the future is directly compared with the actual voltage, verifying the effectiveness and accuracy of a relative voltage prediction error of less than 1%. Moreover, the real-time operating capability of the proposed algorithm was verified on a battery test bench while running on a real-time system performing voltage prediction.

Fleischer, Christian; Waag, Wladislaw; Bai, Ziou; Sauer, Dirk Uwe

2013-12-01

33

Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population. PMID:24566279

Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin

2014-01-01

34

NASA Astrophysics Data System (ADS)

The purpose of this study was to investigate the associations between gait performance, postural stability, and depression in patients with Parkinson's disease (PD) by using an adaptive neuro-fuzzy inference system (ANFIS). Twenty-two idiopathic PD patients were assessed during outpatient physical therapy by using three clinical tests: the Berg balance scale (BBS), Dynamic gait index (DGI), and Geriatric depression scale (GDS). Scores were determined from clinical observation and patient interviews, and associations among gait performance, postural stability, and depression in this PD population were evaluated. The DGI showed significant positive correlation with the BBS scores, and negative correlation with the GDS score. We assessed the relationship between the BBS score and the DGI results by using a multiple regression analysis. In this case, the GDS score was not significantly associated with the DGI, but the BBS and DGI results were. Strikingly, the ANFIS-estimated value of the DGI, based on the BBS and the GDS scores, significantly correlated with the walking ability determined by using the DGI in patients with Parkinson's disease. These findings suggest that the ANFIS techniques effectively reflect and explain the multidirectional phenomena or conditions of gait performance in patients with PD.

Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo

2013-03-01

35

NASA Astrophysics Data System (ADS)

In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55 m2/g) and low pore size (<22.46 Å) and average particle size lower than 48.8 Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30 min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R2) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.

Ghaedi, M.; Hosaininia, R.; Ghaedi, A. M.; Vafaei, A.; Taghizadeh, F.

2014-10-01

36

In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (<22.46Å) and average particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way. PMID:24858196

Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F

2014-10-15

37

Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system

This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation

Hikmet Esen; Mustafa Inalli; Abdulkadir Sengur; Mehmet Esen

2008-01-01

38

NASA Astrophysics Data System (ADS)

In this paper, we present away of using Anfis architecture to implement a new fuzzy logic controller chip. Anfis which tunes the fuzzy inference system with a backpropagation algorithm based on collection of input-output data makes fuzzy system to learn. This training is given from a standard response of the system and membership functions are suitably modified. For adaptive Anfis based fuzzy controller and its circuit design, we propose new circuits for implementing each controller block, and illustrate the test results and control surface of Anfis controller along with CMOS fuzzy logic controller using Matlab and Hspice software respectively. For implementing controller according to the Anfis training, we proposed new and improved integrated circuits which consist of Fuzzifier, Min operator and Multiplier/Divider. The control surfaces of controller are obtained by using Anfis training and simulation results of integrated circuits in less than 0.075 mm2 area in 0.35 ?m CMOS standard technology.

Aminifar, S.; Yosefi, Gh.

2007-09-01

39

Adaptive neuro-fuzzy estimation of optimal lens system parameters

NASA Astrophysics Data System (ADS)

Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.

Petkovi?, Dalibor; Pavlovi?, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani

2014-04-01

40

Using acceleration measurements and neuro-fuzzy systems for monitoring and diagnosis of bearings

NASA Astrophysics Data System (ADS)

Ball bearing is an important type of bearings. The radial acceleration of ball bearings has been measured for monitoring and diagnosis. Feature extraction is used to extract essential features from the experimental data. Three features, including peak amplitude of the frequency domain, percent power, and peak RMS, have been extracted from the radial acceleration of ball bearings. Then Sequential Forward Search Algorithm (SFS? was utilized for feature selection in order to effectively obtain the best vibration features. Adaptive Neuro Fuzzy Inference Systems (ANFIS) have been used. The selected features were the inputs to the neuro-fuzzy system. Whether there is a defect or not and what types of defects were the outputs of this system. Although there is no analytical relationship between the input and the output of the neuro-fuzzy system, this system still can establish the input/output relationship. In other words, this approach can most accurately, most quickly, and most reliably determine whether there is a defect or not and what types of defects, which is very important for preventive monitoring, diagnosis, and maintenance of ball bearings.

Liu, Tien-I.; Lee, Junyi; Singh, Palvinder; Liu, George

2013-10-01

41

The classical methods for detecting the micro biological pollution in water are based on the detection of the coliform bacteria which indicators of contamination. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study, we propose a novel intelligent system which provides a detection of microbiological pollution in fresh

S. Bouharati; K. Benmahammed; D. Harzallah; Y. M. El-Assaf

2008-01-01

42

Neuro-fuzzy modeling and control

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce

JYH-SHING ROGER JANG; Chuen-Tsai Sun

1995-01-01

43

The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage of expert knowledge of

S. Jassar; Z. Liao; L. Zhao

2009-01-01

44

A neuro-fuzzy approach for prediction of human work efficiency in noisy environment

A neuro-fuzzy computing provides the system identification and interpretability of fuzzy models and learning capability of neural networks in a single system. In the last decade, various neuro-fuzzy systems have been developed. Among them, adaptive neuro-fuzzy inference system (ANFIS) provides a systematic and directed approach for model building and gives the best possible design parameters in minimum time. They have

Zaheeruddin; Garima

2006-01-01

45

NASA Astrophysics Data System (ADS)

The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) to estimate the cross-talk contamination of 99 m Tc / 201 Tl image acquisition in the 201 Tl energy window (77 ± 15% keV). GATE (Geant4 Application in Emission and Tomography) is employed due to its ability to simulate multiple radioactive sources concurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. In the real and the simulation studies, data acquisition is carried out using eight energy windows. The ANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training data set. Three indications are evaluated and compared. The ANFIS method yields better outcomes for two indications (Spearman's rank correlation coefficient and contrast) and the two phantom results in each category. The maximum image biasing, which is the third indication, is found to be 6% more than that for the ANN.

Heidary, Saeed; Setayeshi, Saeed; Ghannadi-Maragheh, Mohammad

2014-09-01

46

A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments.

This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines. PMID:22010155

del Campo, Inés; Basterretxea, Koldo; Echanobe, Javier; Bosque, Guillermo; Doctor, Faiyaz

2012-04-01

47

NASA Astrophysics Data System (ADS)

Type 316 LN stainless steel is the major structural material used in the construction of nuclear reactors. Activated flux tungsten inert gas (A-TIG) welding has been developed to increase the depth of penetration because the depth of penetration achievable in single-pass TIG welding is limited. Real-time monitoring and control of weld processes is gaining importance because of the requirement of remoter welding process technologies. Hence, it is essential to develop computational methodologies based on an adaptive neuro fuzzy inference system (ANFIS) or artificial neural network (ANN) for predicting and controlling the depth of penetration and weld bead width during A-TIG welding of type 316 LN stainless steel. In the current work, A-TIG welding experiments have been carried out on 6-mm-thick plates of 316 LN stainless steel by varying the welding current. During welding, infrared (IR) thermal images of the weld pool have been acquired in real time, and the features have been extracted from the IR thermal images of the weld pool. The welding current values, along with the extracted features such as length, width of the hot spot, thermal area determined from the Gaussian fit, and thermal bead width computed from the first derivative curve were used as inputs, whereas the measured depth of penetration and weld bead width were used as output of the respective models. Accurate ANFIS models have been developed for predicting the depth of penetration and the weld bead width during TIG welding of 6-mm-thick 316 LN stainless steel plates. A good correlation between the measured and predicted values of weld bead width and depth of penetration were observed in the developed models. The performance of the ANFIS models are compared with that of the ANN models.

Subashini, L.; Vasudevan, M.

2012-02-01

48

Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro-Fuzzy

energy demand. The mathematical modeling and simulation of the photovoltaic system is implementedMaximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS) like ANFIS. This paper presents Maximum Power Point Tracking Control for Photovoltaic System Using

Paris-Sud XI, UniversitÃ© de

49

Neuro-fuzzy control of an MDOF building with a magnetorheological damper using acceleration feedback

Parameter specification of a fuzzy inference system (HS) with the aid of artificial neural networks allows the creation of complex, multi-dimensional models that are computationally efficient and numerically robust. An adaptive neuro-fuzzy inference...

Schurter, Kyle Christopher

2012-06-07

50

Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm

Abstract—Most methods of fuzzy rule-based system identifica- tion (SI) either ignore feature analysis or do it in a separate phase. This paper proposes a novel neuro-fuzzy system that can simulta- neously do feature analysis and SI in an integrated manner. It is a five-layered feed-forward network for realizing a fuzzy rule-based system. The second layer of the net is the

Debrup Chakraborty; Nikhil R. Pal

2001-01-01

51

Real-time neuro-fuzzy systems for adaptive control of musical processes

NASA Astrophysics Data System (ADS)

We have added a real-time interactive fuzzy reasoning system and neural network simulator to the MAX real-time music programming language. This environment allows us to quickly prototype and experiment with Neural, Fuzzy, and Neuro-Fuzzy systems for control of real- time musical processes. In this paper we introduce our tools and discuss musical contexts that call for the adaptive and generalization capabilities of these systems.

Lee, Michael; Wessel, David

1993-12-01

52

A new document management system is proposed in this paper. Its kernel is based on a new set of neuro-fuzzy systems of the ART family: FasArt and RFasArt. The first one, FasArt, is used to support a simple Optical Character Recognition (OCR) that inherits fine properties of ART architectures, such as fast and incremental learning, stability and modularity. On the

G. I. Sainz Palmero; Y. A. Dimitriadis; R. Sanz Guadarrama; J. M. Cano Izquierdo

2002-01-01

53

Development and realization of bucket wheel excavator knowledge-based neuro-fuzzy control system

Development of a new control system, which significantly increases excavating capacity, as well as availability, and reliability of the bucket wheel excavator, is presented in this paper. Reference of slewing speed and controller parameters are adapted by predicting cutting resistance of materials to be excavated. The predictive-adaptive higher-level control system is realized as a neuro-fuzzy controller. The fuzzy rules for

Branislav T. Jevtovic; Miroslav R. Matausek; Danilo J. Oklobdzija

2008-01-01

54

Nonlinear System Control Using Functional-link-based Neuro-fuzzy Networks

This study presents a functional-link-based neuro-fuzzy network (FLNFN) structure for nonlinear system control. The proposed\\u000a FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal\\u000a polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed\\u000a FLNFN model is a nonlinear

Chin-Teng Lin; Cheng-Hung Chen; Cheng-Jian Lin

55

Development of an adaptive neuro-fuzzy method for supply air pressure control in HVAC system

An adaptive neuro-fuzzy (ANF) method is developed for the supply air pressure control loop of a heating, ventilation and air-conditioning (HVAC) system. Although a well-tuned PID controller performs well around normal working points, its tolerance to process parameter variations is severely affected due to the nature of PID controllers. The ANF controller developed overcomes this weakness. The controller design involves

Wu Jian; Cai Wenjian

2000-01-01

56

Learning from noisy information in FasArt and FasBack neuro-fuzzy systems.

Neuro-fuzzy systems have been in the focus of recent research as a solution to jointly exploit the main features of fuzzy logic systems and neural networks. Within the application literature, neuro-fuzzy systems can be found as methods for function identification. This approach is supported by theorems that guarantee the possibility of representing arbitrary functions by fuzzy systems. However, due to the fact that real data are often noisy, generation of accurate identifiers is presented as an important problem. Within the Adaptive Resonance Theory (ART), PROBART architecture has been proposed as a solution to this problem. After a detailed comparison of these architectures based on their design principles, the FasArt and FasBack models are proposed. They are neuro-fuzzy identifiers that offer a dual interpretation, as fuzzy logic systems or neural networks. FasArt and FasBack can be trained on noisy data without need of change in their structure or data preprocessing. In the simulation work, a comparative study is carried out on the performances of Fuzzy ARTMAP, PROBART, FasArt and FasBack, focusing on prediction error and network complexity. Results show that FasArt and FasBack clearly enhance the performance of other models in this important problem. PMID:11411629

Cano Izquierdo, J M; Dimitriadis, Y A; Gómez Sánchez, E; López Coronado, J

2001-05-01

57

A Neuro-Fuzzy Self Built System For Prognostics: a Way To Ensure Good Prediction Accuracy of a neuro-fuzzy predictor whose architecture is partially determined thanks to a statistical approach based of reliability, availability or safety of a system is a determining factor in regard with the effectiveness

Paris-Sud XI, UniversitÃ© de

58

The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications.

Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza

2014-01-01

59

Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control

NASA Astrophysics Data System (ADS)

The paper presents neuro-fuzzy models' application appropriate for tobacco control: the fuzzy control model, Adaptive Network Based Fuzzy Inference System, Evolving Fuzzy Neural Network models, and EVOlving POLicies. We propose further the use of Fuzzy Casual Networks to help tobacco control decision makers develop policies and measure their impact on social regulation.

Petrovic-Lazarevic, Sonja; Zhang, Jian Ying

2007-12-01

60

Neuro-fuzzy Controlled Autonomous Mobile Robotics System

This article discusses a Neurofuzzy navigation strategy for sensor-based mobile robotics system. A Transputer computation power is used to carry out complicated needed computation (reading sensors data, deciding actions, outputting wheels data, … system monitoring). Robot control mythology was run on a parallel computing environment known as Transputers. The Transputer embedded real-time controller was used on board the robot to

Khalid Al Mutib; Ebrahim Mattar

2011-01-01

61

Adaptive neuro-fuzzy control of systems with time delay

The authors present an adaptive fuzzy logic controller, which learns about the dynamic of the system under control from an online neural network (NN) identification algorithm. The identification is based on the estimation of parameters of a First-Order-Plus-Dead-Time (FOPDT) model. The outputs of the NN are three parameters: gain, apparent time delay and the dominant time constant. By combining this

H. F. Ho; Y. K. Wong; A. B. Rad

2001-01-01

62

A neuro-fuzzy system for extracting environment features based on ultrasonic sensors.

In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160

Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José

2009-01-01

63

A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors

In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160

Marichal, Graciliano Nicolas; Hernandez, Angela; Acosta, Leopoldo; Gonzalez, Evelio Jose

2009-01-01

64

Position control of ionic polymer metal composite actuator based on neuro-fuzzy system

NASA Astrophysics Data System (ADS)

This paper describes the application of Neuro-Fuzzy techniques for controlling an IPMC cantilever configuration under water to improve tracking ability for an IPMC actuator. The controller was designed using an Adaptive Neuro-Fuzzy Controller (ANFC). The measured input data based including the tip-displacements and electrical signals have been recorded for generating the training in the ANFC. These data were used for training the ANFC to adjust the membership functions in the fuzzy control algorithm. The comparison between actual and reference values obtained from the ANFC gave satisfactory results, which showed that Adaptive Neuro-Fuzzy algorithm is reliable in controlling IPMC actuator. In addition, experimental results show that the ANFC performed better than the pure fuzzy controller (PFC). Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the real-time control of the ionic polymer metal composite actuator for which the performance degrades under long-term actuation.

Nguyen, Truong-Thinh; Yang, Young-Soo; Oh, Il-Kwon

2009-07-01

65

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

NASA Technical Reports Server (NTRS)

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

66

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production. PMID:22991575

Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A.

2012-01-01

67

Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.

In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not vulnerable to initial design assumptions. Simulations on selected examples of well-known benchmarks illustrate the potency of the method. PMID:23924413

Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios

2013-10-01

68

In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate. PMID:21431593

Das, Arpita; Bhattacharya, Mahua

2011-01-01

69

Neuro-fuzzy control of a steam boiler-turbine unit

Conceptually, fuzzy logic possesses the quality of simplicity. However, its early applications relied on trial and error in selecting either the fuzzy membership functions or the fuzzy rules. This made it depend rather too heavily on expert knowledge which may not always be available. Hence, a self-tuning or an adaptive fuzzy logic controller (FLC) such as Adaptive Neuro-Fuzzy Inference System

Fahd A. Alturki; Adel Ben Abdennour

1999-01-01

70

Neuro-fuzzy controller to navigate an unmanned vehicle.

A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). PMID:23705105

Selma, Boumediene; Chouraqui, Samira

2013-12-01

71

A Walking Hexapod Controlled by a Neuro-Fuzzy System F. Berardi, M. Chiaberge, E. Miranda and L a walking hexapod. 1 Introduction Real-time control of non-linear plants 1, 2 is often a hard- trol blocks of the walking hexapod, as a way to specify the cooperation between individual legs

Reyneri, Leonardo

72

NASA Astrophysics Data System (ADS)

In this study, energy and exergy analysis of the Afyon geothermal district heating system (AGDHS) in Afyon, Turkey using artificial neural network (ANN) and adaptive neuro-fuzzy (ANFIS) methods is carried out. Actual system data in the analysis of the AGDHS are used. The results of ANN are compared with ANFIS in which the same data sets are used. ANN model is slightly better than ANFIS in determining the energy and exergy rates. In addition, new formulations obtained from ANN are presented for the determination of the energy and exergy rates of the AGDHS. The R2-values obtained when unknown data were used in the networks were 0.999999847 and 0.99999997 for the energy and exergy rates respectively, which are very satisfactory.

?encan ?ahin, Arzu; Yaz?c?, Hilmi

2012-07-01

73

Approximation abilities of neuro-fuzzy networks

NASA Astrophysics Data System (ADS)

The paper presents the operation of two neuro-fuzzy systems of an adaptive type, intended for solving problems of the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems being a combination of the methodology of artificial neural networks and fuzzy sets operate on the basis of a set of fuzzy rules "if-then", generated by means of the self-organization of data grouping and the estimation of relations between fuzzy experiment results. The article includes a description of neuro-fuzzy systems by Takaga-Sugeno-Kang (TSK) and Wang-Mendel (WM), and in order to complement the problem in question, a hierarchical structural self-organizing method of teaching a fuzzy network. A multi-layer structure of the systems is a structure analogous to the structure of "classic" neural networks. In its final part the article presents selected areas of application of neuro-fuzzy systems in the field of geodesy and surveying engineering. Numerical examples showing how the systems work concerned: the approximation of functions of several variables to be used as algorithms in the Geographic Information Systems (the approximation of a terrain model), the transformation of coordinates, and the prediction of a time series. The accuracy characteristics of the results obtained have been taken into consideration.

Mrówczy?ska, Maria

2010-01-01

74

Identification of trash types in ginned cotton using neuro fuzzy techniques

Discusses the use of soft computing techniques such as neural networks and fuzzy logic based approaches in the identification of various types of trash (non-lint material\\/foreign matter) in ginned cotton. Lint is the cotton fiber; non-lint or foreign matter is everything other than lint. The effectiveness of a hybrid neuro-fuzzy structure, namely the adaptive-network-based fuzzy inference system to classify trash

Murali Siddaiah; Michael A. Lieberman; Nadipuram R. Prasad

1999-01-01

75

PREOPERATIVE OVARIAN CANCER DIAGNOSIS USING NEURO-FUZZY APPROACH E.O. Madu, V. Stalbovskaya, B the Adaptive Network based Fuzzy Inference System (ANFIS). Our model predicts ovarian cancer malignancy using operating characteristic curve of 0.85. Keywords: ovarian cancer, medical diagnosis, neural networks, neuro

76

A phenomenological dynamic model of a magnetorheological damper using a neuro-fuzzy system

NASA Astrophysics Data System (ADS)

A magnetorheological (MR) damper is a promising appliance for semi-active suspension systems, due to its capability of damping undesired movement using an adequate control strategy. This research has been carried out a phenomenological dynamic model of two MR dampers using an adaptive-network-based fuzzy inference system (ANFIS) approach. Two kinds of Lord Corporation MR damper (a long stroke damper and a short stroke damper) were used in experiments, and then modeled using the experimental results. In addition, an investigation of the influence of the membership function selection on predicting the behavior of the MR damper and obtaining a mathematical model was conducted to realize the relationship between input current, displacement, and velocity as the inputs and force as output. The results demonstrate that the proposed models for both short stroke and long stroke MR dampers have successfully predicted the behavior of the MR damper with adequate accuracy, and an equation is presented to precisely describe the behavior of each MR damper.

Zeinali, Mohammadjavad; Amri Mazlan, Saiful; Yasser Abd Fatah, Abdul; Zamzuri, Hairi

2013-12-01

77

Error estimation of a neuro-fuzzy predictor for prognostic purpose

Error estimation of a neuro-fuzzy predictor for prognostic purpose Mohamed El-Koujok, Rafael is necessary: it starts from monitoring data and goes through provisional reliability and remaining useful life of the evolving eXtended Tagaki-Sugeno system as a neuro- fuzzy predictor. A method to estimate the probability

Paris-Sud XI, UniversitÃ© de

78

Only a few studies in the literature have focused on the effects of age on virtual environment (VE) sickness susceptibility and even less research was carried out focusing on the elderly. In general, the elderly usually browse VEs on a thin film transistor liquid crystal display (TFT-LCD) at home or somewhere, not a head-mounted display (HMD). While the TFT-LCD is used to present VEs, this set-up does not physically enclose the user. Therefore, this study investigated the factors that contribute to cybersickness among the elderly when immersed into a VE on TFT-LCD, including exposure durations, navigation rotating speeds and angle of inclination. Participants were elderly, with an average age of 69.5 years. The results of the first experiment showed that the rate of simulator sickness questionnaire (SSQ) scores increases significantly with navigational rotating speed and duration of exposure. However, the experimental data also showed that the rate of SSQ scores does not increase with the increase in angle of inclination. In applying these findings, the neuro-fuzzy technology was used to develop a neuro-fuzzy cybersickness-warning system integrating fuzzy logic reasoning and neural network learning. The contributing factors were navigational rotating speed and duration of exposure. The results of the second experiment showed that the proposed system can efficiently determine the level of cybersickness based on the associated subjective sickness estimates and combat cybersickness due to long exposure to a VE. PMID:19144322

Liu, Cheng-Li

2009-05-01

79

A hybrid neuro-fuzzy power system stabilizer for multimachine power systems

A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper to improve power system dynamic stability. The proposed FBFN based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to re-tune the PSS parameters

M. A. Abido; Y. L. Abdel-Magid

1998-01-01

80

A neuro-fuzzy computing technique for modeling hydrological time series

NASA Astrophysics Data System (ADS)

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process.

Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.

2004-05-01

81

This study used the adaptive neuro-fuzzy inference system (ANFIS) and ordinary least squares (OLS) regression to forecast the R&D project performances of Taiwanese IC design companies through three explanatory variables: the fitness of project environment, R&D project manager's skills, and the effectiveness of team work. The results showed that the accuracy rate of ANFIS in this study was 65.52% better

Yu-Shan Chen; Ke-Chiun Chang

2006-01-01

82

Intelligent security system based on neuro-fuzzy multisensor data fusion

This paper presents a real-world application of neurofuzzy processing to a security system with multiple sensor. Integrating fuzzy logic with neural networks, the authors have automated the tasks of sensor data fusion and determination of false\\/true alarms, which currently rely solely on human monitoring operators, so that they operate in a way similar to human reasoning. This integrated security system

Judy Chen; Andrew A. Kostrzewski; Dai Hyun Kim; Yih-Shi Kuo; Gajendra D. Savant; Barney Roberts

1998-01-01

83

NASA Astrophysics Data System (ADS)

In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.

Prakash, S.; Sinha, S. K.

2014-08-01

84

Rate-adaptive pacemaker controlled by motion and respiratory rate using neuro-fuzzy algorithm.

Rate-adaptive pacemakers use information from sensors to change the rate of heart stimulation. Until now, fuzzy-pacemaker algorithms have been used to combine inputs from sensors to improve heart rate control, but they have been difficult to implement. In this paper, a pacemaker algorithm which controlled heart rate adaptively by motion and respiratory rate was studied. After chronotropic assessment exercise protocol (CAEP) tests were performed to collect activity and respiratory rate signals, the intrinsic heart rate was inferred from these two signals by a neuro-fuzzy method. For 10 subjects the heart rate inference, using the neuro-fuzzy algorithm, gave 52.4% improved accuracy in comparison with the normal fuzzy table look-up method. The neuro-fuzzy method was applied to a real pacemaker by reduced mapping of the neuro-fuzzy look-up table. PMID:11804178

Shin, J W; Yoon, J H; Yoon, Y R

2001-11-01

85

A Temperature Controlled System for Car Air Condition Based on Neuro-fuzzy

Neural networks are good at recognizing patterns, but they are not good at explaining how they reach their decisions, while fuzzy logic systems, which can reason with imprecise information, are good at explaining their decisions but they cannot automatically acquire the rules they use to make those decisions. These limitations have been a central driving force behind the creation of

Bingqiang He; Rongguang Liang; Jianghong Wu; Xihui Wang

2009-01-01

86

Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems

NASA Technical Reports Server (NTRS)

Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic.

Gulati, Sandeep; Toomarian, Nikzad; Barhen, Jacob; Maccalla, Ayanna; Tawel, Raoul; Thakoor, Anil; Daud, Taher

1994-01-01

87

Adaptive Neuro-Fuzzy Control of Systems with Unknown Time Delay

We present an adaptive fuzzy logic controller, which learns a lower-order model of the system via an on-line Neural Network (NN) identification algorithm. The identification is based on the estimation of parameters of a First-Order-Plus-Dead-Time (FOPDT) model. The outputs of the NN are three parameters: gain, apparent time delay and the dominant time constant. By combining this algorithm with a

F. H. Ho; A. B. Rad; Y. K. Wong; W. L. Lo

88

Neuro-fuzzy synthesis of flight control electrohydraulic servo

Presents a switching type neuro-fuzzy control synthesis. The control algorithm supposes as a component part a neurocontrol designed to optimize a performance index. Whenever the neurocontrol saturates or a certain performance parameter of the system decreases, the scheme of control switches to a feasible and reliable fuzzy logic control. Describes the procedure of return to the optimizing neurocontrol which is

Ioan Ursu; Felicia Ursu; Lucian Iorga

2001-01-01

89

The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks. PMID:20411596

Theodoridis, Dimitrios; Boutalis, Yiannis; Christodoulou, Manolis

2010-04-01

90

Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies. PMID:25406641

Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla

2014-12-01

91

NASA Astrophysics Data System (ADS)

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

92

Daily soil temperature modeling using neuro-fuzzy approach

NASA Astrophysics Data System (ADS)

Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models' performances at arid and semiarid locations showed that the CANFIS models' performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.

Hosseinzadeh Talaee, P.

2014-01-01

93

A neuro-fuzzy based oil\\/gas producibility estimation method

We present a hybrid neuro-fuzzy technique for predicting producibility of a well. First, multilayer neural networks are used to compute petrophysical parameters such as quality control curves and permeability. In particular, neural networks are used to predict the permeability from nuclear magnetic resonance (NMR) logs. Next, the permeability is used as one of the input to a fuzzy logic inference

Heidar A. Malki; Jeff Baldwin

2002-01-01

94

Recognition of Handwritten Arabic words using a neuro-fuzzy network

We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.

Boukharouba, Abdelhak [Departement de Genie electrique, Universite 08 Mai 45 de Guelma (Algeria); Bennia, Abdelhak [Departement d'Electronique, Universite Mentouri de Constantine (Algeria)

2008-06-12

95

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

NASA Astrophysics Data System (ADS)

In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root mean square error (RMSE), absolute error mean (AEM) and scatter index (SI) between target and predicted sand fraction values. The achieved estimation accuracy may diverge minutely depending on geological characteristics of a particular study area. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.

Verma, Akhilesh K.; Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.; Jenamani, Mamata

2014-12-01

96

Neuro-Fuzzy Phasing of Segmented Mirrors

NASA Technical Reports Server (NTRS)

A new phasing algorithm for segmented mirrors based on neuro-fuzzy techniques is described. A unique feature of this algorithm is the introduction of an observer bank. Its effectiveness is tested in a very simple model with remarkable success. The new algorithm requires much less computational effort than existing algorithms and therefore promises to be quite useful when implemented on more complex models.

Olivier, Philip D.

1999-01-01

97

The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects. PMID:22256169

Favieiro, Gabriela W; Balbinot, Alexandre

2011-01-01

98

The strategy of building a flood forecast model by neuro-fuzzy network

NASA Astrophysics Data System (ADS)

A methodology is proposed for constructing a flood forecast model using the adaptive neuro-fuzzy inference system (ANFIS). This is based on a self-organizing rule-base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall-runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self-constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back-propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model.

Chen, Shen-Hsien; Lin, Yong-Huang; Chang, Li-Chiu; Chang, Fi-John

2006-04-01

99

In our days the importance of reducing the inventory level in a healthcare organization is increasing fast. As a result, the\\u000a value of an accurate supply forecast is becoming more relevant. The main objective of this paper is to analyze and compare\\u000a some of the most popular and widely applied techniques available based on computational intelligence. In addition, it aims

Dimitrios E. Koulouriotis; Georgios Mantas

100

A Neuro-Fuzzy Systems for Control Applications F. Berardi, M. Chiaberge, E. Miranda and L.M. Reyneri

a walking hexapod. 1 Introduction Real-time control of non-linear plants 1, 2 is often a hard respectively. As application of this system, section 5 describes a walking hexapod controlled by our neuro

Reyneri, Leonardo

101

Monitoring and predicting machine components' faults play an important role in maintenance actions. Developing an intelligent system is a good way to overcome the problems of maintenance management. In fact, several methods of fault diagnostics have been developed and applied effectively to identify the machine faults at an early stage using different quantities (Measures or Readings) such as current, voltage,

M. Samhouri; A. Al-Ghandoor; S. A. Ali; I. Hinti; W. Massad

102

Adaptive neuro-fuzzy approach for predicting hardness of deposited TiN/ZrN multilayer coatings.

This paper presents an adaptive neuro-fuzzy approach based on first order function of fuzzy model for establishing the relationship between control factors and thin films properties of TiN/ZrN coatings on Si(100) wafer substrates. A statistical model was designed to explore the space of the processes by an orthogonal array scheme. Eight control factors of closed unbalance magnetron sputtering system were selected for modeling the process, such as interlayer material, argon and nitrogen flow rate, titanium and zirconium target current, rotation speed, work distance, and bias voltage. Analysis of variance (ANOVA) was carried out for determining the influence of control factors. In this study, with the application of ANOVA, the smallest effect of control factors was eliminated. The adaptive neuro-fuzzy inference system (ANFIS) was applied as a tool to model the deposited process with five significant control factors. The experimental results show that ANFIS demonstrates better accuracy than additive model for the film hardness. The root mean square error between prediction values and experimental values were archived to 0.04. PMID:21128476

Yang, Yu-Sen; Huang, Wesley; Huang, Guo-Ping; Chou, Jyh-Horng

2010-07-01

103

Tuning of a neuro-fuzzy controller by genetic algorithm.

Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance. PMID:18252294

Seng, T L; Bin Khalid, M; Yusof, R

1999-01-01

104

Knowledge discovery by a neuro-fuzzy modeling framework

In this paper a neuro-fuzzy modeling framework is proposed, which is devoted to discover knowledge from data and represent it in the form of fuzzy rules. The core of the framework is a knowledge extraction procedure that is aimed to identify the structure and the parameters of a fuzzy rule base, through a two-phase learning of a neuro-fuzzy network. In

Giovanna Castellano; Ciro Castiello; Anna Maria Fanelli; Corrado Mencar

2005-01-01

105

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency. PMID:18244440

Sinha, S K; Karray, F

2002-01-01

106

Terrorism Event Classification Using Fuzzy Inference Systems

Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluat...

Inyaem, Uraiwan; Meesad, Phayung; Tran, Dat

2010-01-01

107

Adaptive Neuro-Fuzzy Methodology for Noise Assessment of Wind Turbine

Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. PMID:25075621

Shamshirband, Shahaboddin; Petkovic, Dalibor; Hashim, Roslan; Motamedi, Shervin

2014-01-01

108

Prediction of Solar Activity Based on Neuro-Fuzzy Modeling

NASA Astrophysics Data System (ADS)

This paper presents an application of the neuro-fuzzy modeling to analyze the time series of solar activity, as measured through the relative Wolf number. The neuro-fuzzy structure is optimized based on the linear adapted genetic algorithm with controlling population size (LAGA-POP). Initially, the dimension of the time series characteristic attractor is obtained based on the smallest regularity criterion (RC) and the neuro-fuzzy model. Then the performance of the proposed approach, in forecasting yearly sunspot numbers, is favorably compared to that of other published methods. Finally, a comparison predictions for the remaining part of the 22nd and the whole 23rd cycle of the solar activity are presented.

Attia, Abdel-Fattah; Abdel-Hamid, Rabab; Quassim, Maha

2005-03-01

109

Neuro-Fuzzy Dynamic Obstacle Avoidance for Autonomous Robot Manipulators

NASA Astrophysics Data System (ADS)

This paper presents an integration of fuzzy local planner and modified Elman neural networks (MENN) approximation-based computed-torque controller for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The navigation is based on fuzzy technique for the idea of artificial potential fields (APF) using analytic harmonic functions. Unlike fuzzy technique, the development of APF is computationally intensive operation. The MENN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a Neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.

Mbede, Jean Bosco; Ele, Pierre; Xinhan, Huang

110

Recurrent neuro-fuzzy networks for nonlinear process modeling

A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of

Jie Zhang; A. Julian Morris

1999-01-01

111

For a safe flight and accurate air navigation, the key data are calculated using Air Data Computer (ADC). Altitude information is one of the important parameter computed by the ADC. According to aircraft type, accuracy of the altitude corrected using tables or charts showing the actual corrections in altitude information special to the aircraft. This correction is important in any

Ilke Turkmen; Yasin Korkmaz

2011-01-01

112

Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

NASA Astrophysics Data System (ADS)

Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1 h, 24 h, 48 h and 72 h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.

Karimi, Sepideh; Kisi, Ozgur; Shiri, Jalal; Makarynskyy, Oleg

2013-03-01

113

NASA Astrophysics Data System (ADS)

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.

Lin, J.; Zheng, Y. B.

2012-07-01

114

An adaptive neuro-fuzzy inference system (ANFIS) with a supervisory control system (SCS) was used to predict the occurrence of gait events using the electromyographic (EMG) activity of lower extremity muscles in the child with cerebral palsy (CP). This is anticipated to form the basis of a control algorithm for the application of electrical stimulation (ES) to leg or ankle muscles in an attempt to improve walking ability. Either surface or percutaneous intramuscular electrodes were used to record the muscle activity from the quadriceps muscles, with concurrent recording of the gait cycle performed using a VICON motion analysis system for validation of the ANFIS with SCS. Using one EMG signal and its derivative from each leg as its inputs, the ANFIS with SCS was able to predict all gait events in seven out of the eight children, with an average absolute time differential between the VICON recording and the ANFIS prediction of less than 30 ms. Overall accuracy in predicting gait events ranged from 98.6% to 95.3% (root mean-squared error between 0.7 and 1.5). Application of the ANFIS with the SCS to the prediction of gait events using EMG data collected two months after the initial data demonstrated comparable results, with no significant differences between gait event detection times. The accuracy rate and robustness of the ANFIS with SCS with two EMG signals suggests its applicability to ES control. PMID:16189966

Lauer, Richard T; Smith, Brian T; Betz, Randal R

2005-09-01

115

As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations. PMID:22773047

Kayacan, Erkan; Kayacan, Erdal; Ramon, Herman; Saeys, Wouter

2012-07-01

116

In maintenance field, prognostics is recognized as a key feature as the prediction of the remaining useful life of a system allows avoiding inopportune maintenance spending. However, it can be a non trivial task to develop and implement effective prognostics models including the inherent uncertainty of prognostics. Moreover, there is no systematic way to construct a prognostics tool since the

Mohamed El-Koujok; Rafael Gouriveau; Noureddine Zerhouni

2010-01-01

117

Neuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines

Neuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines Kasim M. Al which consists of groups of search coils are inserted into the motor stator. A simple neuro-fuzzy in washing machine applications where simplicity, reliability and stability are more important issues

118

VSS Theory Based Training of a Fuzzy Motion Control System

This paper presents a novel training algorithm for adaptive neuro-fuzzy inference systems. The algorithm combines the error backpropagation algorithm with variable structure systems approach. Expressing the parameter update rule as a dynamic system in continuous time and applying sliding mode control (SMC) method to the dynamic model of the gradient based training procedure results in the parameter stabilizing part of

M. Onder Efe; A. Murat Fiskiran; Okyay Kaynak; Imre J. Rudas

119

Neuro-fuzzy control of structures using acceleration feedback

NASA Astrophysics Data System (ADS)

This paper described a new approach for the reduction of environmentally induced vibration in constructed facilities by way of a neuro-fuzzy technique. The new control technique is presented and tested in a numerical study that involves two types of building models. The energy of each building is dissipated through magnetorheological (MR) dampers whose damping properties are continuously updated by a fuzzy controller. This semi-active control scheme relies on the development of a correlation between the accelerations of the building (controller input) and the voltage applied to the MR damper (controller output). This correlation forms the basis for the development of an intelligent neuro-fuzzy control strategy. To establish a context for assessing the effectiveness of the semi-active control scheme, responses to earthquake excitation are compared with passive strategies that have similar authority for control. According to numerical simulation, MR dampers are less effective control mechanisms than passive dampers with respect to a single degree of freedom (DOF) building model. On the other hand, MR dampers are predicted to be superior when used with multiple DOF structures for reduction of lateral acceleration.

Schurter, Kyle C.; Roschke, Paul N.

2001-08-01

120

An intelligent load shedding scheme using neural networks and neuro-fuzzy.

Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper. PMID:20039470

Haidar, Ahmed M A; Mohamed, Azah; Al-Dabbagh, Majid; Hussain, Aini; Masoum, Mohammad

2009-12-01

121

A systematic neuro-fuzzy modeling framework with application to material property prediction.

A systematic neural-fuzzy modeling framework that includes the initial fuzzy model self-generation, significant input selection, partition validation, parameter optimization, and rule-base simplification is proposed in this paper. In this framework, the structure identification and parameter optimization are carried out automatically and efficiently by the combined use of a sell-organization network, fuzzy clustering, adaptive back-propagation learning, and similarity analysis-based model simplification. The proposed neuro-fuzzy modeling approach has been used for nonlinear system identification and mechanical property prediction in hot-rolled steels from construct composition and microstructure data. Experimental studies demonstrate that the predicted mechanical properties have a good agreement with the measured data by using the elicited fuzzy model with a small number of rules. PMID:18244842

Chen, M Y; Linkens, D A

2001-01-01

122

A genetic-based neuro-fuzzy approach for prediction of solar activity

NASA Astrophysics Data System (ADS)

This paper presents an application of the neuro-fuzzy modeling to analyze the time series of solar activity, as measured through the relative Wolf number. The neuro-fuzzy structure will be optimized based on the linear adapted genetic algorithm with controlling population size (LAGA-POP). First, the dimension of the time series characteristic attractor is obtained based on the smallest Regularity Criterion (RC) and the neuro-fuzzy modeling. Second, after describing the neuro-fuzzy structure and optimizing its parameters based on LAGA-POP, the performance of the present approach in forecasting yearly sunspot numbers is favorably compared to that of other published methods. Finally, the comparison predictions for the remaining part of the 22nd and the whole 23rd cycle of solar activity are presented.

Attia, Abdel-Fattah A.; Abdel-Hamid, Rabab H.; Quassim, Maha

2004-09-01

123

A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance

Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions. PMID:24302928

2013-01-01

124

Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration

NASA Technical Reports Server (NTRS)

Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.

Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza

2003-01-01

125

Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.

This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach. PMID:14629866

Carrascal, A; Manrique, D; Ríos, J; Rossi, C

2003-01-01

126

An effective neuro-fuzzy paradigm for machinery condition health monitoring.

An innovative neuro-fuzzy network appropriate for fault detection and classification in a machinery condition health monitoring environment is proposed. The network, called an incremental learning fuzzy neural (ILFN) network, uses localized neurons to represent the distributions of the input space and is trained using a one-pass, on-line, and incremental learning algorithm that is fast and can operate in real time. The ILFN network employs a hybrid supervised and unsupervised learning scheme to generate its prototypes. The network is a self-organized structure with the ability to adaptively learn new classes of failure modes and update its parameters continuously while monitoring a system. To demonstrate the feasibility and effectiveness of the proposed network, numerical simulations have been performed using some well-known benchmark data sets, such as the Fisher's Iris data and the Deterding vowel data set. Comparison studies with other well-known classifiers were performed and the ILFN network was found competitive with or even superior to many existing classifiers. The ILFN network was applied on the vibration data known as Westland data set collected from a U.S. Navy CH-46E helicopter test stand, in order to assess its efficiency in machinery condition health monitoring. Using a simple fast Fourier transform (FFT) technique for feature extraction, the ILFN network has shown promising results. With various torque levels for training the network, 100% correct classification was achieved for the same torque Levels of the test data. PMID:18244819

Yen, G G; Meesad, P

2001-01-01

127

Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling

NASA Technical Reports Server (NTRS)

Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.

Chiu, Stephen

1996-01-01

128

Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

NASA Astrophysics Data System (ADS)

SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.

Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik

2013-04-01

129

Neuro-fuzzy models as an IVIVR tool and their applicability in generic drug development.

The usefulness of neuro-fuzzy (NF) models as an alternative in vitro-in vivo relationship (IVIVR) tool and as a support to quality by design (QbD) in generic drug development is presented. For drugs with complicated pharmacokinetics, immediate release drugs or nasal sprays, suggested level A correlations are not capable to satisfactorily describe the IVIVR. NF systems were recognized as a reasonable method in comparison to the published approaches for development of IVIVR. Consequently, NF models were built to predict 144 pharmacokinetic (PK) parameter ratios required for demonstration of bioequivalence (BE) for 88 pivotal BE studies. Input parameters of models included dissolution data and their combinations in different media, presence of food, formulation strength, technology type, particle size, and spray pattern for nasal sprays. Ratios of PK parameters Cmax or AUC were used as output variables. The prediction performance of models resulted in the following values: 79% of models have acceptable external prediction error (PE) below 10%, 13% of models have inconclusive PE between 10 and 20%, and remaining 8% of models show inadequate PE above 20%. Average internal predictability (LE) is 0.3%, and average external predictability of all models results in 7.7%. In average, models have acceptable internal and external predictabilities with PE lower than 10% and are therefore useful for IVIVR needs during formulation development, as a support to QbD and for the prediction of BE study outcome. PMID:24477942

Opara, Jerneja; Legen, Igor

2014-03-01

130

NASA Astrophysics Data System (ADS)

The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the phenomenon which shows the relationship between the input and output parameters. This study provided new alternatives for solar radiation estimation based on temperatures.

Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

2012-04-01

131

Eliminating current sensors of Indirect Matrix Converter using neuro-fuzzy controller

This paper describes non-linear average model of Indirect Matrix Converter (IMC) with an output LC filter in stationary and rotating reference frames. The defects of pervious control strategies based on derived average model are discussed and a novel adaptive neuro-fuzzy controller is proposed. Eliminating output current sensors, good dynamic performance in any operating point without overshoot and balanced output voltages

Alireza Jahangiri; Ahmad Radan

2011-01-01

132

A Neuro-fuzzy Approach for Predicting the Effects of Noise Pollution on Human Work Efficiency

\\u000a In this paper, an attempt has been made to develop a neuro-fuzzy model for predicting the effects of noise pollution on human\\u000a work efficiency as a function of noise level, type of task, and exposure time. Originally, the model was developed using fuzzy\\u000a logic based on literature survey. So, the data used in the present study has been synthetically generated

Zaheeruddin; Garima

2004-01-01

133

ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system

The aim of this study is to demonstrate the comparison of an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) for the prediction performance of a vertical ground source heat pump (VGSHP) system. The VGSHP system using R-22 as refrigerant has a three single U-tube ground heat exchanger (GHE) made of polyethylene pipe with a 40mm outside

Hikmet Esen; Mustafa Inalli

2010-01-01

134

Adaptive neuro-fuzzy control of ionic polymer metal composite actuators

NASA Astrophysics Data System (ADS)

An adaptive neuro-fuzzy controller was newly designed to overcome the degradation of the actuation performance of ionic polymer metal composite actuators that show highly nonlinear responses such as a straightening-back problem under a step excitation. An adaptive control algorithm with the merits of fuzzy logic and neural networks was applied for controlling the tip displacement of the ionic polymer metal composite actuators. The reference and actual displacements and the change of the error with the electrical inputs were recorded to generate the training data. These data were used for training the adaptive neuro-fuzzy controller to find the membership functions in the fuzzy control algorithm. Software simulation and real-time experiments were conducted by using the Simulink and dSPACE environments. Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the reliable control of the ionic polymer metal composite actuator for which the performance degrades under long-time actuation.

Thinh, Nguyen Truong; Yang, Young-Soo; Oh, Il-Kwon

2009-06-01

135

The goal of this work is to predict the daily performance (COP) of a ground-source heat pump (GSHP) system with the minimum data set based on an adaptive neuro-fuzzy inference system (ANFIS) with a fuzzy weighted pre-processing (FWP) method. To evaluate the effectiveness of our proposal (FWP–ANFIS), a computer simulation is developed on MATLAB environment. The comparison of the proposed

Hikmet Esen; Mustafa Inalli; Abdulkadir Sengur; Mehmet Esen

2008-01-01

136

NASA Astrophysics Data System (ADS)

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

137

Application of neuro-fuzzy methods to gamma spectroscopy

NASA Astrophysics Data System (ADS)

Nuclear non-proliferation activities are an essential part of national security activities both domestic and abroad. The safety of the public in densely populated environments such as urban areas or large events can be compromised if devices using special nuclear materials are present. Therefore, the prompt and accurate detection of these materials is an important topic of research, in which the identification of normal conditions is also of importance. With gamma-ray spectroscopy, these conditions are identified as the radiation background, which though being affected by a multitude of factors is ever present. Therefore, in nuclear non-proliferation activities the accurate identification of background is important. With this in mind, a method has been developed to utilize aggregate background data to predict the background of a location through the use of an Artificial Neural Network (ANN). After being trained on background data, the ANN is presented with nearby relevant gamma-ray spectroscopy data---as identified by a Fuzzy Inference System - to create a predicted background spectra to compare to a measured spectra. If a significant deviation exists between the predicted and measured data, the method alerts the user such that a more thorough investigation can take place. Research herein focused on data from an urban setting in which the number of false positives was observed to be 28 out of a total of 987, representing 2.94% error. The method therefore currently shows a high rate of false positives given the current configuration, however there are promising steps that can be taken to further minimize this error. With this in mind, the method stands as a potentially significant tool in urban nuclear nonproliferation activities.

Grelle, Austin L.

138

NASA Astrophysics Data System (ADS)

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

Pradhan, Biswajeet

2013-02-01

139

Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis.

We propose a systematical approach to assessing arsenic concentration in a river through: important factor extraction by a nonlinear factor analysis; arsenic concentration estimation by the neuro-fuzzy network; and impact assessment of important factors on arsenic concentration by the membership degrees of the constructed neuro-fuzzy network. The arsenic-contaminated Huang Gang Creek in northern Taiwan is used as a study case. Results indicate that rainfall, nitrite nitrogen and temperature are important factors and the proposed estimation model (ANFIS(GT)) is superior to the two comparative models, in which 50% and 52% improvements in RMSE are made over ANFIS(CC) and ANFIS(all), respectively. Results reveal that arsenic concentration reaches the highest in an environment of lower temperature, higher nitrite nitrogen concentration and larger one-month antecedent rainfall; while it reaches the lowest in an environment of higher temperature, lower nitrite nitrogen concentration and smaller one-month antecedent rainfall. It is noted that these three selected factors are easy-to-collect. We demonstrate that the proposed methodology is a useful and effective methodology, which can be adapted to other similar settings to reliably model water quality based on parameters of interest and/or study areas of interest for universal usage. The proposed methodology gives a quick and reliable way to estimate arsenic concentration, which makes good contribution to water environment management. PMID:25046611

Chang, Fi-John; Chung, Chang-Han; Chen, Pin-An; Liu, Chen-Wuing; Coynel, Alexandra; Vachaud, Georges

2014-10-01

140

Experimental Validation of a Neuro-Fuzzy Approach to Phasing the SIBOA Segmented Mirror Testbed

NASA Technical Reports Server (NTRS)

NASA is preparing to launch the Next Generation Space Telescope (NGST). This telescope will be larger than the Hubble Space Telescope, be launched on an Atlas missile rather than the Space Shuttle, have a segmented primary mirror, and be placed in a higher orbit. All these differences pose significant challenges. This effort addresses the challenge of aligning the segments of the primary mirror during the initial deployment. The segments need to piston values aligned to within one tenth of a wavelength. The present study considers using a neuro-fuzzy model of the Fraunhofer diffraction theory. The intention of the current study was to experimentally verify the algorithm derived earlier. The experimental study was to be performed on the SIBOA (Systematic Image Based Optical Alignment) test bed. Unfortunately the hardware/software for SIBOA was not ready by the end of the study period. We did succeed in capturing several images of two stacked segments with various relative phases. These images can be used to calibrate the algorithm for future implementation. This effort is a continuation of prior work. The basic effort involves developing a closed loop control algorithm to phase a segmented mirror test bed (SIBOA). The control algorithm is based on a neuro-fuzzy model of SIBOA and incorporates nonlinear observers built from observer banks. This effort involves implementing the algorithm on the SIBOA test bed.

Olivier, Philip D.

2002-01-01

141

Verifying Stability of Dynamic Soft-Computing Systems

NASA Technical Reports Server (NTRS)

Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent systems that are flexible and robust. Although recent research have shown that certain class of neuro-fuzzy controllers can be proven bounded and stable, they are implementation dependent and difficult to apply to the design and validation process. Many practitioners adopt the trial and error approach for system validation or resort to exhaustive testing using prototypes. In this paper, we describe our on-going research towards establishing necessary theoretic foundation as well as building practical tools for the verification and validation of soft-computing systems. A unified model for general neuro-fuzzy system is adopted. Classic non-linear system control theory and recent results of its applications to neuro-fuzzy systems are incorporated and applied to the unified model. It is hoped that general tools can be developed to help the designer to visualize and manipulate the regions of stability and boundedness, much the same way Bode plots and Root locus plots have helped conventional control design and validation.

Wen, Wu; Napolitano, Marcello; Callahan, John

1997-01-01

142

This paper compares different classification methods of ECG signals including their accuracies. First of all , Preprocessing for ECG signal is necessary in order to detect QRS complex. Then, with the intention of extract influential features in Ischemia disease, baseline wandering and noise suppression is done. Following to above mentioned target, two neuro-fuzzy classification algorithms incorporated with two artificial neural

Hoda Tonekabonipour; Ali Emam; Mohammad Teshnehlab; Mahdi Aliyari Shoorehdeli

2010-01-01

143

A neuro-fuzzy architecture for real-time applications

NASA Technical Reports Server (NTRS)

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

Ramamoorthy, P. A.; Huang, Song

1992-01-01

144

Adaptive neuro-fuzzy fusion of sensor data

NASA Astrophysics Data System (ADS)

A framework is proposed, which consolidates the benefits of a fuzzy rationale and a neural system. The framework joins together Kalman separating and delicate processing guideline i.e. ANFIS to structure an effective information combination strategy for the target following framework. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Fuzzy versatile combination calculation is a compelling device to make the genuine quality of the leftover covariance steady with its hypothetical worth. ANFIS indicates great taking in and forecast proficiencies, which makes it a productive device to manage experienced vulnerabilities in any framework. A neural system is presented, which can concentrate the measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab/Simulink and the exhibitions are explored.

Petkovi?, Dalibor

2014-11-01

145

Active control of blade-vortex interactions using a neuro-fuzzy controller

NASA Astrophysics Data System (ADS)

Rotorcraft blade-vortex interactions (BVI) result in large pressure fluctuations over rotor blades leading to increased unsteady blade loads, noise, and vibration. Previous studies have indicated that an effective method for reducing BVI is through the use of active control schemes. As a workable dynamic model of the process for controller design is difficult to develop a rule-based fuzzy controller is used in this study. As the choice of the fuzzy controller parameters for acceptable performance depend on flight condition, a neural network is trained to adaptively modify the fuzzy controller parameters as a function of flight condition. The resulting neuro-fuzzy control scheme is evaluated using a numerical simulation model of BVI in order to demonstrate the effectiveness of the proposed scheme.

Swaminathan, Ramesh; Prasad, J. V. R.; Sankar, L. N.

1996-04-01

146

Abstract — Nowadays, the great market competition makes that the companies look for high reliability and quality of the products manufacturing. The effective ways to ensure reliability signals of the product is by offering better warranty terms and period associated with sale of the product. In fact, warranty is a legal obligation of the manufacturer or dealer in connection with the sale of the product that defines the liability of the manufacturer or dealer in the event of the premature failure or defects of the product. The purpose of this paper is to propose a method for reliability analysis of the warranty data and to validate the warranty policy. In order to applied neuro-fuzzy approach by optimizing warranty cost and period, modelling the reliability of the product must be beneficial. Index Terms — reliability, failure rate, neuro-fuzzy, warranty cost, warranty period.

Hairudin Abd Majid; Nur Izzati Jamahir; Azurah A. Samah

147

NASA Astrophysics Data System (ADS)

Shear wave velocity is a critical physical property of rock, which provides significant data for geomechanical and geophysical studies. This study proposes a multi-step strategy to construct a model estimating shear wave velocity from conventional well log data. During the first stage, three correlation structures, including power law, exponential, and trigonometric were designed to formulate conventional well log data into shear wave velocity. Then, a Genetic Algorithm-Pattern Search tool was used to find the optimal coefficients of these correlations. Due to the different natures of these correlations, they might overestimate/underestimate in some regions relative to each other. Therefore, a neuro-fuzzy algorithm is employed to combine results of intelligently derived formulas. Neuro-fuzzy technique can compensate the effect of overestimation/underestimation to some extent, through the use of fuzzy rules. One set of data points was used for constructing the model and another set of unseen data points was employed to assess the reliability of the propounded model. Results have shown that the hybrid genetic algorithm-pattern search technique is a robust tool for finding the most appropriate form of correlations, which are meant to estimate shear wave velocity. Furthermore, neuro-fuzzy combination of derived correlations was capable of improving the accuracy of the final prediction significantly.

Asoodeh, Mojtaba; Bagheripour, Parisa

2013-06-01

148

NASA Astrophysics Data System (ADS)

Image alignment is considered a key problem in visual inspection applications. The main concerns for such tasks are fast image alignment with subpixel accuracy. About this, neural network-based approaches are very popular in visual inspection because of their high accuracy and efficiency of aligning images. However, such methods are difficult to identify the structure and parameters of neural network. In this study, a Takagi-Sugeno-Kang-type neuro-fuzzy network (NFN) with data-mining-based evolutionary learning algorithm (DMELA) is proposed. Compared with traditional learning algorithms, DMELA combines the self-organization algorithm (SOA), data-mining selection method (DMSM), and regularized least square (RLS) method to not only determine a suitable number of fuzzy rules, but also automatically tune the parameters of NFN. Experimental results are shown to demonstrate superior performance of the DMELA constructed image alignment system over other typical learning algorithms and existing alignment systems. Such system is useful to develop accurate and efficient image alignment systems.

Hsu, Chi-Yao; Cheng, Yi-Chang; Lin, Sheng-Fuu

2011-12-01

149

NASA Astrophysics Data System (ADS)

Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.

2011-08-01

150

Nonlinear system identification of smart structures under high impact loads

NASA Astrophysics Data System (ADS)

The main purpose of this paper is to develop numerical models for the prediction and analysis of the highly nonlinear behavior of integrated structure control systems subjected to high impact loading. A time-delayed adaptive neuro-fuzzy inference system (TANFIS) is proposed for modeling of the complex nonlinear behavior of smart structures equipped with magnetorheological (MR) dampers under high impact forces. Experimental studies are performed to generate sets of input and output data for training and validation of the TANFIS models. The high impact load and current signals are used as the input disturbance and control signals while the displacement and acceleration responses from the structure-MR damper system are used as the output signals. The benchmark adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. Comparisons of the trained TANFIS models with experimental results demonstrate that the TANFIS modeling framework is an effective way to capture nonlinear behavior of integrated structure-MR damper systems under high impact loading. In addition, the performance of the TANFIS model is much better than that of ANFIS in both the training and the validation processes.

Sarp Arsava, Kemal; Kim, Yeesock; El-Korchi, Tahar; Park, Hyo Seon

2013-05-01

151

A neuro-fuzzy approach for predicting hemodynamic responses during anesthesia.

The effect of drugs' interaction on the hemo-dynamic variables is of great importance when considering patient's safety and stability. It is also important for control infusion systems during anesthesia. In this article, an adaptive-network fuzzy inference system is used to model the effect of two drugs (propofol and remifentanil) on the mean arterial pressure and heart rate. The clinical data of 45 patients is used to train and test the model. The use of subtractive clustering improved the model performance on the testing data set. The fuzzy model is able to capture the synergistic interaction between the two drugs, but other influences were detected. PMID:19164039

Nunes, Catarina S; Amorim, Pedro

2008-01-01

152

In the present research, two neuro-fuzzy models and a neural network model are presented for predictions of material removal\\u000a rate (MRR), tool wear rate (TWR), and radial overcut (G) in die sinking electrical discharge machining (EDM) process for American Iron and Steel Institute D2 tool steel with copper\\u000a electrode. The discharge current (I\\u000a p), pulse duration (T\\u000a on), duty cycle

Mohan Kumar Pradhan; Chandan Kumar Biswas

2010-01-01

153

Clustering of noisy image data using an adaptive neuro-fuzzy system

NASA Technical Reports Server (NTRS)

Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.

Pemmaraju, Surya; Mitra, Sunanda

1992-01-01

154

Heart rate signal can be used as certain indicator of heart disease. Spectral analysis of heart rate variability (HRV) signal\\u000a makes it possible to partly separate the low-frequency (LF) sympathetic component, from the high-frequency (HF) vagal component\\u000a of autonomic cardiac control. Here, we used two important features to characterize the nonlinear fluctuations in the heart\\u000a variability signal (HRV): cardiac vagal

D. Petkovi?; Ž. ?ojbaši?

155

A neuro-fuzzy system for tool condition monitoring in metal cutting

mechanism are used to provide a linguistic model for the detection of tool wear. However the fuzzy membership functions need to be tuned so that they reflect the true meaning of the process variables. This is done by using an error-based, density...

Mesina, Omez Samoon

2012-06-07

156

Biogeography-Based Optimization of Neuro-Fuzzy System Parameters for Diagnosis of Cardiac Disease

Cleveland, Ohio d.j.simon@csuohio.edu ABSTRACT Cardiomyopathy refers to diseases of the heart muscle, which in turn predisposes the heart to failure or arrhythmias. Cardiomyopathy in its two common forms wave features for the diagnosis of cardiomyopathy. In addition, we incorporate opposition

Simon, Dan

157

In this study, we introduce general frame of MAny Connected Intelligent Particles Systems (MACIPS). Connections and interconnections between particles get a complex behavior of such merely simple system (system in system).Contribution of natural computing, under information granulation theory, are the main topic of this spacious skeleton. Upon this clue, we organize different algorithms involved a few prominent intelligent computing and approximate reasoning methods such as self organizing feature map (SOM)[9], Neuro- Fuzzy Inference System[10], Rough Set Theory (RST)[11], collaborative clustering, Genetic Algorithm and Ant Colony System. Upon this, we have employed our algorithms on the several engineering systems, especially emerged systems in Civil and Mineral processing. In other process, we investigated how our algorithms can be taken as a linkage of government-society interaction, where government catches various fashions of behavior: solid (absolute) or flexible. So, transition of such society, by chan...

Owladeghaffari, Hamed

2008-01-01

158

Flood Forecasting in River System Using ANFIS

NASA Astrophysics Data System (ADS)

The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

Ullah, Nazrin; Choudhury, P.

2010-10-01

159

Neuro-fuzzy based vector field model: an unified representation for mobile robot guiding styles

The development of autonomous robot navigation systems has been a challenge for robot specialists for decades. Different guiding or navigation styles are required for autonomous robots to operate in the intelligent space efficiently and safely under different situations. In this paper an extension of the classical potential based guiding model is presented. The vector field based guiding model is proposed

Istviin Nagy; Wai Keung Fung; Peter Baranyi

2000-01-01

160

During the last decade, intraspinal microstimulation (ISMS) has been proposed as a potential technique for restoring motor function in paralyzed limbs. A major challenge to restoration of a desired functional limb movement through the use of ISMS is the development of a robust control strategy for determining the stimulation patterns. Accurate and stable control of limbs by functional intraspinal microstimulation is a very difficult task because neuromusculoskeletal systems have significant nonlinearity, time variability, large latency and time constant, and muscle fatigue. Furthermore, the controller must be able to compensate the effect of the dynamic interaction between motor neuron pools and electrode sites during ISMS. In this paper, we present a robust strategy for multi-joint control through ISMS in which the system parameters are adapted online and the controller requires no offline training phase. The method is based on the combination of sliding mode control with fuzzy logic and neural control. Extensive experiments on six rats are provided to demonstrate the robustness, stability, and tracking accuracy of the proposed method. Despite the complexity of the spinal neuronal networks, our results show that the proposed strategy could provide accurate tracking control with fast convergence and could generate control signals to compensate for the effects of muscle fatigue. PMID:22711783

Asadi, Ali-Reza; Erfanian, Abbas

2012-07-01

161

Stability evaluation of inference methods for optoelectronic fuzzy inference system

NASA Astrophysics Data System (ADS)

System stability of various membership functions and fuzzy control methods are compared by numerical simulations to determine the feasibility of optoelectronic fuzzy inference method. An inverted pendulum is used for the destination system. A Gaussian membership function is better than a triangular one. MIN operations of grade evaluation and modification of consequent membership functions are better than other operations. SUM operation of consequent operation is better than MAX operation.

Itoh, Hideo; Yamada, Tatsuya; Houssay, Bruno; Mukal, Seiji; Uekusa, Shin-ichiro

1994-01-01

162

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

NASA Astrophysics Data System (ADS)

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.

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

2010-09-01

163

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

NASA Astrophysics Data System (ADS)

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.

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

2011-01-01

164

Expert System Shell for Inferring Vegetation Characteristics.

National Technical Information Service (NTIS)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The report describes the extensions that have been made to the first generation version of VEG. An interface to a file of unkown ...

P. A. Harrison, P. R. Harrison

1992-01-01

165

Expert System Shell for Inferring Vegetation Characteristics.

National Technical Information Service (NTIS)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. VEG is described in detail in several references. The first generation version of VEG was extended. In the first year of this con...

P. A. Harrison, P. R. Harrison

1993-01-01

166

In this paper we describe a neuro-fuzzy system with adaptive capability to extract fuzzy If Then rules from input and output sample data through learning. The proposed system, called radial basis function (RBF) based adaptive fuzzy system (AFS), employs the Gaussian functions to represent the membership functions of the premise part of fuzzy rules. Three architectural deviations of the RBF

Kwang Bo Cho; Bo Hyeun Wang

1996-01-01

167

An inference engine for embedded diagnostic systems

NASA Technical Reports Server (NTRS)

The implementation of an inference engine for embedded diagnostic systems is described. The system consists of two distinct parts. The first is an off-line compiler which accepts a propositional logical statement of the relationship between facts and conclusions and produces data structures required by the on-line inference engine. The second part consists of the inference engine and interface routines which accept assertions of fact and return the conclusions which necessarily follow. Given a set of assertions, it will generate exactly the conclusions which logically follow. At the same time, it will detect any inconsistencies which may propagate from an inconsistent set of assertions or a poorly formulated set of rules. The memory requirements are fixed and the worst case execution times are bounded at compile time. The data structures and inference algorithms are very simple and well understood. The data structures and algorithms are described in detail. The system has been implemented on Lisp, Pascal, and Modula-2.

Fox, Barry R.; Brewster, Larry T.

1987-01-01

168

Intelligent Control Systems Using Computational Intelligence [book review

This book consists of 13 chapters contributed mainly by European academic authors. The book opens with three overview chapters on fuzzy, neural, and evolutionary systems for system identification and control. Later chapters cover such topics as adaptive local linear modeling and control of nonlinear dynamical systems; Gaussian process approaches to nonlinear modeling and control; neuro-fuzzy model construction, design and estimation;

Danil V. Prokhorov

2007-01-01

169

The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on- line learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. New connections and new neurons are created during the operation of the

Nikola Kasabov

170

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, esti- mating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level supply chain (SC)

Tugba Efendigil; Semih Önüt; Cengiz Kahraman

2009-01-01

171

A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control

This study presents a functional-link-based neuro- fuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a

Cheng-hung Chen; Cheng-jian Lin; Chin-teng Lin

2008-01-01

172

Multisensor System for Safer Human-Robot Interaction Yucong Lu, Lingqi Zeng and Gary M. Bone*

to perform human tracking more quickly and reliably. Due to the capability of passive infrared (PIR) sensors. A higher reliability is required when human safety is at risk. In this paper, a human locating and tracking and was used with the neuro-fuzzy robot safety system simulated in [7]. The reported location is quantized

Bone, Gary

173

Lock inference for systems software John Regehr Alastair Reid

Lock inference for systems software John Regehr Alastair Reid School of Computing, University and other errors as well as supporting lock inference: the derivation of an appropriate lock implementa- tion for each critical section in a system. Lock inference solves a number of problems in creating

Utah, University of

174

Resistance spot welding (RSW) is still the most successful sheet metal joining method in the automobile industry. However, an effective quality evaluation method has not yet been developed. Real-time quality inspection of RSW is necessary in order to deal with all kinds of problems during welding. This paper developed an experimental system using for measuring electrode displacement. Accordingly based on

Zhongqin Lin; Yansong Zhang; Guanlong Chen; Yongbing Li

2004-01-01

175

The paper introduces a way of using chaos theory and a particular fuzzy neural network, called FuNN, for building adaptive, intelligent information systems. The use of the proposed connectionist-based methodology is illustrated through a biomedical application of heart rate variability (HRV) analysis. It is demonstrated that a multi-scale fractal analysis of HRV data can be used for characterisation and prediction

R. Kozma; N. K. Kasabov; J. A. Swope; M. J. A. Williams

1997-01-01

176

NASA Astrophysics Data System (ADS)

The terrorist attack of 9/11 has revealed how vulnerable the civil aviation industry is from both security and safety points of view. Dealing with several aircrafts cruising in the sky of a specific region requires decision makers to have an automated system that can raise their situational awareness of how much a threat an aircraft presents. In this research, an in-flight array of sensors has been deployed in a simulated aircraft to extract knowledge-base information of how passengers and equipment behave in normal flighttime which has been used to train artificial neural networks to provide real-time streams of normal behaviours. Finally, a cascading of fuzzy logic networks is designed to measure the deviation of real-time data from the predicted ones. The results suggest that Neural-Fuzzy networks have a promising future to raise the awareness of decision makers about certain aviation situations.

Ali, Ali H.; Tarter, Alex

2009-05-01

177

FPGA Implementation of Fuzzy Inference System for Embedded Applications

FPGA Implementation of Fuzzy Inference System for Embedded Applications Dr. Kasim M. Al of the whole system. A fuzzy inference system has been implemented on an FPGA, and used to control a PM motor machine applications where simplicity, reliability and stability are more important issues. Keywords

178

Robotic Hand-Eye Coordination: From Observation to Manipulation

In this paper, we present a new hybrid method of performing eye-to-hand coordination and manipulation to produce a working robot named COERSU. The method is an optimized combination of two neuro-fuzzy approaches developed by the authors: direct fuzzy servoing and fuzzy correction. The fuzzy methods are tuned by an adaptive neuro-fuzzy inference system (ANFIS). On the whole, a genetic tuner

Shahram Jafari; Ray Jarvis

2004-01-01

179

Grey box modelling and advanced control scheme for building heating systems

NASA Astrophysics Data System (ADS)

This dissertation is aimed at generating new knowledge on Recurrent Neuro-Fuzzy Inference Systems (RenFIS) and to explore its application in building automation. Inferential sensing is an attractive approach for modeling the behavior of dynamic processes. Inferential sensor based control strategies are applied to optimize the control of residential heating systems and demonstrate significant energy saving and comfort improvement. Despite the rapidly decreasing cost and improving accuracy of most temperature sensors, it is normally impractical to use a lot of sensors to measure the average air temperature because the wiring and instrumentation can be very expensive to install and maintain. To design a reliable inferential sensor, of fundamental importance is to build a simple and robust dynamic model of the system to be controlled. This dissertation presents the development of an innovative algorithm that is suitable for the robust black-box model. The algorithm is derived from ANFIS (Adaptive Neuro-Fuzzy Inference System) and is referred to as RenFIS. Like all other modeling techniques, RenFIS performance is sensitive to the training data. In this study, RenFIS is used to model two different heating systems, hot water heating system and forced warm-air heating system. The training data is collected under different operational conditions. RenFIS gives better performance if trained with the data set representing overall qualities of the whole universe of the experimental data. The robustness analysis is conducted by introducing simulated noise to the training data. Results show that RenFIS is less sensitive than ANFIS to the quality of training data. The RenFIS based inferential sensor is then applied to design an inferential control algorithm that can improve the operation of residential heating systems. In current practice, the control of heating systems is based on the measurement of air temperature at one point within the building. The inferential control strategy developed through this study allows the control to be based on an estimate of the overall thermal performance. This is achieved through estimating the average room temperature using a RenFIS based inferential sensor and incorporating the estimate with conventional control technology. The performance of this control technology has been investigated through simulation study.

Jassar, Surinder

180

Evaluating functional network inference using simulations of complex biological systems

Motivation: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated

V. Anne Smith; Erich D. Jarvis; Alexander J. Hartemink

2002-01-01

181

NASA Astrophysics Data System (ADS)

An optimal control for maximizing extraction of power in variable-speed wind energy conversion system is presented. Intelligent gradient detection by fuzzy inference system (FIS) in maximum power point tracking control is proposed to achieve power curve operating near optimal point. Speed rotor reference can be adjusted by maximum power point tracking fuzzy controller (MPPTFC) such that the turbine operates around maximum power. Power curve model can be modelled by using adaptive neuro fuzzy inference system (ANFIS). It is required to simply well estimate just a few number of maximum power points corresponding to optimum generator rotor speed under varying wind speed, implying its training can be done with less effort. Using the trained fuzzy model, some estimated maximum power points as well as their corresponding generator rotor speed and wind speed are determined, from which a linear wind speed feedback controller (LWSFC) capable of producing optimum generator speed can be obtained. Applied to a squirrel-cage induction generator based wind energy conversion system, MPPTFC and LWSFC could maximize extraction of the wind energy, verified by a power coefficient stay at its maximum almost all the time and an actual power line close to a maximum power efficiency line reference.

Nadhir, Ahmad; Naba, Agus; Hiyama, Takashi

182

Causal Inferences in the Campbellian Validity System

ERIC Educational Resources Information Center

The purpose of the present paper is to critically examine causal inferences and internal validity as defined by Campbell and co-workers. Several arguments are given against their counterfactual effect definition, and this effect definition should be considered inadequate for causal research in general. Moreover, their defined independence between…

Lund, Thorleif

2010-01-01

183

ANFIS: adaptive-network-based fuzzy inference system

The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation,

Jyh-Shing Roger Jang

1993-01-01

184

A combined differential evolution and neural network approach to nonlinear system identification

This paper addresses the effectiveness of soft computing approaches such as Evolutionary Computation (EC) and Artificial Neural Network (ANN) to system identification of nonlinear systems. In this work, three approaches namely a neuro-fuzzy, differential evolution (DE) and a combined DE-ANN have been applied for nonlinear system identification problem. Results obtained envisage that the proposed combined differential evolution-ANN approach to identification

Bidyadhar Subudhi; Debashisha Jena

2008-01-01

185

Neural networks, which make no assumption about data distribution, have been adopted to classify complex remote sensing data, and achieved improved results compared to traditional statistical methods. The attractions of neural networks also include their ability to learn from empirical examples and simulate any nonlinear decision function. However, a neural network is a black box and it is difficult to

Fang Qiu

2000-01-01

186

Dynamic contingency screening with simplified fuzzy inference in power systems

This paper proposes a fuzzy inference based method for dynamic security assessment in power systems. In recent years, the deregulated power market encourages IPP to serve power utilities so that less expensive generation cost is obtained in power system operation and planning. As a result, the generation scheduling becomes more complicated while the degree of uncertainty increases due to occurrence

Hiroyuki Mori; Eisho Ando

1999-01-01

187

Connectionist Inference Systems Hans Werner Gusgen

this observation is surprising as the main building block of the human nervous system, the neuron, is quite slow conclusion. Massive parallelism must take place in the human nervous system. Though the human nervous system with which neurons excite or inhibit each other. But our nervous system has more remarkable features

Hoelldobler, Steffen

188

NASA Astrophysics Data System (ADS)

This paper gives a technical solution to improve the efficiency in multi-sensor wireless network based estimation for distributed parameter systems. A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose. The three kinds of estimators are working with precision on different parts of the phenomenon characteristic. A comparative study of three methods - linear and nonlinear based on neural networks and adaptive neuro-fuzzy inference system - to implement these algorithms is made. The intelligent wireless sensor networks are taken in consideration as an efficient tool for measurement, data acquisition and communication. They are seen as a "distributed sensor", placed in the desired positions in the measuring field. The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor. A modelling and simulation for a case study is presented. The quality of estimation is validated using a quadratic criterion. A practical implementation is made using virtual instrumentation. Applications of this complex estimation system are in fault detection and diagnosis of distributed parameter systems and discovery of malicious nodes in wireless sensor networks.

Volosencu, Constantin; Curiac, Daniel-Ioan

2013-12-01

189

Minerva: A Scalable OWL Ontology Storage and Inference System

\\u000a With the increasing use of ontologies in Semantic Web and enterprise knowledge management, it is critical to develop scalable\\u000a and efficient ontology management systems. In this paper, we present Minerva, a storage and inference system for large-scale\\u000a OWL ontologies on top of relational databases. It aims to meet scalability requirements of real applications and provide practical\\u000a reasoning capability as well

Jian Zhou; Li Ma; Qiaoling Liu; Lei Zhang; Yong Yu; Yue Pan

2006-01-01

190

A knowledge-based expert system for inferring vegetation characteristics

NASA Technical Reports Server (NTRS)

A prototype knowledge-based expert system VEG is presented that focuses on extracting spectral hemispherical reflectance using any combination of nadir and/or directional reflectance data as input. The system is designed to facilitate expansion to handle other inferences regarding vegetation properties such as total hemispherical reflectance, leaf area index, percent ground cover, phosynthetic capacity, and biomass. This approach is more robust and accurate than conventional extraction techniques previously developed.

Kimes, Daniel S.; Harrison, Patrick R.; Ratcliffe, P. A.

1991-01-01

191

An expert system shell for inferring vegetation characteristics

NASA Technical Reports Server (NTRS)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The report describes the extensions that have been made to the first generation version of VEG. An interface to a file of unkown cover type data has been constructed. An interface that allows the results of VEG to be written to a file has been implemented. A learning system that learns class descriptions from a data base of historical cover type data and then uses the learned class descriptions to classify an unknown sample has been built. This system has an interface that integrates it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented.

Harrison, P. Ann; Harrison, Patrick R.

1992-01-01

192

Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m?2 s?1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. PMID:24465829

Gago, Jorge; Martinez-Nunez, Lourdes; Landin, Mariana; Flexas, Jaume; Gallego, Pedro P.

2014-01-01

193

Inference and learning in sparse systems with multiple states

We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.

Braunstein, A. [Human Genetics Foundation, Via Nizza 52, I-10126 Torino (Italy); Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy); Ramezanpour, A.; Zhang, P. [Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy); Zecchina, R. [Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy); Human Genetics Foundation, Via Nizza 52, I-10126 Torino (Italy); Collegio Carlo Alberto, Via Real Collegio 30, I-10024 Moncalieri (Italy)

2011-05-15

194

Topological augmentation to infer hidden processes in biological systems

Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index. Contact: mikael.sunnaker@bsse.ethz.ch; andreas.wagner@ieu.uzh.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24297519

Sunnaker, Mikael; Zamora-Sillero, Elias; Lopez Garcia de Lomana, Adrian; Rudroff, Florian; Sauer, Uwe; Stelling, Joerg; Wagner, Andreas

2014-01-01

195

This paper presents a novel training algorithm for fuzzy inference systems. The algorithm combines the Levenberg-Marquardt algorithm with variable structure systems approach. The combination is performed by expressing the parameter update rule in continuous time and application of sliding control method to the gradient based training procedure. In this paper, a fuzzy inference mechanism that can be trained such that

M. Onder Efe; Okyay Kaynak; Bogdan M. Wilamowski

1999-01-01

196

Platform-Based Inference System Design Using FML and Fuzzy Technology for Healthcare

This study presents a framework of platform-based inference system design to provide specific field experts\\/users developing customized fuzzy inference system efficiently and effectively. The framework is composed of three major components: FML editor, FML parser and FML inference engine. First, the FML editor can let field experts compile knowledge base and rule base with friendly user interface. The composed FML

Chen-Yu Chen; Mingzoo Wu; Chi-Lu Yang; Shing-Hua Ho; Chin-Yuan Hsu

2009-01-01

197

Available transfer capability (ATC) determination using intelligent techniques

In this paper ATC has been computed for real time applications using three different intelligent techniques viz., i) back propagation algorithm (BPA) ii) radial basis function (RBF) neural network and iii) adaptive neuro fuzzy inference system (ANFIS). The ATC is to be made available on open access same time information system (OASIS), which is accessible to seller and buyer. The

D. M. Vinod Kumar; G. Narayan Reddy; Ch. Venkaiah

2006-01-01

198

Erratum: Erratum to Central European Journal of Engineering, Volume 4, Issue 1

NASA Astrophysics Data System (ADS)

Paper by M. Ajay Kumar, N. V. Srikanth, et al. "An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions" in Volume 4, Issue 1, 27-38/March 2014 doi: 10.2478/s13531-013-0143-4 contains an error in the title. The correct title is presented below

Kumar, M. Ajay; Srikanth, N. V.

2014-06-01

199

FPGA-Based Fuzzy Inference System for Real-time Embedded Applications

FPGA-Based Fuzzy Inference System for Real-time Embedded Applications Dr. Kasim M. Al and reduce the cost of the whole system. A fuzzy inference system has been implemented on an FPGA, and used controller in washing machine applications where simplicity, reliability and stability are more important

200

A Self-Tuning Kalman Filter for Autonomous Navigation Using the Global Positioning System (GPS)

NASA Technical Reports Server (NTRS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS (Global Positioning Systems) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, Son H.

1999-01-01

201

Artificial Intelligence Techniques for Steam Generator Modelling

This paper investigates the use of different Artificial Intelligence methods to predict the values of several continuous variables from a Steam Generator. The objective was to determine how the different artificial intelligence methods performed in making predictions on the given dataset. The artificial intelligence methods evaluated were Neural Networks, Support Vector Machines, and Adaptive Neuro-Fuzzy Inference Systems. The types of

Sarah Wright; Tshilidzi Marwala

2008-01-01

202

Optimization of ANFIS with Applications in Machine Defect Severity Classification

High accuracy and high generalization capability are two conflicting objectives in the design of adaptive neuro-fuzzy inference system (ANFIS). Motivated by previous studies on handling similar conflicting situations in model selection and autoregressive order estimation, this paper investigates information criteria for the optimization of ANFIS model with applications in machine defect severity classification. The studied criteria include the Akaike Information

Shuangwen Sheng; Robert X. Gao

2006-01-01

203

This paper proposes a new metamodeling framework that reduces the computational burden of the structural optimization against the time history loading. In order to achieve this, two strategies are adopted. In the first strategy, a novel metamodel consisting of adaptive neuro-fuzzy inference system (ANFIS), subtractive algorithm (SA), self organizing map (SOM) and a set of radial basis function (RBF) networks

Saeed Gholizadeh; Eysa Salajegheh

2009-01-01

204

Inferring Meta-Models for Runtime System Data from the Clients of Management APIs

Inferring Meta-Models for Runtime System Data from the Clients of Management APIs Hui Song1 , Gang. To do this, users have to understand the different management APIs provided by different systems to inferring such meta-models by analyzing client code that accesses man- agement APIs. A set of experiments

Paris-Sud XI, UniversitÃ© de

205

A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)

NASA Technical Reports Server (NTRS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, S. H.

1999-01-01

206

Automatic Road Gap Detection Using Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.

2011-09-01

207

Expert System Shell for Inferring Vegetation Characteristics: Atmospheric Techniques (Task G).

National Technical Information Service (NTIS)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG Subgoals have been reorganized into categories. A new subgoal category 'Atmospheric Techniques' containing two new subgoa...

P. A. Harrison, P. R. Harrison

1993-01-01

208

Inferring the Gibbs state of a small quantum system

Gibbs states are familiar from statistical mechanics, yet their use is not limited to that domain. For instance, they also feature in the maximum entropy reconstruction of quantum states from incomplete measurement data. Outside the macroscopic realm, however, estimating a Gibbs state is a nontrivial inference task, due to two complicating factors: the proper set of relevant observables might not be evident a priori; and whenever data are gathered from a small sample only, the best estimate for the Lagrange parameters is invariably affected by the experimenter's prior bias. I show how the two issues can be tackled with the help of Bayesian model selection and Bayesian interpolation, respectively, and illustrate the use of these Bayesian techniques with a number of simple examples.

Rau, Jochen [Institut fuer Theoretische Physik, Johann Wolfgang Goethe-Universitaet, Max-von-Laue-Strasse 1, D-60438 Frankfurt am Main (Germany)

2011-07-15

209

Object Metrics for Aspect Systems: Limiting Empirical Inference Based on Modularity

In empirical comparisons of Aspect-Oriented (AO) to Object- Oriented (OO) systems, system properties such as understandability, maintainability, reusability, and testability have often been inferred from other metrics, such as lines of code, sites of change, and modularity. However, in traditional OO metrics suites such system properties are assessed separately from these measures. We applied OO metrics for comparing an AO

Shiu Lun Tsang; Siobhán Clarke; Elisa Baniassad

210

An Agent-Based Approach to Inference Prevention in Distributed Database Systems

We propose an inference prevention agent as a tool that enables each of the databases in a distributed system to keep track of probabilistic dependencies with other databases and then use that information to help preserve the confidentiality of sensitive data. This is accomplished with minimal sacrifice of the performance and survivability gains that are associated with distributed database systems.

James Tracy; Liwu Chang; Ira S. Moskowitz

2003-01-01

211

Adaptive network fuzzy inference system used in interference cancellation of radar seeker

The method of adaptive network fuzzy inference system (ANFIS) applied to the interference cancellation system of radar seeker was described in this paper. When the antiaircraft missile, which adopts the pulse Doppler radar seeker, attacks the low altitude target in the down-looking mode, the seeker of missile will receive strong ground clutter. As we all know the ground clutter will

Xiang Li

2010-01-01

212

An expert system shell for inferring vegetation characteristics: The learning system (tasks C and D)

NASA Technical Reports Server (NTRS)

This report describes the implementation of a learning system that uses a data base of historical cover type reflectance data taken at different solar zenith angles and wavelengths to learn class descriptions of classes of cover types. It has been integrated with the VEG system and requires that the VEG system be loaded to operate. VEG is the NASA VEGetation workbench - an expert system for inferring vegetation characteristics from reflectance data. The learning system provides three basic options. Using option one, the system learns class descriptions of one or more classes. Using option two, the system learns class descriptions of one or more classes and then uses the learned classes to classify an unknown sample. Using option three, the user can test the system's classification performance. The learning system can also be run in an automatic mode. In this mode, options two and three are executed on each sample from an input file. The system was developed using KEE. It is menu driven and contains a sophisticated window and mouse driven interface which guides the user through various computations. Input and output file management and data formatting facilities are also provided.

Harrison, P. Ann; Harrison, Patrick R.

1992-01-01

213

Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory

NASA Astrophysics Data System (ADS)

An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H? tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H? tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.

Rigatos, Gerasimos G.

2013-10-01

214

Adaptive fuzzy control with output feedback for H infinity tracking of SISO nonlinear systems.

Observer-based adaptive fuzzy H(infinity) control is proposed to achieve H(infinity) tracking performance for a class of nonlinear systems, which are subject to model uncertainty and external disturbances and in which only a measurement of the output is available. The key ideas in the design of the proposed controller are (i) to transform the nonlinear control problem into a regulation problem through suitable output feedback, (ii) to design a state observer for the estimation of the non-measurable elements of the system's state vector, (iii) to design neuro-fuzzy approximators that receive as inputs the parameters of the reconstructed state vector and give as output an estimation of the system's unknown dynamics, (iv) to use an H(infinity) control term for the compensation of external disturbances and modelling errors, (v) to use Lyapunov stability analysis in order to find the learning law for the neuro-fuzzy approximators, and a supervisory control term for disturbance and modelling error rejection. The control scheme is tested in the cart-pole balancing problem and in a DC-motor model. PMID:18763730

Rigatos, Gerasimos G

2008-08-01

215

NASA Astrophysics Data System (ADS)

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

Asoodeh, Mojtaba; Bagheripour, Parisa

2012-01-01

216

Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System

With the rapid growth of the amount of data available in electronic libraries, through Internet and enterprise network mediums,\\u000a advanced methods of search and information retrieval are in demand. Information retrieval systems, designed for storing, maintaining\\u000a and searching large-scale sets of unstructured documents, are the subject of intensive investigation. An information retrieval\\u000a system, a sophisticated application managing underlying documentary databases,

Václav Snásel; Ajith Abraham; Suhail S. J. Owais; Jan Platos; Pavel Krömer

2009-01-01

217

Non-linear system identification using Bayesian inference

Many real world systems can only be described well by non-linear models. The analysis and use of non-linear models can be very difficult and time consuming. An attractive class of models is one whose analysis can be based directly on linear systems analysis. One such class comprises models that are linear-in-the-parameters. Such models tend to have extremely large numbers of

K. J. Pope; P. J. W. Rayner

1994-01-01

218

An Improved Storage and Inference Method for Ontology Based Remote Sensing Interpretation System

NASA Astrophysics Data System (ADS)

As the incredibly expanding volumes of remote sensing archives, and the number of objects necessary to identify in remote sensing pictures increasing, the conventional process of remote sensing interpretation is becoming more and more inefficient, and it seems impossible to finish all interpretation tasks in time. This paper applies ontology techniques to this process. It uses ontology to describe the domain knowledge, and brings forward a new hybrid ontology mapping model and an effective inference method without using any ontology inference engine. Finally it constructs an interpretation system, using which the work efficiency can be improved greatly.

Jia, Xiaoguang; Lin, Zhengwei; Huang, Ning

219

Inferring prey perception of relative danger in large-scale marine systems

Problem: Infer relative danger from spatially segregated predators in large-scale marine systems, using individual differences in prey foraging behaviour. Mathematical models: Optimization of trade-offs between predation risk and energy gain. Key assumption: Foraging individuals choosing to incur higher risk of predation should experience higher energetic gain than individuals choosing safer foraging options. Organisms: Alaskan harbour seals foraging under predation risk

Alejandro Frid; Lawrence M. Dill; Richard E. Thorne; Gail M. Blundell

2007-01-01

220

This work substantiates novel perspectives and tools for analysis and design of fuzzy inference systems (FIS). It is shown rigorously that the cardinality of the set F of fuzzy numbers equals ?1, hence a FIS can implement “in principle” ?2 functions, where and ?1 is the cardinality of the set R of real numbers; furthermore, a FIS is endowed with

Vassilis G. Kaburlasos; Athanasios Kehagias

2006-01-01

221

An attempt has been made to investigate the possibility of using adaptive network-based fuzzy inference systems to predict the post-construction settlement of rockfill dams. Four types of dams, namely, central core, sloping core, compacted membrane faced, and dumped membrane faced rockfill dams are considered in this study. An index is defined to indicate the combined compressibility of the dam embankment

Ghassem Habibagahi

2002-01-01

222

Functional equivalence between radial basis function networks and fuzzy inference systems

It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe

J.-S. R. Jang; C.-T. Sun

1993-01-01

223

NASA Technical Reports Server (NTRS)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'

Harrison, P. Ann

1992-01-01

224

Earth system sensitivity inferred from Pliocene modelling and data

Quantifying the equilibrium response of global temperatures to an increase in atmospheric carbon dioxide concentrations is one of the cornerstones of climate research. Components of the Earths climate system that vary over long timescales, such as ice sheets and vegetation, could have an important effect on this temperature sensitivity, but have often been neglected. Here we use a coupled atmosphere-ocean general circulation model to simulate the climate of the mid-Pliocene warm period (about three million years ago), and analyse the forcings and feedbacks that contributed to the relatively warm temperatures. Furthermore, we compare our simulation with proxy records of mid-Pliocene sea surface temperature. Taking these lines of evidence together, we estimate that the response of the Earth system to elevated atmospheric carbon dioxide concentrations is 30-50% greater than the response based on those fast-adjusting components of the climate system that are used traditionally to estimate climate sensitivity. We conclude that targets for the long-term stabilization of atmospheric greenhouse-gas concentrations aimed at preventing a dangerous human interference with the climate system should take into account this higher sensitivity of the Earth system. ?? 2010 Macmillan Publishers Limited. All rights reserved.

Lunt, D. J.; Haywood, A. M.; Schmidt, G. A.; Salzmann, U.; Valdes, P. J.; Dowsett, H. J.

2010-01-01

225

Fuzzy systems in high-energy physics

NASA Astrophysics Data System (ADS)

Decision making is one of the major subjects of interest in physics. This is due to the intrinsic finite accuracy of measurement that leads to the possible results to span a region for each quantity. In this way, to recognize a particle type among the others by a measure of a feature vector, a decision must be made. The decision making process becomes a crucial point whenever a low statistical significance occurs as in space cosmic ray experiments where searching in rare events requires us to reject as many background events as possible (high purity), keeping as many signal events as possible (high efficiency). In the last few years, interesting theoretical results on some feedforward connectionist systems (FFCSs) have been obtained. In particular, it has been shown that multilayer perceptrons (MLPs), radial basis function networks (RBFs), and some fuzzy logic systems (FLSs) are nonlinear universal function approximators. This property permits us to build a system showing intelligent behavior , such as function estimation, time series forecasting, and pattern classification, and able to learn their skill from a set of numerical data. From the classification point of view, it has been demonstrated that non-parametric classifiers based FFCSs holding the universal function approximation property, can approximate the Bayes optimal discriminant function and then minimize the classification error. In this paper has been studied the FBF when applied to a high energy physics problem. The FBF is a powerful neuro-fuzzy system (or adaptive fuzzy logic system) holding the universal function approximation property and the capability of learning from examples. The FBF is based on product-inference rule (P), the Gaussian membership function (G), a singleton fuzzifier (S), and a center average defuzzifier (CA). The FBF can be regarded as a feedforward connectionist system with just one hidden layer whose units correspond to the fuzzy MIMO rules. The FBF can be identified both by exploiting the linguistic knowledge available (structure identification problem) and by using the information contained in a data set (parameter estimation problem). The fuzzy system has been found to be effective for the classification tasks of about 2 by 10-3 hadron contamination at 90% of electron acceptance. A comparison between the adaptive system results and the others previous ones obtained by using both statistical and neural network based methodologies also is presented.

Castellano, Marcello; Masulli, Francesco; Penna, Massimo

1996-06-01

226

Asymptotic inference in system identification for the atom maser

System identification is an integrant part of control theory and plays an increasing role in quantum engineering. In the quantum set-up, system identification is usually equated to process tomography, i.e. estimating a channel by probing it repeatedly with different input states. However for quantum dynamical systems like quantum Markov processes, it is more natural to consider the estimation based on continuous measurements of the output, with a given input which may be stationary. We address this problem using asymptotic statistics tools, for the specific example of estimating the Rabi frequency of an atom maser. We compute the Fisher information of different measurement processes as well as the quantum Fisher information of the atom maser, and establish the local asymptotic normality of these statistical models. The statistical notions can be expressed in terms of spectral properties of certain deformed Markov generators and the connection to large deviations is briefly discussed.

Catalin Catana; Merlijn van Horssen; Madalin Guta

2011-12-09

227

On inferring autonomous system relationships in the internet

The Internet consists of rapidly increasing number of hosts interconnected by constantly evolving networks of links and routers. Interdomain routing in the Internet is coordinated by the Border Gateway Protocol (BGP). BGP allows each autonomous system (AS) to choose its own administrative policy in selecting routes and propagating reachability information to others. These routing policies are constrained by the contractual

Lixin Gao

2001-01-01

228

Reducing Failure Rates of Robotic Systems though Inferred Invariants Monitoring

are tailored to match the spatial, temporal, and operational at- tributes of robotic systems. Further and not rotating when landing, the UAV's angles are not greater than a All authors are with the NIMBUS Lab potential invariants from a set of template invariants utilizing the trace values, and dropping or refining

Farritor, Shane

229

Intermediate articleProduction Systems and Rule-based Inference

to simulate human behavior are Soar (Laird et al., 1987), ACT-R (Anderson and Lebiere, 1998), and OPS5 (Forgy systems as cognitive architectures Summary x 5 6 7 84321 7 6 5 4 3 2 1 Exit y Figure 1. Problem scenario.), Encyclopedia of cognitive science. vol. 3, 741-747. London: Nature Publishing Group. [A031.pdf] #12;move

Ritter, Frank

230

Inferring the mass of spherical stellar systems from velocity moments

The usefulness of line-of-sight velocity distributions for constraining the potential and kinematics of a nonrotating spherical system when nothing is known a priori about its radial mass distribution is discussed. A formalism, based on velocity moments, is developed in order to make use of the additional information contained within the distribution of line-of-sight velocities at every projected radius. It is

Herwig Dejonghe; David Merritt

1992-01-01

231

Parametric inference from system lifetime data under a proportional hazard rate model

In this paper, we discuss the statistical inference of the lifetime distribution of components based on observing the system\\u000a lifetimes when the system structure is known. A general proportional hazard rate model for the lifetime of the components\\u000a is considered, which includes some commonly used lifetime distributions. Different estimation methods—method of moments, maximum\\u000a likelihood method and least squares method—for the

Hon Keung Tony Ng; Jorge Navarro; Narayanaswamy Balakrishnan

2012-01-01

232

Large-Scale Optimization for Bayesian Inference in Complex Systems

The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT--Sandia component of the SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas--Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as ``reduce then sample'' and ``sample then reduce.'' In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.

Willcox, Karen [MIT] [MIT; Marzouk, Youssef [MIT] [MIT

2013-11-12

233

1 Simulation of Tactical Communications Systems by Inferring Detailed Data from the Joint Theater by using the data inferred from the joint theater level battle simulations. In this technique, the mobility Theater Level Simulation, Highly Aggregated Combat Modeling, Computer Aided Exercise, Mobile

234

FINDS: A fault inferring nonlinear detection system programmers manual, version 3.0

NASA Technical Reports Server (NTRS)

Detailed software documentation of the digital computer program FINDS (Fault Inferring Nonlinear Detection System) Version 3.0 is provided. FINDS is a highly modular and extensible computer program designed to monitor and detect sensor failures, while at the same time providing reliable state estimates. In this version of the program the FINDS methodology is used to detect, isolate, and compensate for failures in simulated avionics sensors used by the Advanced Transport Operating Systems (ATOPS) Transport System Research Vehicle (TSRV) in a Microwave Landing System (MLS) environment. It is intended that this report serve as a programmers guide to aid in the maintenance, modification, and revision of the FINDS software.

Lancraft, R. E.

1985-01-01

235

Inference of biological S-system using the separable estimation method and the genetic algorithm.

Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations (ODEs) is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due to its nonlinearity and complexity. In this paper, a pruning separable parameter estimation algorithm (PSPEA) is proposed for inferring S-systems. This novel algorithm combines the separable parameter estimation method (SPEM) and a pruning strategy, which includes adding an l? regularization term to the objective function and pruning the solution with a threshold value. Then, this algorithm is combined with the continuous genetic algorithm (CGA) to form a hybrid algorithm that owns the properties of these two combined algorithms. The performance of the pruning strategy in the proposed algorithm is evaluated from two aspects: the parameter estimation error and structure identification accuracy. The results show that the proposed algorithm with the pruning strategy has much lower estimation error and much higher identification accuracy than the existing method. PMID:21968962

Liu, Li-Zhi; Wu, Fang-Xiang; Zhang, W J

2012-01-01

236

Network inference of AP pattern formation system in D.melanogaster by structural equation modeling

NASA Astrophysics Data System (ADS)

Within the field of systems biology, revealing the control systems functioning during embryogenesis is an important task. To clarify the mechanisms controlling sequential events, the relationships between various factors and the expression of specific genes should be determined. In this study, we applied a method based on Structural Equation Modeling (SEM), combined with factor analysis. SEM can include the latent variables within the constructed model and infer the relationships among the latent and observed variables, as a network model. We improved a method for the construction of initial models for the SEM calculation, and applied our approach to estimate the regulatory network for Antero-Posterior (AP) pattern formation in D. melanogaster embryogenesis. In this new approach, we combined cross-correlation and partial correlation to summarize the temporal information and to extract the direct interactions from the gene expression profiles. In the inferred model, 18 transcription factor genes were regulated by not only the expression of other genes, but also the estimated factors. Since each factor regulated the same type of genes, these factors were considered to be involved in maternal effects or spatial morphogen distributions. The interpretation of the inferred network model allowed us to reveal the regulatory mechanism for the patterning along the head to tail axis in D. melanogaster.

Aburatani, S.; Toh, H.

2014-03-01

237

Bayesian Inference Bayesian Inference

) p(A) p(A|B) = p(A, B) p(B) Eliminating p(A, B) gives Bayes rule p(B|A) = p(A|B)p(B) p(A) #12 Subtraction Angiography (DSA) (bottom) is the gold standard method for detecting IA but is an invasive a negative MRA test result ? #12;Bayesian Inference Will Penny Bayesian Inference Bayes rule Medical Decision

Penny, Will

238

The nonlinear effect of green innovation on the corporate competitive advantage

This study uses Adaptive Neuro-Fuzzy Inference System (ANFIS) to explore the nonlinear relationships between green innovation\\u000a performance and corporate competitive advantage. The result indicates that green innovation performance has the nonlinear\\u000a effect on the corporate competitive advantage. If companies hope to enhance their competitive advantages through green innovation,\\u000a they must check their green innovation performance in advance. If their green

Yu-Shan Chen; Ke-Chiun Chang

239

Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid

NASA Astrophysics Data System (ADS)

In this study, experiments were performed by six different volume fractions of Al2O3 nanoparticles in distilled water. Then, actual nanofluid Nusslet number compared by Adaptive neuro fuzzy inference system (ANFIS) predicted number in square cross-section duct in laminar flow under uniform heat flux condition. Statistical values, which quantify the degree of agreement between experimental observations and numerically calculated values, were found greater than 0.99 for all cases.

Salehi, H.; Zeinali-Heris, S.; Esfandyari, M.; Koolivand, M.

2013-04-01

240

Soft computing modeling for indirect determination of the weathering degrees of a granitic rock

NASA Astrophysics Data System (ADS)

Determination of weathering degrees of intact rock has been one of the difficult problems in engineering geology. Additionally, granitic rocks are commonly used as building and ornamental stones and pavement material in various civil engineering structures. For this reason, correct determination of weathering degree of the granitic rocks has a crucial importance in engineering geology. Up to now, some approaches for the determination of weathering degree of granitic rocks have been proposed. Some soft computing methods have been used for the determination of the weathering degree of the granitic rocks. However, in literature, the adaptive neuro-fuzzy inference system has not been used for the weathering classification yet. For this reason, the main purpose of the present study is to apply some soft computing methods such as artificial neural networks and adaptive neuro-fuzzy inference system on the determination of weathering degree of a granitic rock selected from Turkey by using some index and mechanical properties. The study is formed by four main stages such as sampling, testing, modeling and assessment of the model performances. During the modeling stage, two weathering prediction models with multi-inputs are developed with two soft computing techniques such as artificial neural networks and the adaptive neuro-fuzzy inference system. The general performances of models developed in this study are close; however the adaptive neuro-fuzzy inference system exhibits the best performance considering the performance index and the degree of consistency. Finally, both models developed in this present study can be used when determining the weathering degree. The results obtained from this study revealed that the soft computing techniques used in the study are highly useful tools to solve some complex problems encountered frequently in engineering geology.

Dagdelenler, G.; Sezer, E.; Gokceoglu, C.

2010-05-01

241

(cont.) by presenting two case studies both in the context of the Chicago Transit Authority. One study proposes an enhanced method of inferring the rail trip OD matrix from an origin-only AFC system to replace the routine ...

Zhao, Jinhua, 1977-

2004-01-01

242

Background Model selection and parameter inference are complex problems that have yet to be fully addressed in systems biology. In contrast with parameter optimisation, parameter inference computes both the parameter means and their standard deviations (or full posterior distributions), thus yielding important information on the extent to which the data and the model topology constrain the inferred parameter values. Results We report on the application of nested sampling, a statistical approach to computing the Bayesian evidence Z, to the inference of parameters, and the estimation of log Z in an established model of circadian rhythms. A ten-fold difference in the coefficient of variation between degradation and transcription parameters is demonstrated. We further show that the uncertainty remaining in the parameter values is reduced by the analysis of increasing numbers of circadian cycles of data, up to 4 cycles, but is unaffected by sampling the data more frequently. Novel algorithms for calculating the likelihood of a model, and a characterisation of the performance of the nested sampling algorithm are also reported. The methods we develop considerably improve the computational efficiency of the likelihood calculation, and of the exploratory step within nested sampling. Conclusions We have demonstrated in an exemplar circadian model that the estimates of posterior parameter densities (as summarised by parameter means and standard deviations) are influenced predominately by the length of the time series, becoming more narrowly constrained as the number of circadian cycles considered increases. We have also shown the utility of the coefficient of variation for discriminating between highly-constrained and less-well constrained parameters. PMID:23899119

2013-01-01

243

Free-energy inference from partial work measurements in small systems.

Fluctuation relations (FRs) are among the few existing general results in nonequilibrium systems. Their verification requires the measurement of the total work performed on a system. Nevertheless in many cases only a partial measurement of the work is possible. Here we consider FRs in dual-trap optical tweezers where two different forces (one per trap) are measured. With this setup we perform pulling experiments on single molecules by moving one trap relative to the other. We demonstrate that work should be measured using the force exerted by the trap that is moved. The force that is measured in the trap at rest fails to provide the full dissipation in the system, leading to a (incorrect) work definition that does not satisfy the FR. The implications to single-molecule experiments and free-energy measurements are discussed. In the case of symmetric setups a second work definition, based on differential force measurements, is introduced. This definition is best suited to measure free energies as it shows faster convergence of estimators. We discuss measurements using the (incorrect) work definition as an example of partial work measurement. We show how to infer the full work distribution from the partial one via the FR. The inference process does also yield quantitative information, e.g., the hydrodynamic drag on the dumbbell. Results are also obtained for asymmetric dual-trap setups. We suggest that this kind of inference could represent a previously unidentified and general application of FRs to extract information about irreversible processes in small systems. PMID:25099353

Ribezzi-Crivellari, Marco; Ritort, Felix

2014-08-19

244

A multi-step predictor for dynamic system property forecasting

NASA Astrophysics Data System (ADS)

A reliable multi-step predictor is very useful to a wide array of industries to forecast the behavior of dynamic systems. In this paper, an adaptive predictor is developed based on a novel weighted recurrent neuro-fuzzy paradigm to forecast properties of dynamic systems. An online training technique is proposed to improve forecasting convergence and accommodate different operating conditions. The viability of the developed predictor is firstly evaluated based on benchmark data sets, and then it is implemented for real-time machinery system monitoring. The monitoring index is derived from measurement based on a beta kurtosis reference function. The investigation results show that the developed adaptive predictor is a reliable forecasting tool and is able to accommodate different system conditions. It can capture the system's dynamic behavior quickly and track the system's characteristics accurately. Its performance is superior to other classical forecasting schemes.

Wang, Wilson; Vrbanek, Josip, Jr.

2007-12-01

245

Use of fuzzy inference system for condition monitoring of induction motor

NASA Astrophysics Data System (ADS)

Three phase induction motors are commonly used in industry due to its robustness, simplicity of its construction and high reliability. The tasks performed by these motors grow increasingly complex because of modern industries hence there is a need to determine the faults. Early detection of faults will reduce an unscheduled machine downtime that can upset production deadlines and may cause heavy financial losses. This paper is focused in developing a computer based system using Fuzzy Inference system's membership function. An unusual increase in vibration of the motor could be an indicator of faulty condition hence the vibration of the motor of an induction motor was used as an input, whereas the output is the motor condition. An inference system of the Fuzzy Logic was created to classify the vibration characteristics of the motor which is called vibration analysis. The system classified the motor of the gas distribution pump condition as from 'acceptable' to 'monitor closely'. The early detection of unusual increase in vibration of the induction motor is an important part of a predictive maintenance for motor driven machinery.

Janier, Josefina B.; Zaim Zaharia, M. F.; Karim, Samsul Ariffin Abd.

2012-09-01

246

Imaging Search for Dynamically Inferred Planets in Nearby Debris Disk Systems

NASA Astrophysics Data System (ADS)

The nearby stars Eps Eri, Vega, and Fomalhaut all host large debris disks with morphological structures that can be interpreted as being due to dynamical influence from unseen giant planets residing in the systems. At the ages of the systems of a few hundred Myrs, such planets are expected to have cooled down to temperatures of ~200 K, which makes them unreachable from the ground due to their faintness at JHKL wavelengths and the prohibitively large thermal background at longer wavelengths. Spitzer, however, has the sensitivity required at 4.5 micron to detect such objects. As we have shown previously (Janson et al. 2012), a dedicated observing strategy and data reduction scheme can be used to improve the Spitzer contrast performance by more than an order of magnitude compared to conventional methods, which enables this degree of sensitivity down to separations of ~10'. The corresponding detection space provides an excellent match to the predicted properties of inferred companions in the three systems. Here, we propose to re-observe Fomalhaut to follow up a candidate companion detected in our previous image, and to observe Vega and Eps Eri to search for their inferred companions. In each case we will be sensitive to Jovian or sub-Jovian companions in the primary separation regions of interest, which is a factor 3 better mass sensitivity than previously achieved. These observations provide a unique opportunity to study far colder and more Jupiter-like planets than previously imaged.

Janson, Markus; Carson, Joe; Lafreniere, David; Spiegel, Dave; Quanz, Sascha; Thalmann, Christian; Amara, Adam

2012-12-01

247

Classification of delaminated composites using neuro-fuzzy image analysis

involves video imaging of a surface displacements illuminated by laser. A specially designed CCD camera produces two laterally sheared images of the surface. Shearography is based on the phenomenon that coherent examination, the test specimen is excited while illuminated by laser light. An image shearing CCD

Martin, Ralph R.

248

Modeling and experimentation of a positioning system of SMA wires

NASA Astrophysics Data System (ADS)

This work reports two modeling and control attempts performed on a positioning system comprising of linking SMA wires and an overlooking video system for on-line measurements. The first attempt takes the model by Ikuta and identifies experimentally the parameters of the SMA wire. The identified single wire model is then extended to a system of two SMA wires joining together at their tips, based upon which open loop position control of the linkage is then conducted. The approach, however, becomes too complicated when more SMA wires are involved. The second attempt utilizes a neuro-fuzzy based approach for positioning control of a linkage point joining together four SMA wires. The second approach involves four ANFIS neuro-networks with hybrid learning algorithm trained to model the currents to the SMA wires as functions of present and target positions of the linkage point. Experimentation for both the two-wires and four-wires system yield quite satisfactory performance.

Lei, KinFong; Yam, Yeung

2000-06-01

249

Strapdown fiber optic gyrocompass using adaptive network-based fuzzy inference system

NASA Astrophysics Data System (ADS)

This paper aims to propose a new strapdown fiber optic gyrocompass (strapdown FOGC) using adaptive network-based fuzzy inference system (ANFIS). The strapdown FOGC is based on the principle of strapdown inertial navigation system and utilizes electromagnetic log (EM log) and damping equalizer to bound oscillatory errors existing in attitude and heading. As the introduction of damping technique, extra errors are aroused by EM log errors. To decrease the extra errors, ANFIS is utilized to adjust the damping ratio automatically in terms of the ship maneuver conditions. The simulation and trial results indicate that, compared with the conventional gyrocompass scheme, the proposed one can reduce the attitude and heading errors and improve the system performance effectively.

Li, Qian; Ben, Yueyang; Sun, Feng

2014-01-01

250

NASA Technical Reports Server (NTRS)

The modifications to the design of a fault inferring nonlinear detection system (FINDS) algorithm to accommodate flight computer constraints and the resulting impact on the algorithm performance are summarized. An overview of the flight data-driven FINDS algorithm is presented. This is followed by a brief analysis of the effects of modifications to the algorithm on program size and execution speed. Significant improvements in estimation performance for the aircraft states and normal operating sensor biases, which have resulted from improved noise design parameters and a new steady-state wind model, are documented. The aircraft state and sensor bias estimation performances of the algorithm's extended Kalman filter are presented as a function of update frequency of the piecewise constant filter gains. The results of a new detection system strategy and failure detection performance, as a function of gain update frequency, are also presented.

Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

1986-01-01

251

The Gaia astrophysical parameters inference system (Apsis). Pre-launch description

The Gaia satellite will survey the entire celestial sphere down to 20th magnitude, obtaining astrometry, photometry, and low resolution spectrophotometry on one billion astronomical sources, plus radial velocities for over one hundred million stars. Its main objective is to take a census of the stellar content of our Galaxy, with the goal of revealing its formation and evolution. Gaia's unique feature is the measurement of parallaxes and proper motions with hitherto unparalleled accuracy for many objects. As a survey, the physical properties of most of these objects are unknown. Here we describe the data analysis system put together by the Gaia consortium to classify these objects and to infer their astrophysical properties using the satellite's data. This system covers single stars, (unresolved) binary stars, quasars, and galaxies, all covering a wide parameter space. Multiple methods are used for many types of stars, producing multiple results for the end user according to different models and assumptions. ...

Bailer-Jones, C A L; Arcay, B; Astraatmadja, T; Bellas-Velidis, I; Berihuete, A; Bijaoui, A; Carrión, C; Dafonte, C; Damerdji, Y; Dapergolas, A; de Laverny, P; Delchambre, L; Drazinos, P; Drimmel, R; Frémat, Y; Fustes, D; García-Torres, M; Guédé, C; Heiter, U; Janotto, A -M; Karampelas, A; Kim, D -W; Knude, J; Kolka, I; Kontizas, E; Kontizas, M; Korn, A J; Lanzafame, A C; Lebreton, Y; Lindstrøm, H; Liu, C; Livanou, E; Lobel, A; Manteiga, M; Martayan, C; Ordenovic, Ch; Pichon, B; Recio-Blanco, A; Rocca-Volmerange, B; Sarro, L M; Smith, K; Sordo, R; Soubiran, C; Surdej, J; Thévenin, F; Tsalmantza, P; Vallenari, A; Zorec, J

2013-01-01

252

Another expert system rule inference based on DNA molecule logic gates

NASA Astrophysics Data System (ADS)

With the help of silicon industry microfluidic processors were invented utilizing nano membrane valves, pumps and microreactors. These so called lab-on-a-chips combined together with molecular computing create molecular-systems-ona- chips. This work presents a new approach to implementation of molecular inference systems. It requires the unique representation of signals by DNA molecules. The main part of this work includes the concept of logic gates based on typical genetic engineering reactions. The presented method allows for constructing logic gates with many inputs and for executing them at the same quantity of elementary operations, regardless of a number of input signals. Every microreactor of the lab-on-a-chip performs one unique operation on input molecules and can be connected by dataflow output-input connections to other ones.

WÄ siewicz, Piotr

2013-10-01

253

This paper proposes a new method for short-term load forecasting in power systems. The proposed method makes use of simplified fuzzy inference that has an output variable in crisp number rather than fuzzy. The technique is quite popular for reducing computational effort. Also, it is quite acceptable to system operators in a sense that the output variable in crisp number

Hiroyuki Mori; Yasuyuki Sone

1998-01-01

254

The purpose of this research is to facilitate the understanding of remote sensing image classifications based on the integration of neural networks with fuzzy expert systems, which is often known as neuro-fuzzy systems. A neuro-fuzzy system is basically a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters (fuzzy sets

Fang Qiu

2000-01-01

255

An expert system shell for inferring vegetation characteristics: Atmospheric techniques (Task G)

NASA Technical Reports Server (NTRS)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG Subgoals have been reorganized into categories. A new subgoal category 'Atmospheric Techniques' containing two new subgoals has been implemented. The subgoal Atmospheric Passes allows the scientist to take reflectance data measured at ground level and predict what the reflectance values would be if the data were measured at a different atmospheric height. The subgoal Atmospheric Corrections allows atmospheric corrections to be made to data collected from an aircraft or by a satellite to determine what the equivalent reflectance values would be if the data were measured at ground level. The report describes the implementation and testing of the basic framework and interface for the Atmospheric Techniques Subgoals.

Harrison, P. Ann; Harrison, Patrick R.

1993-01-01

256

GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures.

Bank failure prediction is an important issue for the regulators of the banking industries. The collapse and failure of a bank could trigger an adverse financial repercussion and generate negative impacts such as a massive bail out cost for the failing bank and loss of confidence from the investors and depositors. Very often, bank failures are due to financial distress. Hence, it is desirable to have an early warning system (EWS) that identifies potential bank failure or high-risk banks through the traits of financial distress. Various traditional statistical models have been employed to study bank failures [J Finance 1 (1975) 21; J Banking Finance 1 (1977) 249; J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073]. However, these models do not have the capability to identify the characteristics of financial distress and thus function as black boxes. This paper proposes the use of a new neural fuzzy system [Foundations of neuro-fuzzy systems, 1997], namely the Generic Self-organising Fuzzy Neural Network (GenSoFNN) [IEEE Trans Neural Networks 13 (2002c) 1075] based on the compositional rule of inference (CRI) [Commun ACM 37 (1975) 77], as an alternative to predict banking failure. The CRI based GenSoFNN neural fuzzy network, henceforth denoted as GenSoFNN-CRI(S), functions as an EWS and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. The interaction between the selected features is captured in the form of highly intuitive IF-THEN fuzzy rules. Such easily comprehensible rules provide insights into the possible characteristics of financial distress and form the knowledge base for a highly desired EWS that aids bank regulation. The performance of the GenSoFNN-CRI(S) network is subsequently benchmarked against that of the Cox's proportional hazards model [J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073], the multi-layered perceptron (MLP) and the modified cerebellar model articulation controller (MCMAC) [IEEE Trans Syst Man Cybern: Part B 30 (2000) 491] in predicting bank failures based on a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed-bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the GenSoFNN-CRI(S) network as a bank failure classification and EWS is encouraging. PMID:15109685

Tung, W L; Quek, C; Cheng, P

2004-05-01

257

NASA Astrophysics Data System (ADS)

Threshold behavior in hydrological systems generally involves a qualitative change of a single process, the system response or the functioning of the system. Different types of thresholds and their underlying controls are examined using the example of the Lurbach karst system (Austria). This karst system receives concentrated allogenic recharge from the sinking stream Lurbach, which under low-flow conditions only resurges at the Hammerbach spring. Under medium- to high-flow conditions, however, an overflow toward another spring, the Schmelzbach outlet occurs. The overflow probably is activated when a conduit pathway connecting the two sub-catchments is flooded at a given threshold water level. Unfortunately, the value of this threshold cannot be determined, as information about water levels within this karst system are scarce due to the lack of observation wells and the inaccessibility of relevant cave sections. Yet a corresponding threshold discharge of the Hammerbach spring can be inferred from tracer test results. Interestingly, a tracer test conducted in 2008 suggests that the overflow is activated at a discharge lower than that inferred from tracer tests reported earlier (Wagner et al., EGU2011-7962). In order to better understand this suspected change in the discharge threshold, the physicochemical responses of the Hammerbach spring were analyzed. Applying the concept of process time scales (Birk and Wagner, EGU2013-11365) to the Hammerbach spring suggests that the threshold travel time controlling the response of the spring water temperature was changed in the time period from 2006 to 2009 relative to the years before. At the same time, the Hammerbach spring hydrograph appears to have changed. For instance, the flow duration curve and the master recession curves for the time period from 2006 to 2009 are found to be markedly different from those of earlier time periods. All of these observations can be consistently explained by a reduction of the conduit diameters within the Hammerbach sub-catchment, presumably caused by the redistribution of sediments due to a distinct flood event in 2005. This finding suggests that a change in the hydrological functioning of the Lurbach karst system occurred possibly because a threshold related to sediment transport was crossed. Whether or not such thresholds are crossed depends on processes and factors both internal and external to the karst aquifer. In the case considered here, the suspected redistribution of sediments, for instance, is likely controlled by geomorphologic processes within both the karst aquifer and the headwater catchment providing the allogenic recharge as well as by anthropogenic and climatic factors affecting the occurrence of extreme hydrological events. Identifying and understanding such controls is of paramount importance for assessing the uncertainty of model predictions in karst catchments.

Birk, Steffen; Wagner, Thomas; Mayaud, Cyril

2014-05-01

258

Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.

Petrov, S.

1996-10-01

259

neuro-fuzzy system that emerges from literature. 1 INTRODUCTION The growth of reliability, availabilityDevelopment of a prognostic tool to perform reliability analysis Mohamed El-Koujok, Rafael this frame, neuro-fuzzy systems are well suited for practical problems where it is easier to gather data

Paris-Sud XI, UniversitÃ© de

260

Bayesian Model Comparison and Parameter Inference in Systems Biology Using Nested Sampling

Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focusses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design. PMID:24523891

Pullen, Nick; Morris, Richard J.

2014-01-01

261

This study presents a new approach to adaptation of Sugeno type fuzzy inference systems using regularization, since regularization improves the robustness of standard parameter estimation algorithms leading to stable fuzzy approximation. The proposed method can be used for modelling, identification and control of physical processes. A recursive method for on-line identification of fuzzy parameters employing Tikhonov regularization is suggested. The

Mohit Kumar; Regina Stoll; Norbert Stoll

2003-01-01

262

The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets. PMID:25295291

Bal, Mert; Amasyali, M Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse

2014-01-01

263

The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets. PMID:25295291

Bal, Mert; Amasyali, M. Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse

2014-01-01

264

Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction.

Post-dialysis urea rebound (PDUR) has been attributed mostly to redistribution of urea from different compartments, which is determined by variations in regional blood flows and transcellular urea mass transfer coefficients. PDUR occurs after 30-90min of short or standard hemodialysis (HD) sessions and after 60min in long 8-h HD sessions, which is inconvenient. This paper presents adaptive network based on fuzzy inference system (ANFIS) for predicting intradialytic (Cint) and post-dialysis urea concentrations (Cpost) in order to predict the equilibrated (Ceq) urea concentrations without any blood sampling from dialysis patients. The accuracy of the developed system was prospectively compared with other traditional methods for predicting equilibrated urea (Ceq), post dialysis urea rebound (PDUR) and equilibrated dialysis dose (eKt/V). This comparison is done based on root mean squares error (RMSE), normalized mean square error (NRMSE), and mean absolute percentage error (MAPE). The ANFIS predictor for Ceq achieved mean RMSE values of 0.3654 and 0.4920 for training and testing, respectively. The statistical analysis demonstrated that there is no statistically significant difference found between the predicted and the measured values. The percentage of MAE and RMSE for testing phase is 0.63% and 0.96%, respectively. PMID:23806679

Azar, Ahmad Taher

2013-09-01

265

The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection

NASA Astrophysics Data System (ADS)

In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including "extremely preferred", "moderately preferred", and "weakly preferred". In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.

Tahriri, Farzad; Mousavi, Maryam; Hozhabri Haghighi, Siamak; Zawiah Md Dawal, Siti

2014-06-01

266

User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program

NASA Technical Reports Server (NTRS)

Described are the operation and internal structure of the computer program FINDS (Fault Inferring Nonlinear Detection System). The FINDS algorithm is designed to provide reliable estimates for aircraft position, velocity, attitude, and horizontal winds to be used for guidance and control laws in the presence of possible failures in the avionics sensors. The FINDS algorithm was developed with the use of a digital simulation of a commercial transport aircraft and tested with flight recorded data. The algorithm was then modified to meet the size constraints and real-time execution requirements on a flight computer. For the real-time operation, a multi-rate implementation of the FINDS algorithm has been partitioned to execute on a dual parallel processor configuration: one based on the translational dynamics and the other on the rotational kinematics. The report presents an overview of the FINDS algorithm, the implemented equations, the flow charts for the key subprograms, the input and output files, program variable indexing convention, subprogram descriptions, and the common block descriptions used in the program.

Caglayan, A. K.; Godiwala, P. M.; Satz, H. S.

1988-01-01

267

This paper presents a novel training algorithm for fuzzy inference systems. The algorithm combines the Levenberg-Marquardt algorithm with variable structure systems approach. The combination is performed by expressing the parameter update rule in continuous time and application of sliding mode control method to the gradient based training procedure. The proposed combination therefore exhibits a degree of robustness to the unmodeled

Mehmet Önder Efe; Okyay Kaynak

2001-01-01

268

Expert System Shell for Inferring Vegetation Characteristics: The Learning System (Tasks C and D).

National Technical Information Service (NTIS)

This report describes the implementation of a learning system that uses a data base of historical cover type reflectance data taken at different solar zenith angles and wavelengths to learn class descriptions of classes of cover types. It has been integra...

P. A. Harrison, P. R. Harrison

1992-01-01

269

A dental condition prediction system with artificial neural networks and fuzzy inference systems

Tooth decay (dental caries) can be prevented with a combination of daily home care and professional care. In the daily home care for the prevention of tooth decay, tooth brushing after every meal is very significant. Consequently, tooth brushing instruction forms an important part of the work of a dentist. In this paper, we develop a decision support system which

T. Okuda; T. Yoshida; M. Hotta

1997-01-01

270

NASA Astrophysics Data System (ADS)

In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.

Caticha, Ariel

2011-03-01

271

NASA Astrophysics Data System (ADS)

Integration of process planning with scheduling by considering the manufacturing system's capacity, cost and capacity in its workshop is a critical issue. The concurrency between them can also eliminate the redundant process and optimize the entire production cycle, but most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. In this paper, a fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The mean time to failure (MTF) values is input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalized based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, the particle swarm optimization (PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling.

Yang, Yahong; Zhao, Fuqing; Hong, Yi; Yu, Dongmei

2005-12-01

272

Inductive Inference: Theory and Methods

There has been a great deal of theoretical and experimental work in computer science on inductive inference systems, that is, systems that try to infer general rules from examples. However, a complete and applicable theory of such systems is still a distant goal. This survey highlights and explains the main ideas that have been developed in the study of inductive

Dana Angluin; Carl H. Smith

1983-01-01

273

Measure of librarian pressure using fuzzy inference system: A case study in Longyan University

NASA Astrophysics Data System (ADS)

As the hierarchy of middle managers in college's librarian. They may own much work pressure from their mind. How to adapt psychological problem, control the emotion and keep a good relationship in their work place, it becomes an important issue. Especially, they work in China mainland environment. How estimate the librarians work pressure and improve the quality of service in college libraries. Those are another serious issues. In this article, the authors would like discuss how can we use fuzzy inference to test librarian work pressure.

Huang, Jian-Jing

2014-10-01

274

This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistical decision making. The topics covered go from basic concepts and definitions (random variables, Bayes' rule, prior distributions) to various models of general use in biology (hierarchical models, in particular) and ways to calibrate and use them (MCMC methods, model checking, inference, and decision). The second half of this Bayesian primer develops an example of model setup, calibration, and inference for a physiologically based analysis of 1,3-butadiene toxicokinetics in humans. PMID:23086859

Bois, Frederic Y

2013-01-01

275

NASA Astrophysics Data System (ADS)

Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author's own publications have contributed network inference, simulation, modeling, and analysis methods to the much larger body of work in systems biology, and indeed, in network science. The aim of this thesis is therefore twofold: to present this original work in the historical context of network science, but also to provide sufficient review and reference regarding complex systems (with an emphasis on complex networks in systems biology) and tools and techniques for their inference, simulation, analysis, and modeling, such that the reader will be comfortable in seeking out further information on the subject. The review-like Chapters 1, 2, and 4 are intended to convey the co-evolution of network science and the slow but noticeable breakdown of boundaries between disciplines in academia as research and comparison of diverse systems has brought to light the shared properties of these systems. It is the author's hope that theses chapters impart some sense of the remarkable and rapid progress in complex systems research that has led to this unprecedented academic synergy. Chapters 3 and 5 detail the author's original work in the context of complex systems research. Chapter 3 presents the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B.subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. These networks are then analyzed from a graph theoretical perspective, and their biological viability is critiqued by comparing the networks' graph theoretical properties to those of other biological systems. The results of topological perturbation analyses revealing commonalities in behavior at multiple levels of complexity are also presented, and are shown to be an invaluable means by which to ascertain the level of complexity to which the network inference process is robust to noise. Chapter 5 outlines a learning algorithm for the development of a realistic, evolving social network (a city) into which a disease is introduced. The results of simulations in populat

Christensen, Claire Petra

276

Non-exponential tolerance to infection in epidemic systems--modeling, inference, and assessment.

The transmission dynamics of infectious diseases have been traditionally described through a time-inhomogeneous Poisson process, thus assuming exponentially distributed levels of disease tolerance following the Sellke construction. Here we focus on a generalization using Weibull individual tolerance thresholds under the susceptible-exposed-infectious-removed class of models which is widely employed in epidemics. Applications with experimental foot-and-mouth disease and historical smallpox data are discussed, and simulation results are presented. Inference is carried out using Markov chain Monte Carlo methods following a Bayesian approach. Model evaluation is performed, where the adequacy of the models is assessed using methodology based on the properties of Bayesian latent residuals, and comparison between 2 candidate models is also considered using a latent likelihood ratio-type test that avoids problems encountered with relevant methods based on Bayes factors. PMID:22522236

Streftaris, George; Gibson, Gavin J

2012-09-01

277

ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study.

Coagulation is the most important stage in drinking water treatment processes for the maintenance of acceptable treated water quality and economic plant operation, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, pH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Traditionally, jar tests are used to determine the optimum coagulant dosage. However, this is expensive and time-consuming and does not enable responses to changes in raw water quality in real time. Modelling can be used to overcome these limitations. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for modelling of coagulant dosage in drinking water treatment plant of Boudouaou, Algeria. Six on-line variables of raw water quality including turbidity, conductivity, temperature, dissolved oxygen, ultraviolet absorbance, and the pH of water, and alum dosage were used to build the coagulant dosage model. Two ANFIS-based Neuro-fuzzy systems are presented. The two Neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based (FIS), named ANFIS-SUB. The low root mean square error and high correlation coefficient values were obtained with ANFIS-SUB method of a first-order Sugeno type inference. This study demonstrates that ANFIS-SUB outperforms ANFIS-GRID due to its simplicity in parameter selection and its fitness in the target problem. PMID:21562791

Heddam, Salim; Bermad, Abdelmalek; Dechemi, Noureddine

2012-04-01

278

Space radiation effect on fibre optical gyroscope control circuit and compensation algorithm

NASA Astrophysics Data System (ADS)

The process of a ?-irradiation experiment of fibre optical gyroscope (FOG) control circuit was described, in which it is demonstrated that the FOG control circuit, except for D/A converter, could endure the dose of 10krad with the protection of cabin material. The distortion and drift in D/A converter due to radiation, which affect the performance of FOG seriously, was indicated based on the elemental analysis. Finally, a compensation network based on adaptive neuro-fuzzy inference system is proposed and its function is verified by simulation.

Zhang, Chun-Xi; Tian, Hai-Ting; Li, Min; Jin, Jing; Song, Ning-Fang

2008-02-01

279

A hybrid adaptive control strategy for a smart prosthetic hand.

This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The simulations of this hybrid controller, when compared with the proportional-integral-derivative (PID) controller showed enhanced performance. Work is in progress to extend this methodology to a five-fingered, three-dimensional prosthetic hand. PMID:19964853

Chen, Cheng-Hung; Naidu, D Subbaram; Perez-Gracia, Alba; Schoen, Marco P

2009-01-01

280

NASA Astrophysics Data System (ADS)

The inferred line-spread function is an easy technique for measuring orthogonal components of the two-dimensional modulation transfer function (MTF), even from the air. However, it has been most commonly used for cameras for which the resolution is nowhere near the Nyquist frequency. The purpose of such limitation is so that the pixel sampling does not have a serious consequence on the measurement of the MTF. The binning capability of the purely digital DIPOL camera is used to demonstrate that using this method even in moderately oversampled systems does not impact results as long as certain averaging techniques are used. A brief tutorial of the normalization and pitfalls of the method will also be given so that this powerful and simple measurement will become more widely used. Example images will also be shown of mine simulators, together with polarization-product images.

Suiter, Harold R.; Pham, Chuong N.; Arrieta, Rodolpho T.

2003-09-01

281

Bugs as deviant behavior: a general approach to inferring errors in systems code

A major obstacle to finding program errors in a real system is knowing what correctness rules the system must obey. These rules are often undocumented or specified in an ad hoc manner. This paper demonstrates techniques that automatically extract such checking information from the source code itself, rather than the programmer, thereby avoiding the need for a priori knowledge of

Dawson Engler; David Yu Chen; Seth Hallem; Andy Chou; Benjamin Chelf

2001-01-01

282

Inferring Network-Wide Quality in P2P Live Streaming Systems

This paper explores how to remotely monitor network-wide quality in mesh-pull P2P live streaming systems. Peers in such systems advertise to each other buffer maps which summarize the chunks of data that they currently have cached and make available for sharing. We show how buffer maps can be exploited to monitor network-wide quality. We show that information provided in a

Xiaojun Hei; Yong Liu; Keith W. Ross

2007-01-01

283

Inferring Network-Wide Quality in P2P Live Streaming Systems

This paper explores how to remotely monitor network-wide quality in mesh-pull P2P live streaming systems. Peers in such systems advertise to each other buffer maps which summarize the chunks of data that they currently have cached and make available for sharing. We show how buffer maps can be exploited to monitor network-wide quality. We show that information provided in a

Xiaojun Hei; Yong Liu; Keith W. Ross

284

Adaptive fuzzy system for 3-D vision

NASA Technical Reports Server (NTRS)

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

Mitra, Sunanda

1993-01-01

285

Inference for nonlinear dynamical systems E. L. Ionides, C. Breto , and A. A. King

). The growth rate of V. cholerae depends strongly on water temperature and salinity, which can fluctuate the intrinsic nonlinear dynamics of the system, reveals some effects overlooked by previous studies. maximum processing (1), economics (2), cell biology (3), mete- orology (4), ecology (5), neuroscience (6

Ionides, Edward

286

RULE-BASED INFERENCE SYSTEM FOR PREDICTING LINER/WASTE COMPATIBILITY

Determining the chemical compatibility of a liner material for containment of wastes rests mainly on the application of expert opinion to interpret the results of short-term immersion tests. A methodology known as a production system is employed to encode such expert opinion into...

287

Since reproductive cycles are shorter than life cycles every species tends towards overpopulation and must be subject to population control. Since man, the dominant life form in the ecosystem, adapts primarily through culture, his culture must include, as part of its adaptive scheme, a subsystem to control population growth. A systemic model of population control is presented involving female infanticide

William T. Divale

1972-01-01

288

A neural network mode inference engine for the advisory system for training and safety

for different phases of the flight. In order to perform this task, the ASTRAS system is endowed with an artificial intelligence engine or Situation Recognizer (SR) which is able to discern the flight mode from sensor readings. The current SR is based on fuzzy...

Nguyen, Thinh Xuan

2012-06-07

289

Automated adaptive inference of coarse-grained dynamical models in systems biology

Cellular regulatory dynamics is driven by large and intricate networks of interactions at the molecular scale, whose sheer size obfuscates understanding. In light of limited experimental data, many parameters of such dynamics are unknown, and thus models built on the detailed, mechanistic viewpoint overfit and are not predictive. At the other extreme, simple ad hoc models of complex processes often miss defining features of the underlying systems. Here we propose an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the amount of available data. Such adaptive models lead to accurate predictions even when microscopic details of the studied systems are unknown due to insufficient data. The approach is computationally tractable, even for a relatively large number of dynamical variables, allowing its software realization, named Sir Isaac, to make successful predictions even when important dynamic variables are unobserved. For e...

Daniels, Bryan C

2014-01-01

290

We perform a joint inversion of Quaternary strain rates and 238 Global Positioning System (GPS) velocities in Asia for a self-consistent velocity field. The reference frames for all geodetic velocity observations are determined in our inversion procedure. India (IN) moves relative to Eurasia (EU) about a pole of rotation at (29.78°N, 7.51°E, 0.353° Myr-1), which yields a velocity along the

W. E. Holt; N. Chamot-Rooke; X. Le Pichon; A. J. Haines; B. Shen-Tu; J. Ren

2000-01-01

291

Expert system for a nuclear power plant accident diagnosis using a fuzzy inference method

The huge and complicated plants such as nuclear power stations are likely to cause the operators to make mistakes due to a\\u000a variety of inexplicable reasons and symptoms in case of emergency. That’s why the prevention system assisting the operators\\u000a is being developed for. First of all, I suggest an improved fuzzy diagnosis. Secondly, I want to demonstrate that a

Mal-Rey Lee; Jong-Chul Oh

2001-01-01

292

Bayesian Hierarchical\\/Multilevel Models for Inference and Prediction Using Cross-System Lake Data

\\u000a Cross-system data have been extensively used to estimate models for predicting lake responses to management actions. Using\\u000a data from many lakes for model estimation is based on an implicit assumption that all lakes in the data set behave similarly.\\u000a A common strategy to help meet this assumption is to group the data by common lake features, such as geography, landscape

Craig A. Stow; E. Conrad Lamon; Song S. Qian; Patricia A. Soranno; Kenneth H. Recltbow

293

NASA Astrophysics Data System (ADS)

On volcanoes which display hydrothermal/magmatic unrests, Electromagnetic (EM) methods can be combined with geochemical (GC) and thermal methods. The integration of these methods allows to image in detail hydrothermal systems, to find out possible scenarios of volcanic unrest, and to monitor the on-going activity with knowledge on the sources of heat, gas and fluid transfers. Since the 1990's the volcano shows recurrent periods of seismic activity, ground deformation, hydrothermal activity, and surface activity (geysers). Combined EM and GC methods noticeably contribute to map in detail the hydrothermal system and to analyse the sources of the activity: - Total magnetic field mapping evidences demagnetised zones over the two main areas forming the hydrothermal system (in the northern part of Main crater (MC)). These low magnetized areas are ascribed to thermal sources located at some hundreds metres of depth, - Self-potential surveys, delineate the contours of the fluids-heat transfer, and the northern and southern structural discontinuities enclosing the hydrothermal system, - Ground temperature gradient measurements evidence the distinctive heat transfer modes, from low fluxes related to soil temperature dominated by solar input to extremely high temperature gradients of 1200 °C m-1 or to more related to magmatic fluids. - Ground temperature and surface temperature of central acidic lake calculated by Thermal Aster imaging highlight the location of the most active ground fissures, outcrops and diffuse areas. Higher and larger anomalies are observed in the northern part of MC. A rough estimation of the thermal discharge in the northern part of the volcano gives 17 MW. - CO2 concentrations and fluxes from soil supply inform on fluids origin and on local processes operating along active fractures. Much higher carbon dioxide fluxes at MC sites confirm that the source of Taal activity is presently located in the northern part of the crater. - Heat and fluids release from the hydrothermal system delineate a general NW-SE ellipsoid in the northern part of MC and may be related to a suspected NW-SE fault along which seismicity takes place and dikes are believed to intrude triggering volcanic crises. The northern flank of the volcano is mechanically and hydro thermally reactivated during seismic crises and this sector could be subjected to a flank failure.

Zlotnicki, Jacques; Toutain, Jean Paul; Sasai, Yoichi; Villacorte, Egardo; Bernard, Alain; Fauquet, Frederic; Nagao, Toshiyatsu

2010-05-01

294

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments. PMID:24068902

Yildiz, Izzet B.; von Kriegstein, Katharina; Kiebel, Stefan J.

2013-01-01

295

NASA Astrophysics Data System (ADS)

An expert system for diagnosing sickness and suggesting treatment based on Islamic Medication (IM) was constructed using Rule Based (RB) and Bayes' theorem (BT) algorithms independently as its inference engine. Comparative assessment on the quality of diagnosing based on symptoms provided by users for certain type of sickness using RB and BT reasoning that lead to the suggested treatment (based on IM) are discussed. Both approaches are found to be useful, each has its own advantages and disadvantages. Major difference of the two algorithms is the selection of symptoms during the diagnosing process. For BT, likely combinations of symptoms need to be classified for each sickness before the diagnosing process. This eliminates any irrelevant sickness based on the combination of symptoms provided by user and combination of symptoms that is unlikely. This is not the case for RB, it will diagnose the sickness as long as one the symptoms is related to the sickness regardless of unlikely combination. Few tests have been carried out using combinations of symptoms for same sickness to investigate their diagnosing accuracy in percentage. BT gives more promising diagnosing results compared to RB for each sickness that comes with common symptoms.

Daud, H.; Razali, R.; Low, T. J.; Sabdin, M.; Zafrul, S. Z. Mohd

2014-06-01

296

Crustal growth of oceanic island arc inferred from seismic structure of Mariana arc-backarc system

NASA Astrophysics Data System (ADS)

The Izu-Ogasawara-Marina arc (IBM arc) is one of the typical oceanic island arcs and it has developed repeating magmatic arc volcanisms and backarc spreading since Eocene. Because tectonics of the IBM arc is relatively simple and does not include collisions between the arc and a continent, it is one of best targets to research crustal growth. In 2003, wide-angle seismic survey using 106 ocean bottom seismographs had been carried out as a part of Margin program in collaboration between US and Japan in Mariana region. The seismic line runs from a serpentine diaper near the trench to Parece Vela basin through the Mariana arc, the Marina trough and the West Mariana ridge. We present the characteristics of the seismic structure of the Mariana arc-backarc system and discuss the crustal growth process by comparison with a structure of the northern Izu-Ogasawara arc. Main structural characteristics of the Mariana arc-backarc system are (1) variation of the crustal thickness (Mariana arc: 20 km, West Mariana ridge: 17 km, Mariana trough and Parece Vela basin: 6 km), (2) distribution of an andesitic middle crust with about P-wave velocity of 6 km/s, (3) variation of P-wave velocity in the middle crust (4) velocity anomalies of the lower crust in transition area between the arc and the backarc, (5) thickening of the lower crust under the Mariana trough axis and (6) slow mantle velocities under the Mariana arc, Mariana trough axis and the West Mariana ridge. Above characteristics from (1) to (4) are common to the seismic structure of the northern Izu-Ogasawara arc. In particular, the vertical P-wave velocity gradients of the middle crust under the forearc in both regions tend to become large rather than those under the arc. Main differences of seismic structures between both regions are the velocity gradients and an existence of a thin transition layer between the middle and lower crust. These differences and similarities of the velocity gradient might originate the age and indicate a difference of a crustal differentiation relating each tectonic stage.

Takahashi, N.; Kodaira, S.; Ito, A.; Klemperer, S. L.; Kaneda, Y.; Suyehiro, K.

2004-12-01

297

Asteroseismic inference on rotation, gyrochronology and planetary system dynamics of 16 Cygni

The solar analogs 16 Cyg A and 16 Cyg B are excellent asteroseismic targets in the \\Kepler field of view and together with a red dwarf and a Jovian planet form an interesting system. For these more evolved Sun-like stars we cannot detect surface rotation with the current \\Kepler data but instead use the technique of asteroseimology to determine rotational properties of both 16 Cyg A and B. We find the rotation periods to be $23.8^{+1.5}_{-1.8} \\rm \\, days$ and $23.2^{+11.5}_{-3.2} \\rm \\, days$, and the angles of inclination to be $56^{+6}_{-5} \\, ^{\\circ}$ and $36^{+17}_{-7} \\, ^{\\circ}$, for A and B respectively. Together with these results we use the published mass and age to suggest that, under the assumption of a solar-like rotation profile, 16 Cyg A could be used when calibrating gyrochronology relations. In addition, we discuss the known 16 Cyg B star-planet eccentricity and measured low obliquity which is consistent with Kozai cycling and tidal theory.

Davies, G R; Farr, W M; a, R A Garc\\'?; Lund, M N; Mathis, S; Metcalfe, T S; Appourchaux, T; Basu, S; Benomar, O; Campante, T L; Ceillier, T; Elsworth, Y; Handberg, R; Salabert, D; Stello, D

2014-01-01

298

Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

299

Bayesian inference Jean Daunizeau

inference 2 Numerical Bayesian inference methods 2.1 Sampling methods 2.2 Variational methods (EM, VB) 3 SPM versus Bayesian inference 2 Numerical Bayesian inference methods 2.1 Sampling methods 2.2 Variational methods 2.1 Sampling methods 2.2 Variational methods (EM, VB) 3 SPM applications 3.1 aMRI segmentation 3

Daunizeau, Jean

300

Continental shelf island systems, created by rising sea levels, provide a premier setting for studying the effects of past fragmentation, dispersal, and genetic drift on taxon diversification. We used phylogeographical (nested clade) and population genetic analyses to elucidate the relative roles of these processes in the evolutionary history of the Aegean Nigella arvensis alliance (= 'coenospecies'). We surveyed chloroplast DNA (cpDNA) variation in 455 individuals from 47 populations (nine taxa) of the alliance throughout its core range in the Aegean Archipelago and surrounding mainland areas of Greece and Turkey. The study revealed the presence of three major lineages, with largely nonoverlapping distributions in the Western, Central, and Eastern Aegean. There is evidence supporting the idea that these major lineages evolved in situ from a widespread (pan-Aegean) ancestral stock as a result of multiple fragmentation events, possibly due to the influence of post-Messinian sea flooding, Pleistocene eustatic changes and corresponding climate fluctuations. Over-sea dispersal and founder events appear to have played a rather insignificant role in the group's history. Rather, all analytical approaches identified the alliance as an organism group with poor seed dispersal capabilities and a susceptibility to genetic drift. In particular, we inferred that the observed level of cpDNA differentiation between Kikladian island populations of Nigella degenii largely reflects population history, (viz. Holocene island fragmentation) and genetic drift in the near absence of seed flow since their time of common ancestry. Overall, our cpDNA data for the N. arvensis alliance in general, and N. degenii in particular, indicate that historical events were important in determining the phylogeographical patterns seen, and that genetic drift has historically been relatively more influential on population structure than has cytoplasmic gene flow. PMID:16262859

Bittkau, C; Comes, H P

2005-11-01

301

. 1 Introduction It is well known that the genetic information of an organism, stored in its genome for inferring regulatory networks. With this concept, we intend to assess the effects of sta- tistical, is used to de- termine the phenotypic traits of that organism. Consistent with the central dogma

302

Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor

NASA Astrophysics Data System (ADS)

Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.

Wali, W. A.; Hassan, K. H.; Cullen, J. D.; Al-Shamma'a, A. I.; Shaw, A.; Wylie, S. R.

2011-08-01

303

NASA Astrophysics Data System (ADS)

We studied the ground deformation associated with the eruption at the Showa crater of Sakurajima, which has been active since 2006. Using the Mogi’s spherical pressure model, a volume change of magma chambers can be estimated from the displacement, tilt, or strain observations near the ground surface. After the application of the Mogi’s model to data in the past observations, the existence of two magma chambers has been inferred beneath the Sakurajima down to a depth of 5 km. The tilt and strain data in 2 underground tunnel sites observed 36 hours before an eruption in April 9, 2009, are analyzed to reveal the behavior of magma leading to eruption. From these data, there seems to be a time lag in the inflation between the two magma chambers at a depth of 4km and 0.1km, respectively. In addition, the order of the volume change of the shallow source is about one tenth of that of the deep one. A system which consists of shallow and deep magma chambers and a vertical conduit connecting them is numerically modeled to investigate the mechanism of the time lag and why the difference in the magnitude of the volumetric changes in the two chambers appears as described above. The initial values of magma properties in the deep magma chamber are assumed from the volcanic ejecta of Sakurajima volcano. We assumed that magma is supplied with a constant rate to the deep magma chamber. The two different pressure limits are assigned to the deep and shallow chamber, respectively: (1) one triggers the magma uprise from the deep to the shallow, and (2) the other to start to erupt. In a one-dimentional steady flow model of a magma conduit, we consider the vesiculation of volatile-bearing magma, gas escape and overpressure in the bubble due to the viscous resistance, which largely influences the physical properties of magma. Although our simulation results cannot exactly describe the data, we confirmed that our hypothetical model with two triggers could explain the time lag of the inflation and the difference in the order of the volume change. We would like to propose that the numerical simulation could be a powerful tool for understanding the behavior of magma before eruption once ground deformations are well observed.

Minami, S.; Iguchi, M.; Mikada, H.; Goto, T.; Takekawa, J.

2010-12-01

304

Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems

Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives. PMID:25153475

Sincak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Vircikova, Maria; Vranayova, Zuzana; Sabol, Jakub

2014-01-01

305

ERIC Educational Resources Information Center

Learning about what inferences are, and what a good inference is, will help students become more scientifically literate and better understand the nature of science in inquiry. Students in K-4 should be able to give explanations about what they investigate (NSTA 1997) and that includes doing so through inferring. This article provides some tips…

Finson, Kevin D.

2010-01-01

306

The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs. Thus, the identification scheme leads up to a recurrent high-order neural network (RHONN), which however takes into account the fuzzy output partitions of the initial FDS. The proposed scheme does not require a priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Once the system is identified around an operation point, it is regulated to zero adaptively. Weight updating laws for the involved HONNFs are provided, which guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating law. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given. Also, the applicability of the method is tested on a direct current (dc) motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation. PMID:19273046

Boutalis, Yiannis; Theodoridis, Dimitris C; Christodoulou, Manolis A

2009-04-01

307

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results\\u000a in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes\\u000a crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based\\u000a on stochastic modeling or artificial intelligence (AI)

Ahmed El-Shafie; Mahmoud Reda Taha; Aboelmagd Noureldin

2007-01-01

308

Neuro-fuzzy controller for gas turbine in biomass-based electric power plant

Biomass gasification is a technology that transforms solid biomass into syngas. The gas turbine controller regulates both the gas turbine and the gas turbine generator. Two fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers based on the operating conditions of

Francisco Jurado; Manuel Ortega; Antonio Cano; José Carpio

2002-01-01

309

Combining classifiers of pesticides toxicity through a neuro-fuzzy approach

. The increasing amount and complexity of data in toxicity prediction calls for new approaches based on hybrid% of the animals (for instance LC50: lethal concentration for 50% of the test animals). This dose is a continuous tolerance. The major shortcoming of neural networks is represented by their low degree of human

Gini, Giuseppina

310

A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller

NASA Astrophysics Data System (ADS)

This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.

Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi

311

User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach

ERIC Educational Resources Information Center

Internet evolution has affected all industrial, commercial, and especially learning activities in the new context of e-learning. Due to cost, time, or flexibility e-learning has been adopted by participators as an alternative training method. By development of computer-based devices and new methods of teaching, e-learning has emerged. The…

Fazlollahtabar, Hamed; Mahdavi, Iraj

2009-01-01

312

NASA Astrophysics Data System (ADS)

A bias correction skill in radar- and satellite-derived precipitation was built.The effectiveness of precipitation merging process was investigated by GA.The contribution of satellite and radar to merged product is about 10% and 24%.The ANFIS produced reliable rainfall forecasting with lead time of 1-2 h.

Chang, Fi-John; Chiang, Yen-Ming; Tsai, Meng-Jung; Shieh, Ming-Chang; Hsu, Kuo-Lin; Sorooshian, Soroosh

2014-01-01

313

Neuro-fuzzy control of vertical vibrations in railcars using magnetorheological dampers

are tested extensively in a laboratory and data obtained from these tests are used to train, test, and validate a fuzzy model of each damper. Two controller FIS are trained using NEFCON in a numerical environment to send a time varying voltage signal...

Atray, Vipul Sunil

2012-06-07

314

Intelligent Speed Adaptation Using a Self-Organizing Neuro-Fuzzy Controller

. The causes of these accidents are attributed to human error, alcohol, bad weather, heavy traffic or bad of road users and pedestrians being killed in traffic accidents each year. The Centre for Computational years. The global cost of road crashed and injuries is estimated to be US$ 518 billions per year

Paris-Sud XI, UniversitÃ© de

315

Representation and Reasoning Under Uncertainty in Deception Detection: A Neuro-Fuzzy Approach

An analysis of the process and human cognitive model of deception detection (DD) shows that DD is infused with uncertainty, especially in high-stake situations. There is a recent trend toward automating DD in computer-mediated communication. However, extant approaches to automatic DD overlook the importance of representation and reasoning under uncertainty in DD. They represent uncertain cues as crisp values and

Lina Zhou; Azene Zenebe

2008-01-01

316

Neuro-fuzzy control of a robotic exoskeleton with EMG signals

We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however,

Kazuo Kiguchi; Takakazu Tanaka; Toshio Fukuda

2004-01-01

317

Neuro-fuzzy model of superelastic shape memory alloys with application to seismic engineering

................................................................. 61 6.2.3. Earthquake Excitation................................................................. 64 6.3. Dynamic Analysis of a Three Story Benchmark Building................. 67 6.3.1. Optimization of SMA Bracing Elements.... Ranges of strain and strain rate.........................................................................50 Table 5. Maximum response of the frames to the scaled Kobe earthquake record.........66 Table 6. Dynamic characteristic of benchmark building...

Ozbulut, Osman Eser

2009-05-15

318

Neuro-fuzzy algorithm for quality assurance of resistance spot welding

Resistance spot welding is widely used in the field of plate assembly. However, there is currently no satisfactory nondestructive quality evaluation for this type of welding either in real-time or on-line. Moreover, even though the rate of welding under conditions of expulsion has been high until now, there is still no established method of quality control against expulsion. Accordingly, this

SangRyong Lee; YoonJun Choo; TaeYoung Lee; ChangWoo Han; MyunHee Kim

2000-01-01

319

A quality assurance technique for resistance spot welding using a neuro-fuzzy algorithm

Resistance spot welding is widely used in the field of plate assembly; however, there is currently no satisfactory nondestructive quality evaluation for this type of welding, either in real time or on-line. Moreover, even though the rate of welding under conditions of expulsion has been high until now, there is still no established method of quality control against expulsion.Accordingly, this

M. H. Kim; S. K. Choi

2001-01-01

320

in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P- wave. TABLE 2 Â ECG parameters Data & Methods AF Control gender male 28 32 female 15 23 age (years) average 70 ectopic sources and spread erratically over the atria - rates often exceeding 350/min. Â·Post surgical AF

Simon, Dan

321

activity, heart rate variability (HRV), and P- wave morphology. We hypothesize that the limitations. TABLE 1 Â DEMOGRAFICS OF DATABASE AF control male 28 32gender female 15 23 age average 70 59 (years

Simon, Dan

322

NASA Astrophysics Data System (ADS)

Advanced high strength steels are being increasingly used in the automotive industry to reduce weight and improve fuel economy. However, due to increased physical properties and chemistry of high strength steels, it is difficult to directly substitute these materials into production processes currently designed for mild steels. New process parameters and process-related issues must be developed and understood for high strength steels. Among all issues, endurance of the electrode cap is the most important. In this paper, electrode wear characteristics of hot-dipped galvanized dual-phase (DP600) steels and the effect on weld quality are firstly analysed. An electrode displacement curve which can monitor electrode wear was measured by a developing experimental system using a servo gun. A neuro-fuzzy inference system based on the electrode displacement curve is developed for minimizing the effect of a worn electrode on weld quality by adaptively adjusting input variables based on the measured electrode displacement curve when electrode wear occurs. A modified current curve is implemented to reduce the effects of electrode wear on weld quality using a developed neuro-fuzzy system.

Zhang, Y. S.; Wang, H.; Chen, G. L.; Zhang, X. Q.

2007-03-01

323

Inferring Task Structure From Data

Abstract: An algorithm is presented for fitting an expression composed of continuous and discontinuous primitive functions toreal-valued data points. The data modeling problem comes from the need to infer task structure for making coordinationdecisions for multi-agent systems. The presence of discontinuous primitive functions requires a novel approach.

Paul Utgoff David

324

Inferring Task Structure From Data

An algorithm is presented for fitting an expression composed of continuous and discontinuous primitive functions to real-valued data points. The data modeling problem comes from the need to infer task structure for making coordina- tion decisions for multi-agent systems. The presence of discontinuous primitive functions requires a novel approach.

Paul E. Utgoff; David Jensen; Victor Lesser

325

Causal Inference Problem Set 1.

Causal Inference Problem Set 1. 1. In children at risk for malaria, research is conducted in stimulating the immune system with micronutrients supplementation, an important one being vitamin A (e falciparum (Pf), in loge (counts / microL), for 6 children taking vitamin A supplementation and 6 children

Frangakis, Constantine

326

A new direct adaptive regulator with robustness analysis of systems in Brunovsky form.

The direct adaptive regulation of unknown nonlinear dynamical systems in Brunovsky form with modeling error effects, is considered in this paper. Since the plant is considered unknown, we propose its approximation by a special form of a Brunovsky type neuro-fuzzy dynamical system (NFDS) assuming also the existence of disturbance expressed as modeling error terms depending on both input and system states plus a not-necessarily-known constant value. The development is combined with a sensitivity analysis of the closed loop and provides a comprehensive and rigorous analysis of the stability properties. The existence and boundness of the control signal is always assured by introducing a novel method of parameter hopping and incorporating it in weight updating laws. Simulations illustrate the potency of the method and its applicability is tested on well known benchmarks, as well as in a bioreactor application. It is shown that the proposed approach is superior to the case of simple recurrent high order neural networks (HONN's). PMID:20726041

Theodoridis, Dimitrios; Boutalis, Yiannis; Christodoulou, Manolis

2010-08-01

327

Application of Transformations in Parametric Inference

ERIC Educational Resources Information Center

The objective of the present paper is to provide a simple approach to statistical inference using the method of transformations of variables. We demonstrate performance of this powerful tool on examples of constructions of various estimation procedures, hypothesis testing, Bayes analysis and statistical inference for the stress-strength systems.…

Brownstein, Naomi; Pensky, Marianna

2008-01-01

328

Efficient ECG signal analysis using wavelet technique for arrhythmia detection: an ANFIS approach

NASA Astrophysics Data System (ADS)

This paper deals with improved ECG signal analysis using Wavelet Transform Techniques and employing subsequent modified feature extraction for Arrhythmia detection based on Neuro-Fuzzy technique. This improvement is based on suitable choice of features in evaluating and predicting life threatening Ventricular Arrhythmia . Analyzing electrocardiographic signals (ECG) includes not only inspection of P, QRS and T waves, but also the causal relations they have and the temporal sequences they build within long observation periods. Wavelet-transform is used for effective feature extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) is considered for the classifier model. In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia and CSE databases, developed for validation purposes. Features based on the ECG waveform shape and heart beat intervals are used as inputs to the classifiers. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95.13% is achieved which is a significant improvement.

Khandait, P. D.; Bawane, N. G.; Limaye, S. S.

2010-02-01

329

NASA Astrophysics Data System (ADS)

This paper proposes a neuro-fuzzy model of NiTi shape memory alloy (SMA) wires that is capable of capturing behavior of superelastic SMAs at different temperatures and at various loading rates while remaining simple enough to realize numerical simulations. First, in order to collect data, uniaxial tensile tests are conducted on superelastic wires in the temperature range of 0 ÂºC to 40 ÂºC, and at the loading frequencies of 0.05 Hz to 2 Hz that is the range of interest for seismic applications. Then, an adaptive neuro-fuzzy inference system (ANFIS) is employed to construct a model of SMAs based on experimental input-output data pairs. The fuzzy model obtained from ANFIS training is validated by using an experimental data set that is not used during training. Upon having a model that can represent behavior of superelastic SMAs at various ambient temperature and loading-rates, nonlinear simulation of a multi-span continuous bridge isolated by rubber bearings that is equipped with SMA dampers is carried out. Response of the bridge to a historical earthquake record is presented at different ambient temperatures in order to evaluate the effect of temperature on the performance of the structure. It is shown that SMA damping elements can effectively decrease peak deck displacement and the relative displacement between piers and superstructure in an isolated bridge while recovering all the deformations to their original position.

Ozbulut, Osman E.; Hurlebaus, Stefan

2009-03-01

330

Student Model Based on Flexible Fuzzy Inference

NASA Astrophysics Data System (ADS)

In this paper we present a design of a student model based on generic fuzzy inference design. The membership functions and the rules of the fuzzy inference can be fine-tuned by the teacher during the learning process (run time) to suit the pedagogical needs, creating a more flexible environment. The design is used to represent the learner's performance. In order to test the human computer interaction of the system, a prototype of the system was developed with limited teaching materials. The interaction with the first prototype of the system demonstrated the effectiveness of the decision making using fuzzy inference.

Kseibat, Dawod; Mansour, Ali; Adjei, Osei; Phillips, Paul

331

Surface creep rate, observed along five branches of the dextral San Andreas fault system in northern California, varies considerably from one section to the next, indicating that so too may the depth at which the faults are locked. We model locking on 29 fault sections using each section’s mean long?term creep rate and the consensus values of fault width and geologic slip rate. Surface creep rate observations from 111 short?range alignment and trilateration arrays and 48 near?fault, Global Positioning System station pairs are used to estimate depth of creep, assuming an elastic half?space model and adjusting depth of creep iteratively by trial and error to match the creep observations along fault sections. Fault sections are delineated either by geometric discontinuities between them or by distinctly different creeping behaviors. We remove transient rate changes associated with five large (M?5.5) regional earthquakes. Estimates of fraction locked, the ratio of moment accumulation rate to loading rate, on each section of the fault system provide a uniform means to inform source parameters relevant to seismic?hazard assessment. From its mean creep rates, we infer the main branch (the San Andreas fault) ranges from only 20%±10% locked on its central creeping section to 99%–100% on the north coast. From mean accumulation rates, we infer that four urban faults appear to have accumulated enough seismic moment to produce major earthquakes: the northern Calaveras (M 6.8), Hayward (M 6.8), Rodgers Creek (M 7.1), and Green Valley (M 7.1). The latter three faults are nearing or past their mean recurrence interval.

Lienkaemper, James J.; McFarland, Forrest S.; Simpson, Robert W.; Caskey, S. John

2014-01-01

332

Inferring signalling networks from images.

The mapping of signalling networks is one of biology's most important goals. However, given their size, complexity and dynamic nature, obtaining comprehensive descriptions of these networks has proven extremely challenging. A fast and cost-effective means to infer connectivity between genes on a systems-level is by quantifying the similarity between high-dimensional cellular phenotypes following systematic gene depletion. This review describes the methodology used to map signalling networks using data generated in the context of RNAi screens. PMID:23841886

Evans, L; Sailem, H; Vargas, P Pascual; Bakal, C

2013-10-01

333

NASA Technical Reports Server (NTRS)

A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)

2001-01-01

334

Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising

Claire Petra Christensen

2007-01-01

335

Social Inference Through Technology

NASA Astrophysics Data System (ADS)

Awareness cues are computer-mediated, real-time indicators of people’s undertakings, whereabouts, and intentions. Already in the mid-1970 s, UNIX users could use commands such as “finger” and “talk” to find out who was online and to chat. The small icons in instant messaging (IM) applications that indicate coconversants’ presence in the discussion space are the successors of “finger” output. Similar indicators can be found in online communities, media-sharing services, Internet relay chat (IRC), and location-based messaging applications. But presence and availability indicators are only the tip of the iceberg. Technological progress has enabled richer, more accurate, and more intimate indicators. For example, there are mobile services that allow friends to query and follow each other’s locations. Remote monitoring systems developed for health care allow relatives and doctors to assess the wellbeing of homebound patients (see, e.g., Tang and Venables 2000). But users also utilize cues that have not been deliberately designed for this purpose. For example, online gamers pay attention to other characters’ behavior to infer what the other players are like “in real life.” There is a common denominator underlying these examples: shared activities rely on the technology’s representation of the remote person. The other human being is not physically present but present only through a narrow technological channel.

Oulasvirta, Antti

336

Hybrid optical inference machines - Architectural considerations

NASA Astrophysics Data System (ADS)

A class of optical computing systems is introduced for solving symbolic logic problems that are characterized by a set of data objects and a set of relationships describing the data objects. The data objects and relationships are arranged into sets of facts and rules to form a knowledge base. The solutions to symbolic logic problems involve inferring conclusions to queries by applying logical inference to the facts and rules. The general structure of an inference machine is discussed in terms of rule-driven and query-driven control flows. As examples of a query-driven inference machine, two hybrid optical system architectures are presented which use matched-filter and mapped-template logic, respectively.

Warde, C.; Kottas, J.

1986-03-01

337

A Real-Time Intelligent Wireless Mobile Station Location Estimator with Application to TETRA Network

Mobile location estimation has received considerable interest over the past few years due to its great potential in different applications such as logistics, patrol, and fleet management. Many mobile location estimation techniques had been proposed to improve the accuracy of location estimation. Location estimation based on artificial intelligence techniques is a recent alternative approach. In this paper, adaptive neuro-fuzzy inference

Faihan D. Alotaibi; Adel Abdennour; Adel Ahmed Ali

2009-01-01

338

NASA Astrophysics Data System (ADS)

creep, depending on its rate and spatial extent, is thought to reduce earthquake hazard by releasing tectonic strain aseismically. We use Bayesian inversion and a newly expanded GPS data set to infer the deep slip rates below assigned locking depths on the San Andreas, Maacama, and Bartlett Springs Faults of Northern California and, for the latter two, the spatially variable interseismic creep rate above the locking depth. We estimate deep slip rates of 21.5 ± 0.5, 13.1 ± 0.8, and 7.5 ± 0.7 mm/yr below 16 km, 9 km, and 13 km on the San Andreas, Maacama, and Bartlett Springs Faults, respectively. We infer that on average the Bartlett Springs fault creeps from the Earth's surface to 13 km depth, and below 5 km the creep rate approaches the deep slip rate. This implies that microseismicity may extend below the locking depth; however, we cannot rule out the presence of locked patches in the seismogenic zone that could generate moderate earthquakes. Our estimated Maacama creep rate, while comparable to the inferred deep slip rate at the Earth's surface, decreases with depth, implying a slip deficit exists. The Maacama deep slip rate estimate, 13.1 mm/yr, exceeds long-term geologic slip rate estimates, perhaps due to distributed off-fault strain or the presence of multiple active fault strands. While our creep rate estimates are relatively insensitive to choice of model locking depth, insufficient independent information regarding locking depths is a source of epistemic uncertainty that impacts deep slip rate estimates.

Murray, Jessica R.; Minson, Sarah E.; Svarc, Jerry L.

2014-07-01

339

Background The core enzymes of the DNA replication systems show striking diversity among cellular life forms and more so among viruses. In particular, and counter-intuitively, given the central role of DNA in all cells and the mechanistic uniformity of replication, the core enzymes of the replication systems of bacteria and archaea (as well as eukaryotes) are unrelated or extremely distantly related. Viruses and plasmids, in addition, possess at least two unique DNA replication systems, namely, the protein-primed and rolling circle modalities of replication. This unexpected diversity makes the origin and evolution of DNA replication systems a particularly challenging and intriguing problem in evolutionary biology. Results I propose a specific succession for the emergence of different DNA replication systems, drawing argument from the differences in their representation among viruses and other selfish replicating elements. In a striking pattern, the DNA replication systems of viruses infecting bacteria and eukaryotes are dominated by the archaeal-type B-family DNA polymerase (PolB) whereas the bacterial replicative DNA polymerase (PolC) is present only in a handful of bacteriophage genomes. There is no apparent mechanistic impediment to the involvement of the bacterial-type replication machinery in viral DNA replication. Therefore, I hypothesize that the observed, markedly unequal distribution of the replicative DNA polymerases among the known cellular and viral replication systems has a historical explanation. I propose that, among the two types of DNA replication machineries that are found in extant life forms, the archaeal-type, PolB-based system evolved first and had already given rise to a variety of diverse viruses and other selfish elements before the advent of the bacterial, PolC-based machinery. Conceivably, at that stage of evolution, the niches for DNA-viral reproduction have been already filled with viruses replicating with the help of the archaeal system, and viruses with the bacterial system never took off. I further suggest that the two other systems of DNA replication, the rolling circle mechanism and the protein-primed mechanism, which are represented in diverse selfish elements, also evolved prior to the emergence of the bacterial replication system. This hypothesis is compatible with the distinct structural affinities of PolB, which has the palm-domain fold shared with reverse transcriptases and RNA-dependent RNA polymerases, and PolC that has a distinct, unrelated nucleotidyltransferase fold. I propose that PolB is a descendant of polymerases that were involved in the replication of genetic elements in the RNA-protein world, prior to the emergence of DNA replication. By contrast, PolC might have evolved from an ancient non-templated polymerase, e.g., polyA polymerase. The proposed temporal succession of the evolving DNA replication systems does not depend on the specific scenario adopted for the evolution of cells and viruses, i.e., whether viruses are derived from cells or virus-like elements are thought to originate from a primordial gene pool. However, arguments are presented in favor of the latter scenario as the most parsimonious explanation of the evolution of DNA replication systems. Conclusion Comparative analysis of the diversity of genomic strategies and organizations of viruses and cellular life forms has the potential to open windows into the deep past of life's evolution, especially, with the regard to the origin of genome replication systems. When complemented with information on the evolution of the relevant protein folds, this comparative approach can yield credible scenarios for very early steps of evolution that otherwise appear to be out of reach. Reviewers Eric Bapteste, Patrick Forterre, and Mark Ragan. PMID:17176463

Koonin, Eugene V

2006-01-01

340

On the criticality of inferred models

NASA Astrophysics Data System (ADS)

Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality.

Mastromatteo, Iacopo; Marsili, Matteo

2011-10-01

341

In this article the author presents JFK, which stands for Java Fuzzy Kit. JFK is an Application Programming Interface (API) that complies with both, a general structure of a fuzzy rule base and the necessary processing to compute the generalized principle of extension. A recurrent structure is found for a class of fuzzy expert systems, known as the Mamdani model.

Omar López-ortega

2008-01-01

342

FUNCTIONAL OVERLAP OF ROOT SYSTEMS IN AN OLD-GROWTH FOREST INFERRED FROM TRACER 15N UPTAKE

Belowground competition for nutrients and water is considered a key factor affecting spatial organization and productivity of individual stems within forest stands, yet there are few data describing the lateral extent and overlap of competing root systems. We quantified the func...

343

NASA Astrophysics Data System (ADS)

Spatial and temporal variability of sediment storage in fluvio-deltaic sedimentary systems is controlled by the interplay of allogenic and autogenic processes. In order to investigate the effects of this interplay on the resulting stratigraphy at varying spatio-temporal scales, we carried out a series of numerical experiments using an aggregated process-based model of fluvio-deltaic systems (SIMCLAST), which combines diffusive and advective transport with sub-grid channel stability algorithms in the fluvial domain. New distributary channels occur by avulsions under conditions of local superelevation or through bifurcations due to mouth bar deposition. A series of numerical experiments were performed under forcing by glacio-eustatic sealevel cycles in the order of 100 kyr. Initial conditions of all experiments are represented by classic continental-margin topography with a shelf break. In this scenario, erosional features (canyons) are developed when sea level falls below the shelf break. Sediment supply and liquid discharge remain constant throughout the experiments. In order to characterize the topographic variability during the experiments, we used a difference measure obtained by summation of local changes in net sediment accumulation rates across the entire model domain. Long-term average variability (10 kyr resolution) correlates strongly with the allogenic sea-level signal. The long-term variability reaches a maximum around the time interval corresponding to isochronous maximum flooding surfaces, when retrogradation gives way to a new episode of progradation. Long-term mean variability is lowest during periods of sea-level fall, when incision restricts sediment dispersal. Increasing the time resolution of our difference measure allows recognition of numerous small peaks which correspond to local changes in sediment accumulation rates induced by autogenic processes (avulsions and bifurcations). The amplitudes of these peaks are related to the rate of change of the allogenic sea-level signal. During intervals characterized by positive rates of sea-level change, retrogradational stratal patterns are generated in which avulsions are the dominant autogenic control on spatial variability. During intervals characterized by negative rates of sea-level change, progradational stratal patterns are generated in which bifurcations are the dominant autogenic control on spatial variability. The highest amplitudes of the high-resolution difference measure occur during sea-level rise, because avulsions affect the entire downstream portion of the sediment dispersal system, whereas bifurcations affect only the terminal parts of the system. These results indicate that the relation between autogenic and allogenic processes varies throughout a base-level cycle, and underscores the fact that the relative importance and the type of autogenic processes occurring in fluvio-deltaic systems are governed by allogenic (low-frequency) forcing.

Karamitopoulos, P.; Weltje, G.; Dalman, R.

2011-12-01

344

Financial interaction networks inferred from traded volumes

NASA Astrophysics Data System (ADS)

In order to use the advanced inference techniques available for Ising models, we transform complex data (real vectors) into binary strings, by local averaging and thresholding. This transformation introduces parameters, which must be varied to characterize the behaviour of the system. The approach is illustrated on financial data, using three inference methods -- equilibrium, synchronous and asynchronous inference -- to construct functional connections between stocks. We show that the traded volume information is enough to obtain well known results about financial markets, which use however the presumably richer price information: collective behaviour ("market mode") and strong interactions within industry sectors. Synchronous and asynchronous Ising inference methods give results which are coherent with equilibrium ones, and more detailed since the obtained interaction networks are directed.

Zeng, Hong-Li; Lemoy, Rémi; Alava, Mikko

2014-07-01

345

The authors are developing a computer application, called the Bayes Inference Engine, to provide the means to make inferences about models of physical reality within a Bayesian framework. The construction of complex nonlinear models is achieved by a fully object-oriented design. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical programming environment. Maximum a posteriori solutions are achieved using a general, gradient-based optimization algorithm. The application incorporates a new technique of estimating and visualizing the uncertainties in specific aspects of the model.

Hanson, K.M.; Cunningham, G.S.

1996-04-01

346

ANFIS based modeling and inverse control of a thin SMA wire

NASA Astrophysics Data System (ADS)

In this work, we propose an Adaptive Neuro Fuzzy Inference System (ANFIS) based hysteresis modeling and control strategy for a thin Shape Memory Alloy (SMA) wire. Controlling the SMA wire is a challenging problem because of its dynamic hysteretic behavior. By using a hybrid learning procedure ANFIS architectures are powerful tools for many applications, such as identifying nonlinear parameters in a controlled system, predicting chaotic time series and modeling nonlinear functions. We tested our ANFIS model by making it predict major and minor hysteresis loops in different driving frequencies and compared them with the experimental data. To compensate the hysteretic effect, we used an inverse ANFIS model and used it directly as a controller. After dramatically reducing the hysteretic effect, we implemented a PI control to fine tune the response.

Kilicarslan, Atilla; Song, Gangbing; Grigoriadis, Karolos

2008-03-01

347

A wavelet transform based feature extraction and classification of cardiac disorder.

This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved. PMID:25023652

Sumathi, S; Beaulah, H Lilly; Vanithamani, R

2014-09-01

348

Inferring signalling networks from images

The mapping of signalling networks is one of biology’s most important goals. However, given their size, complexity and dynamic nature, obtaining comprehensive descriptions of these networks has proven extremely challenging. A fast and cost-effective means to infer connectivity between genes on a systems-level is by quantifying the similarity between high-dimensional cellular phenotypes following systematic gene depletion. This review describes the methodology used to map signalling networks using data generated in the context of RNAi screens. PMID:23841886

Evans, L; Sailem, H; Vargas, P Pascual; Bakal, C

2013-01-01

349

Sampling in Statistical Inference

NSDL National Science Digital Library

This site, presented by the Department of Statistics at Yale University, gives an explanation, a definition and an example of sampling in statistical inference. Topics include parameters, statistics, sampling distributions, bias, and variability. Overall, this is a great resource for any mathematics classroom studying statistics.

Lacey, Michelle

2008-12-23

350

Bayesian inference in geomagnetism

The inverse problem in empirical geomagnetic modeling is investigated, with critical examination of recently published studies. Particular attention is given to the use of Bayesian inference (BI) to select the damping parameter lambda in the uniqueness portion of the inverse problem. The mathematical bases of BI and stochastic inversion are explored, with consideration of bound-softening problems and resolution in linear

George E. Backus

1988-01-01

351

of Approximate Reasoning 1994 11:1 158 c 1994 Elsevier Science Inc. 655 Avenue of the Americas, New York, NY for probabilistic infer- ence. We describe, in procedural fashion, the Probability Propagation in Trees of Clusters then be spent conducting research and developing applications that make use of this technology. 1.2. What

Page Jr., C. David

352

Software for Weibull Inference

Exact inference, that is, confidence limits and hypothesis tests for the Weibull distribution parameters and percentiles based on maximum likelihood estimation in complete or type II censored samples requires the determination via simulation of percentage points of the distribution of certain pivotal quantities. Tables have been published for a limited range of sample sizes and are scattered through the literature.

John I. McCool

2011-01-01

353

NSDL National Science Digital Library

Different approaches to teaching the reading comprehension strategy of inferring in K-5 classrooms are identified in this article. The article appears in the free, online magazine Beyond Weather and the Water Cycle, which is structured around the essential principles of climate science.

Fries-Gaither, Jessica

2011-05-01

354

Background Main waterfowl migration systems are well understood through ringing activities. However, in mallards (Anas platyrhynchos) ringing studies suggest deviations from general migratory trends and traditions in waterfowl. Furthermore, surprisingly little is known about the population genetic structure of mallards, and studying it may yield insight into the spread of diseases such as Avian Influenza, and in management and conservation of wetlands. The study of evolution of genetic diversity and subsequent partitioning thereof during the last glaciation adds to ongoing discussions on the general evolution of waterfowl populations and flyway evolution. Hypothesised mallard flyways are tested explicitly by analysing mitochondrial mallard DNA from the whole northern hemisphere. Results Phylogenetic analyses confirm two mitochondrial mallard clades. Genetic differentiation within Eurasia and North-America is low, on a continental scale, but large differences occur between these two land masses (FST = 0.51). Half the genetic variance lies within sampling locations, and a negligible portion between currently recognised waterfowl flyways, within Eurasia and North-America. Analysis of molecular variance (AMOVA) at continent scale, incorporating sampling localities as smallest units, also shows the absence of population structure on the flyway level. Finally, demographic modelling by coalescence simulation proposes a split between Eurasia and North-America 43,000 to 74,000 years ago and strong population growth (~100fold) since then and little migration (not statistically different from zero). Conclusions Based on this first complete assessment of the mallard's world-wide population genetic structure we confirm that no more than two mtDNA clades exist. Clade A is characteristic for Eurasia, and clade B for North-America although some representatives of clade A are also found in North-America. We explain this pattern by evaluating competing hypotheses and conclude that a complex mix of historical, recent and anthropogenic factors shaped the current mallard populations. We refute population classification based on flyways proposed by ornithologists and managers, because they seem to have little biological meaning. Our results have implications for wetland management and conservation, with special regard to the release of farmed mallards for hunting, as well as for the possible transmission of Avian Influenza by mallards due to migration. PMID:22093799

2011-01-01

355

Distributed inference : combining variational inference with distributed computing

The study of inference techniques and their use for solving complicated models has taken off in recent years, but as the models we attempt to solve become more complex, there is a worry that our inference techniques will ...

Calabrese, Chris, M. Eng. Massachusetts Institute of Technology

2013-01-01

356

3D image analysis and artificial intelligence for bone disease classification.

In order to prevent bone fractures due to disease and ageing of the population, and to detect problems while still in their early stages, 3D bone micro architecture needs to be investigated and characterized. Here, we have developed various image processing and simulation techniques to investigate bone micro architecture and its mechanical stiffness. We have evaluated morphological, topological and mechanical bone features using artificial intelligence methods. A clinical study is carried out on two populations of arthritic and osteoporotic bone samples. The performances of Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machines (SVM) and Genetic Algorithm (GA) in classifying the different samples have been compared. Results show that the best separation success (100 %) is achieved with Genetic Algorithm. PMID:20703627

Akgundogdu, Abdurrahim; Jennane, Rachid; Aufort, Gabriel; Benhamou, Claude Laurent; Ucan, Osman Nuri

2010-10-01

357

ANFIS-based approach for the estimation of transverse mixing coefficient.

Understanding of the fate of pollutants, disposed of in streams, is a matter of concern in recent years for the effective control of pollution. Transverse mixing of the pollutants in open channels is arguably more important than the longitudinal mixing and near-field mixing. Several attempts have been made to establish the relationship between the transverse mixing coefficient and bulk channel and flow parameters such as width, depth, shear velocity, friction factor, curvature and sinuosity. This paper presents adaptive neuro fuzzy inference system (ANFIS) approach to predict the transverse mixing coefficient in open channel flows. Available laboratory and field data for the transverse mixing coefficients covering wide range of channel and flow conditions are used for the development and testing of the proposed method. The proposed ANFIS approach produces satisfactory results (R(2)=0.945) compared to the artificial neural network (ANN) model and existing predictors for mixing coefficient. PMID:21411952

Ahmad, Z; Azamathulla, H Md; Zakaria, N A

2011-01-01

358

Approximately more than 90% of all coal production in Iranian underground mines is derived directly longwall mining method. Out of seam dilution is one of the essential problems in these mines. Therefore the dilution can impose the additional cost of mining and milling. As a result, recognition of the effective parameters on the dilution has a remarkable role in industry. In this way, this paper has analyzed the influence of 13 parameters (attributed variables) versus the decision attribute (dilution value), so that using two approximate reasoning methods, namely Rough Set Theory (RST) and Self Organizing Neuro- Fuzzy Inference System (SONFIS) the best rules on our collected data sets has been extracted. The other benefit of later methods is to predict new unknown cases. So, the reduced sets (reducts) by RST have been obtained. Therefore the emerged results by utilizing mentioned methods shows that the high sensitive variables are thickness of layer, length of stope, rate of advance, number of miners, type of...

Owladeghaffari, H; Saeedi, G H R

2008-01-01

359

Forecasting daily lake levels using artificial intelligence approaches

NASA Astrophysics Data System (ADS)

Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.

Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher

2012-04-01

360

Estimating discharge coefficient of semi-elliptical side weir using ANFIS

NASA Astrophysics Data System (ADS)

SummaryA labyrinth weir is defined as a weir crest that is not straight in planform. The increased sill length provided by the semi-elliptical labyrinth side weirs effectively reduces upstream head to the particular discharge. They can therefore be used to particular advantage where the width of a channel is restricted and a weir is required to pass a range of discharges with a limited variation in upstream water level. In this study, the discharge capacity of semi-elliptical side weirs is estimated by using Adaptive-Neuro Fuzzy Inference System (ANFIS). 675 Laboratory test results are used for determining discharge coefficient of semi-elliptical labyrinth side weirs. The performance of the ANFIS model is compared Multiple Linear Regression (MLR) and Nonlinear Regression (NLR) models based on performance evaluation parameters. Comparison results indicated that the ANFIS technique could be successfully employed in modeling discharge coefficient.

Dursun, O. Faruk; Kaya, Nihat; Firat, Mahmut

2012-03-01

361

SYSTEM SUPPORT FOR FORENSIC INFERENCE

, indirectly deters crime by punishing its perpetrators after they have acted. The difference in the two prohibited acts are in progress. An important consequence is that behavior can be policed even when users policy is so challenging that corporations routinely outsource the task to specialized consultants [5, 13

Stehr, Mark-Oliver

362

In Defense of Imperative Inference

“Surrender; therefore, surrender or fight” is apparently an argument corresponding to an inference from an imperative to an\\u000a imperative. Several philosophers, however (Williams 1963; Wedeking 1970; Harrison 1991; Hansen 2008), have denied that imperative\\u000a inferences exist, arguing that (1) no such inferences occur in everyday life, (2) imperatives cannot be premises or conclusions\\u000a of inferences because it makes no sense

Peter B. M. Vranas

2010-01-01

363

Action understanding and active inference

We have suggested that the mirror-neuron system might be usefully understood as implementing Bayes-optimal perception of actions emitted by oneself or others. To substantiate this claim, we present neuronal simulations that show the same representations can prescribe motor behavior and encode motor intentions during action–observation. These simulations are based on the free-energy formulation of active inference, which is formally related to predictive coding. In this scheme, (generalised) states of the world are represented as trajectories. When these states include motor trajectories they implicitly entail intentions (future motor states). Optimizing the representation of these intentions enables predictive coding in a prospective sense. Crucially, the same generative models used to make predictions can be deployed to predict the actions of self or others by simply changing the bias or precision (i.e. attention) afforded to proprioceptive signals. We illustrate these points using simulations of handwriting to illustrate neuronally plausible generation and recognition of itinerant (wandering) motor trajectories. We then use the same simulations to produce synthetic electrophysiological responses to violations of intentional expectations. Our results affirm that a Bayes-optimal approach provides a principled framework, which accommodates current thinking about the mirror-neuron system. Furthermore, it endorses the general formulation of action as active inference. PMID:21327826

Mattout, Jeremie; Kilner, James

2012-01-01

364

Autonomous agricultural remote sensing systems with high spatial and temporal resolutions

NASA Astrophysics Data System (ADS)

In this research, two novel agricultural remote sensing (RS) systems, a Stand-alone Infield Crop Monitor RS System (SICMRS) and an autonomous Unmanned Aerial Vehicles (UAV) based RS system have been studied. A high-resolution digital color and multi-spectral camera was used as the image sensor for the SICMRS system. An artificially intelligent (AI) controller based on artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) was developed. Morrow Plots corn field RS images in the 2004 and 2006 growing seasons were collected by the SICMRS system. The field site contained 8 subplots (9.14 m x 9.14 m) that were planted with corn and three different fertilizer treatments were used among those subplots. The raw RS images were geometrically corrected, resampled to 10cm resolution, removed soil background and calibrated to real reflectance. The RS images from two growing seasons were studied and 10 different vegetation indices were derived from each day's image. The result from the image processing demonstrated that the vegetation indices have temporal effects. To achieve high quality RS data, one has to utilize the right indices and capture the images at the right time in the growing season. Maximum variations among the image data set are within the V6-V10 stages, which indicated that these stages are the best period to identify the spatial variability caused by the nutrient stress in the corn field. The derived vegetation indices were also used to build yield prediction models via the linear regression method. At that point, all of the yield prediction models were evaluated by comparing the R2-value and the best index model from each day's image was picked based on the highest R 2-value. It was shown that the green normalized difference vegetation (GNDVI) based model is more sensitive to yield prediction than other indices-based models. During the VT-R4 stages, the GNDVI based models were able to explain more than 95% potential corn yield consistently for both seasons. The VT-R4 stages are the best period of time to estimate the corn yield. The SICMS system is only suitable for the RS research at a fixed location. In order to provide more flexibility of the RS image collection, a novel UAV based system has been studied. The UAV based agricultural RS system used a light helicopter platform equipped with a multi-spectral camera. The UAV control system consisted of an on-board and a ground station subsystem. For the on-board subsystem, an Extended Kalman Filter (EKF) based UAV navigation system was designed and implemented. The navigation system, using low cost inertial sensors, magnetometer, GPS and a single board computer, was capable of providing continuous estimates of UAV position and attitude at 50 Hz using sensor fusion techniques. The ground station subsystem was designed to be an interface between a human operator and the UAV to implement mission planning, flight command activation, and real-time flight monitoring. The navigation system is controlled by the ground station, and able to navigate the UAV in the air to reach the predefined waypoints and trigger the multi-spectral camera. By so doing, the aerial images at each point could be captured automatically. The developed UAV RS system can provide a maximum flexibility in crop field RS image collection. It is essential to perform the geometric correction and the geocoding before an aerial image can be used for precision farming. An automatic (no Ground Control Point (GCP) needed) UAV image georeferencing algorithm was developed. This algorithm can do the automatic image correction and georeferencing based on the real-time navigation data and a camera lens distortion model. The accuracy of the georeferencing algorithm was better than 90 cm according to a series test. The accuracy that has been achieved indicates that, not only is the position solution good, but the attitude error is extremely small. The waypoints planning for UAV flight was investigated. It suggested that a 16.5% forward overlap and a 15% lateral overlap were required to avoi

Xiang, Haitao

365

Exopop: Exoplanet population inference

NASA Astrophysics Data System (ADS)

Exopop is a general hierarchical probabilistic framework for making justified inferences about the population of exoplanets. Written in python, it requires that the occurrence rate density be a smooth function of period and radius (employing a Gaussian process) and takes survey completeness and observational uncertainties into account. Exopop produces more accurate estimates of the whole population than standard procedures based on weighting by inverse detection efficiency.

Foreman-Mackey, Daniel

2014-07-01

366

The authors an uncertainty analysis of data taken using the Rossi technique, in which the horizontal oscilloscope sweep is driven sinusoidally in time ,while the vertical axis follows the signal amplitude. The analysis is done within a Bayesian framework. Complete inferences are obtained by tilting the Markov chain Monte Carlo technique, which produces random samples from the posterior probability distribution expressed in terms of the parameters.

KENNETH M. HANSON; JANE M. BOOKER

2000-09-08

367

NASA Astrophysics Data System (ADS)

The Muglad rift basin of Sudan, is a good example of polyphase rifting, with at least three major phases of basin development. Each phase has resulted in the generation of source rock, reservoir and seal geology with structural traps often closely linked to basement highs. In this paper we investigate on a regional scale the tectonic processes that have contributed to rift basin development. On a regional scale, the evolution of the Africa-wide Mesozoic rift system is intimately linked to relative movements of African sub-plates and to global plate tectonic processes and plate interactions. Changes in plate interactions are observed in the oceanic crust as azimuth changes of fracture zone geometries and by inference have caused significant modifications to both the orientation and magnitude of the motions of the African sub-plates. Such plate motion processes have controlled the polyphase development of the West and Central African Rift System. On the basinal scale, changes of sub-plate motions have resulted in changes in the stress field which have had a clear impact on the deformation and fault geometries of rift basins and on the resulting stratigraphy. The construction of the first unified stratigraphic chart for the West and Central African Rift System shows a close correlation in the timing of the major unconformities with the timing of changes in relative plate motion as observed in the changes of the azimuthal geometry of the oceanic fracture zones in the Central Atlantic. Since similarly timed unconformities exist along the continental margins of Africa and South America, we propose that the causative mechanism is change in relative plate motion which leads to an increase or decrease in the tension on the plate and thus controls the strength or effective elastic thickness, Te, of the crust/plate beneath the margins. This results in a focused change in isostatic response of the margin during short-period changes in relative plate motion; i.e. more tension will mean that loads are not compensated locally resulting in local uplift of the margin.

Fairhead, J. D.; Green, C. M.; Masterton, S. M.; Guiraud, R.

2013-05-01

368

Matrix Factorization for Transcriptional Regulatory Network Inference

Inference of Transcriptional Regulatory Networks (TRNs) provides insight into the mechanisms driving biological systems, especially mammalian development and disease. Many techniques have been developed for TRN estimation from indirect biochemical measurements. Although successful when initially tested in model organisms, these regulatory models often fail when applied to data from multicellular organisms where multiple regulation and gene reuse increase dramatically. Non-negative matrix factorization techniques were initially introduced to find non-orthogonal patterns in data, making them ideal techniques for inference in cases of multiple regulation. We review these techniques and their application to TRN analysis.

Ochs, Michael F.; Fertig, Elana J.

2013-01-01

369

Computational Inference of Neural Information Flow Networks

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks. PMID:17121460

Smulders, Tom V; Hartemink, Alexander J; Jarvis, Erich D

2006-01-01

370

Reliability of the Granger causality inference

NASA Astrophysics Data System (ADS)

How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result.

Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David

2014-04-01

371

Modeling & Inference Low-level Context

;...... ... ... ...... ... [1] B. Neumann, A Conceptual Framework for High-Level Vision, Bericht, FB Informatik, FBI-HH-B245 for diagnosis and user profiling," Technical Report CIA-RI-043, Center for Artificial Intelligence, ITESM Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA, 1988. [5

Cho, Sung-Bae

372

Structure Inference for Bayesian Multisensory Scene Understanding

We investigate a solution to the problem of multi-sensor scene understanding by formulating it in the framework of Bayesian model selection and structure inference. Humans robustly associate multimodal data as appropriate, but previous modelling work has focused largely on optimal fusion, leaving segregation unaccounted for and unexploited by machine perception systems. We illustrate a unifying, Bayesian solution to multi-sensor perception

Timothy M. Hospedales; Sethu Vijayakumar

2008-01-01

373

Recognising Textual Entailment with Robust Logical Inference

We use logical inference techniques for recognising textual entailment, with theorem proving operating on deep semantic interpre- tations as the backbone of our system. However, the performance of theorem proving on its own turns out to be highly dependent on a wide range of background knowledge, which is not necessarily included in pub- lically available knowledge sources. Therefore, we achieve

Johan Bos; Katja Markert

2005-01-01

374

An Inference Network Approach to Image Retrieval

Most image retrieval systems only allow a fragment of text or an example image as a query. Most users have more complex infor- mation needs that are not easily expressed in either of these forms. This paper proposes a model based on the Inference Network framework from information retrieval that employs a powerful query language that allows structured query operators,

Donald Metzler; R. Manmatha

2004-01-01

375

Inferences of Ice Processes From Properties

Barclay Kamb's pioneering work on the physics and mineralogy of laboratory and natural ices has guided glaciological research spanning 40 years. Much of that research required extremely tedious use of optical universal stages to study thin sections of ice. Recent advances in digital systems have revolutionized data collection and offer great opportunities to use ice properties to infer processes that

R. B. Alley; L. A. Wilen; M. K. Spencer; D. P. Hansen; J. J. Fitzpatrick

2001-01-01

376

Fuzzy Economizer control using a Prolog-C inference engine

This research is in two parts: I. Develop a generic tool to perform fuzzy inference on a wide class of systems.Thisis done using Prolog and C. 2.Develop a hierarchical control scheme using this fuzzy inference mechanism tool for a constant volume...

Belur, Raghuveer R.

2012-06-07

377

Nonstandard Inferences in Description Logics: The Story So Far

Description logics (DLs) are a successful family of logic-based knowledge represen- tation formalisms that can be used to represent the terminological knowledge of an application domain in a structured and formally well-founded way. DL systems pro- vide their users with inference procedures that allow to reason about the represented knowledge. Standard inference problems (such as the subsumption and the instance

Franz Baader; Ralf Küsters

378

NASA Technical Reports Server (NTRS)

The goal of visual inference programming is to develop a software framework data analysis and to provide machine learning algorithms for inter-active data exploration and visualization. The topics include: 1) Intelligent Data Understanding (IDU) framework; 2) Challenge problems; 3) What's new here; 4) Framework features; 5) Wiring diagram; 6) Generated script; 7) Results of script; 8) Initial algorithms; 9) Independent Component Analysis for instrument diagnosis; 10) Output sensory mapping virtual joystick; 11) Output sensory mapping typing; 12) Closed-loop feedback mu-rhythm control; 13) Closed-loop training; 14) Data sources; and 15) Algorithms. This paper is in viewgraph form.

Wheeler, Kevin; Timucin, Dogan; Rabbette, Maura; Curry, Charles; Allan, Mark; Lvov, Nikolay; Clanton, Sam; Pilewskie, Peter

2002-01-01

379

Causal network inference using biochemical kinetics

Motivation: Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems. Results: We present a general framework for network inference and dynamical prediction using time course data that is rooted in non-linear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. This allows prediction of dynamical behavior even when the underlying reaction graph itself is unknown or uncertain. Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that non-linear formulations can yield gains in causal network inference and permit dynamical prediction and uncertainty quantification in the challenging setting where the reaction graph is unknown. Availability and implementation: MATLAB R2014a software is available to download from warwick.ac.uk/chrisoates. Contact: c.oates@warwick.ac.uk or sach@mrc-bsu.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161235

Oates, Chris J.; Dondelinger, Frank; Bayani, Nora; Korkola, James; Gray, Joe W.; Mukherjee, Sach

2014-01-01

380

Moment inference from tomograms

Time-lapse geophysical tomography can provide valuable qualitative insights into hydrologic transport phenomena associated with aquifer dynamics, tracer experiments, and engineered remediation. Increasingly, tomograms are used to infer the spatial and/or temporal moments of solute plumes; these moments provide quantitative information about transport processes (e.g., advection, dispersion, and rate-limited mass transfer) and controlling parameters (e.g., permeability, dispersivity, and rate coefficients). The reliability of moments calculated from tomograms is, however, poorly understood because classic approaches to image appraisal (e.g., the model resolution matrix) are not directly applicable to moment inference. Here, we present a semi-analytical approach to construct a moment resolution matrix based on (1) the classic model resolution matrix and (2) image reconstruction from orthogonal moments. Numerical results for radar and electrical-resistivity imaging of solute plumes demonstrate that moment values calculated from tomograms depend strongly on plume location within the tomogram, survey geometry, regularization criteria, and measurement error. Copyright 2007 by the American Geophysical Union.

Day-Lewis, F. D.; Chen, Y.; Singha, K.

2007-01-01

381

Probabilistic inference in human infants.

In this chapter, we review empirical evidence in support of infants' ability to make rudimentary probabilistic inferences. A recent surge of research in cognitive developmental psychology examines whether human learners, from infancy through adulthood, reason in ways consistent with Bayesian inference. However, when exploring this question an important first step is to identify the available inference mechanisms and computational machinery that might allow infants and young children to make inductive inferences. A number of recent studies have asked if infants may be "intuitive statisticians," making inferences about the relationship between samples and populations in both looking-time and choice tasks. Furthermore, infants make these inferences under a variety of sampling conditions and integrate prior domain knowledge into their probability calculations. The competences demonstrated in the reviewed experiments appear to draw on an intuitive probability notion that is early emerging and does not appear to be available for conscious reflection. PMID:23205407

Denison, Stephanie; Xu, Fei

2012-01-01

382

Composable Probabilistic Inference with Blaise

Probabilistic inference provides a unified, systematic framework for specifying and solving these problems. Recent work has demonstrated the great value of probabilistic models defined over complex, structured domains. ...

Bonawitz, Keith A

2008-07-23

383

Improving motor imagery classification with a new BCI design using neuro-fuzzy S-dFasArt.

This paper presents an algorithm based on neural networks and fuzzy theory (S-dFasArt) to classify spontaneous mental activities from electroencephalogram (EEG) signals, in order to operate a noninvasive brain-computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates. PMID:21997321

Cano-Izquierdo, Jose-Manuel; Ibarrola, Julio; Almonacid, Miguel

2012-01-01

384

use of an artificial earthquake. Next, performance of a fuzzy controller is validated by investigating time histories of the absolute acceleration response and the overall performance indices when the structure is subjected to a set of actual...

Likhitruangsilp, Visit

2012-06-07

385

Process scheduling with fuzzy inference models

NASA Astrophysics Data System (ADS)

This paper presents a model to treat the problem of process scheduling within a computer network using a fuzzy inference system. The scheduling system implemented, simulated in the Network Simulator, acts in two particularly points: first defining the discard priority for the applications according to its characteristics; and further, to redefine their transfer rate, also considering its particularities; thus providing fitting transfer rates for the applications and, with it, means for Quality of Service.

Cardoso, Diego L.; Santana, Ádamo L.; Francês, Carlos R.; Souza, Jorge A.; Costa, João W.

2007-09-01

386

Bayesian inference in geomagnetism

NASA Technical Reports Server (NTRS)

The inverse problem in empirical geomagnetic modeling is investigated, with critical examination of recently published studies. Particular attention is given to the use of Bayesian inference (BI) to select the damping parameter lambda in the uniqueness portion of the inverse problem. The mathematical bases of BI and stochastic inversion are explored, with consideration of bound-softening problems and resolution in linear Gaussian BI. The problem of estimating the radial magnetic field B(r) at the earth core-mantle boundary from surface and satellite measurements is then analyzed in detail, with specific attention to the selection of lambda in the studies of Gubbins (1983) and Gubbins and Bloxham (1985). It is argued that the selection method is inappropriate and leads to lambda values much larger than those that would result if a reasonable bound on the heat flow at the CMB were assumed.

Backus, George E.

1988-01-01

387

Causal Inference in Retrospective Studies.

ERIC Educational Resources Information Center

The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…

Holland, Paul W.; Rubin, Donald B.

1988-01-01

388

Empirical Inference ResearchOverview

into the underlying mechanisms, and make predictions about the effect of interventions). Likewise, the type). It has since broadened its set of inference tools to include a stronger component of Bayesian methods for empirical inference pertaining to our department's core interests. In cases where the appli- cation areas

389

Bayesian Inference for Linear Models

Bayesian Inference for Linear Models Maximum Likelihood Linear Models fMRI analysis Bayesian Linear Inference for Linear Models Maximum Likelihood Linear Models fMRI analysis Bayesian Linear Models fMRI Linear Models fMRI analysis Bayesian Linear Models fMRI example Augmented Form MAP Learning MEG Source

Penny, Will

390

Causal Inference and Developmental Psychology

ERIC Educational Resources Information Center

Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…

Foster, E. Michael

2010-01-01

391

ERACER: A Database Approach for Statistical Inference and Data Cleaning

SQL and user defined functions. The system performs the inference and cleansing tasks in an integrated cleaning for the purpose of maintaining quality in relational databases. Data cleaning (or cleansing it possible to automate the cleansing proc

Neville, Jennifer

392

Double jeopardy in inferring cognitive processes.

Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2 (n) . In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs. PMID:25374545

Fific, Mario

2014-01-01

393

Double jeopardy in inferring cognitive processes

Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2n. In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs. PMID:25374545

Fific, Mario

2014-01-01

394

HMF: simple type inference for first-class polymorphism

HMF is a conservative extension of Hindley-Milner type inference with first-class polymorphism. In contrast to other proposals, HML uses regular System F types and has a simple type inference algorithm that is just a small extension of the usual Damas-Milner algorithm W. Given the relative simplicity and expressive power, we feel that HMF can be an attractive type system in

Daan Leijen

2008-01-01

395

INFERRING THE ECCENTRICITY DISTRIBUTION

Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here, we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementation of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision-other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, distances, or photometric redshifts-so long as the measurements have been communicated as a likelihood function or a posterior sampling.

Hogg, David W.; Bovy, Jo [Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003 (United States); Myers, Adam D., E-mail: david.hogg@nyu.ed [Max-Planck-Institut fuer Astronomie, Koenigstuhl 17, D-69117 Heidelberg (Germany)

2010-12-20

396

The empirical accuracy of uncertain inference models

NASA Technical Reports Server (NTRS)

Uncertainty is a pervasive feature of the domains in which expert systems are designed to function. Research design to test uncertain inference methods for accuracy and robustness, in accordance with standard engineering practice is reviewed. Several studies were conducted to assess how well various methods perform on problems constructed so that correct answers are known, and to find out what underlying features of a problem cause strong or weak performance. For each method studied, situations were identified in which performance deteriorates dramatically. Over a broad range of problems, some well known methods do only about as well as a simple linear regression model, and often much worse than a simple independence probability model. The results indicate that some commercially available expert system shells should be used with caution, because the uncertain inference models that they implement can yield rather inaccurate results.

Vaughan, David S.; Yadrick, Robert M.; Perrin, Bruce M.; Wise, Ben P.

1987-01-01

397

Ensemble Inference and Inferability of Gene Regulatory Networks

The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference of GRNs. The ensemble corresponds to the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of gene knock-out (KO) experiments. We applied TRaCE to analyze the inferability of random GRNs and the GRNs of E. coli and yeast from single- and double-gene KO experiments. The results showed that, with the exception of networks with very few edges, GRNs are typically not inferable even when the data are ideal (unbiased and noise-free). Finally, we compared the performance of TRaCE with top performing methods of DREAM4 in silico network inference challenge. PMID:25093509

Ud-Dean, S. M. Minhaz; Gunawan, Rudiyanto

2014-01-01

398

Intersection Bounds: Estimation and Inference

We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or, equivalently, the value of a linear ...

Chernozhukov, Victor V.

399

Sequential Inference for Latent Force Models

Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulated and solved using the state variable approach. We shall also show how the Gaussian process prior used in LFMs can be equivalently formulated as a linear statespace model driven by a white noise process and how inference on the resulting model can be efficiently implemented using Kalman filter and smoother. Then we shall show how the recently proposed switching LFM can be reformulated using the state variable approach, and how we can construct a probabilistic model for the switches by formulating a similar switching LFM as a switching linear dynamic system (SLDS). We illustrate the performance of the proposed methodology in simulated scenarios and apply it to inferring the switching points in GPS data collected from car movement data in urban environment.

Hartikainen, Jouni

2012-01-01

400

Drug target inference through pathway analysis of genomics data.

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues in this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Ma, Haisu; Zhao, Hongyu

2013-06-30

401

FOWL: an Inference Engine for the Semantic Web 1

Understanding and using the data and knowledge encoded in seman- tic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology

Youyong Zou; Tim Finin; Harry Chen

402

Drug target inference through pathway analysis of genomics data

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues is this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Ma, Haisu; Zhao, Hongyu

2013-01-01

403

Multistability and Perceptual Inference

Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent ...

Gershman, Samuel J.

404

Open vehicular data interfaces for in-car context inference

In this paper, we present a concept for an open vehicular data interface and describe it's components and architecture. We discuss the enabled applications in the context of advanced driver assistance systems with a focus on humanmachine interfaces, vehicle-to-x (V2X) communication and context inference systems. We conclude by a presentation of the initial implementation and deployed system.

Matthias Kranz; Eduard Weber; Korbinian Frank; Daniel Hermosilla Galceran

2009-01-01

405

Inferring Network Topology from Complex Dynamics

Inferring network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method to infer the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is not restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstruct both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of substantial external noise.

Srinivas Gorur Shandilya; Marc Timme

2010-07-09

406

Record concatenation, multiple inheritance, and multiple-object cloning are closely related and part of various language designs. For example, in Cardelli's untyped Obliq language, a new object can be constructed from several existing objects by cloning followed by con- catenation; an error is given in case of eld name conicts. Type systems for record concatenation have been studied by Wand, Harper

Jens Palsberg; Tian Zhao; Purdue Universityy

407

Model Inference with Reference Priors

We describe the application of model inference based on reference priors to two concrete examples in high energy physics: the determination of the CKM matrix parameters rhobar and etabar and the determination of the parameters m_0 and m_1/2 in a simplified version of the CMSSM SUSY model. We show how a 1-dimensional reference posterior can be mapped to the n-dimensional (n-D) parameter space of the given class of models, under a minimal set of conditions on the n-D function. This reference-based function can be used as a prior for the next iteration of inference, using Bayes' theorem recursively.

Maurizio Pierini; Harrison Prosper; Sezen Sekmen; Maria Spiropulu

2011-07-14

408

Teaching Inference for Randomized Experiments

ERIC Educational Resources Information Center

Nearly all introductory statistics textbooks include a chapter on data collection methods that includes a detailed discussion of both random sampling methods and randomized experiments. But when statistical inference is introduced in subsequent chapters, its justification is nearly always based on principles of random sampling methods. From the…

Ernst, Michael D.

2009-01-01

409

Ancestral Inference in Population Genetics

Mitochondrial DNA sequence variation is now being used to study the history of our species. In this paper we discuss some aspects of estimation and inference that arise in the study of such variability, focusing in particular on the estimation of substitution rates and their use in calibrating estimates of the time since the most recent common ancestor of a

R. C. Griffiths; Simon Tavare

1994-01-01

410

Forensic Inference from DNA Fingerprints

The recent discovery of hypervariable regions of the human genome provides scientists with an important new tool for forensic inference. The DNA data obtained from these hypervariable regions have been dubbed DNA fingerprints. Despite the potential power of DNA fingerprints, their use has been fraught with controversy, deriving in part from a lack of statistical methods to summarize the information

B. Devlin; Neil Risch; Kathryn Roeder

1992-01-01

411

Motion Inference During +Gz Acceleration.

National Technical Information Service (NTIS)

In the combat setting there are times when the pilot's attention is drawn away from the target momentarily and then redirected back to the target. In this scenario the pilot must infer the target's new position based on information about its previous posi...

J. L. Tripp, R. A. McKinley, R. L. Esken

2006-01-01

412

Sample Size and Correlational Inference

ERIC Educational Resources Information Center

In 4 studies, the authors examined the hypothesis that the structure of the informational environment makes small samples more informative than large ones for drawing inferences about population correlations. The specific purpose of the studies was to test predictions arising from the signal detection simulations of R. B. Anderson, M. E. Doherty,…

Anderson, Richard B.; Doherty, Michael E.; Friedrich, Jeff C.

2008-01-01

413

A Thesis in Inductive Inference

Inductive inference is the theory of identifying recursive functions from examples. In [26], [27], [30] the following thesis was stated: Any class of recursive functions which is identifiable at all can always be identified by an enumeratively working strategy. Moreover, the identification can always be realized with respect to a suitable nonstandard (i.e. non-Gödel) numbering. We review some of the

Rolf Wiehagen

1990-01-01

414

How Forgetting Aids Heuristic Inference

ERIC Educational Resources Information Center

Some theorists, ranging from W. James (1890) to contemporary psychologists, have argued that forgetting is the key to proper functioning of memory. The authors elaborate on the notion of beneficial forgetting by proposing that loss of information aids inference heuristics that exploit mnemonic information. To this end, the authors bring together 2…

Schooler, Lael J.; Hertwig, Ralph

2005-01-01

415

Inferences on Small Area Proportions

SUMMARY Design-based methods are generally inefficient for making inferences about small area proportions for rare events. In this paper, we discuss an alternative hierarchical model and the associated hierarchical Bayes methodology. Sufficient conditions for propriety of the posterior distributions of relevant parameters are presented. PMID:24453380

Chen, Shijie; Lahiri, P.

2013-01-01

416

The mechanisms of temporal inference

NASA Technical Reports Server (NTRS)

The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space.

Fox, B. R.; Green, S. R.

1987-01-01

417

Inferring Music Selections for Casual Music Interaction Daniel Boland

-like interaction that spans from casual mood-setting through to explicit music selection. These systems embraceInferring Music Selections for Casual Music Interaction Daniel Boland University of Glasgow United music interaction systems developed for casual exploratory search. In casual search scenarios, users

Murray-Smith, Roderick

418

Inferring and Visualizing Social Networks on Internet Relay Cha

Internet Relay Chat is a system that allows groups of people to collaborate and chat from anywhere in the world. Clearly defined by several RFC documents, it is arguably the most standard real-time chat system currently in use. This paper describes a method of inferring the social network of a group of IRC users in a channel. An IRC bot

Paul Mutton

2004-01-01

419

Multimodal Inference for Driver-Vehicle Interaction Tevfik Metin Sezgin

a novel system for driver-vehicle interaction which combines speech recognition with facial- expression1 Multimodal Inference for Driver-Vehicle Interaction Tevfik Metin Sezgin Computer Laboratory recognition to increase intention recognition accuracy in the presence of engine- and road-noise. Our system

Robinson, Peter

420

Inference algorithms for gene networks: a statistical mechanics analysis

NASA Astrophysics Data System (ADS)

The inference of gene regulatory networks from high throughput gene expression data is one of the major challenges in systems biology. This paper aims at analysing and comparing two different algorithmic approaches. The first approach uses pairwise correlations between regulated and regulating genes; the second one uses message-passing techniques for inferring activating and inhibiting regulatory interactions. The performance of these two algorithms can be analysed theoretically on well-defined test sets, using tools from the statistical physics of disordered systems like the replica method. We find that the second algorithm outperforms the first one since it takes into account collective effects of multiple regulators.

Braunstein, A.; Pagnani, A.; Weigt, M.; Zecchina, R.

2008-12-01

421

Inference and Uncertainty from Models and Multiple Observations (Invited)

NASA Astrophysics Data System (ADS)

Model-supported inference with uncertainty generally involves examples of observations from some system of interest and a model of that system, for example, Earth-system observations and climate models. Comparison between the model and the observations allow inferences to be made about various aspects of the model and system. Observation error is always a source of uncertainty, and additional uncertainty comes from two sources related to the model: unknown settings of parameters controlling the model behavior, and an unknown discrepancy between the model and the system. There are several sorts of inference that can be targeted in this framework, but from the viewpoint of uncertainty quantification they involve construction of a statistical model that relates these three sources of uncertainty to produce an estimate involving each. These can then be used directly, such as in examining the parameter uncertainty or model discrepancy, or employed in further inferences such as sensitivity analysis or analysis of uncertainty in model-based predictions of the system. This talk examines the combination of evidence in the common case that the system observations move beyond assumptions of simple error models. This is typically the case when comparing varied observations to domains of model response that have distinct structural error. The talk motivates a hierarchical model on parameter settings corresponding to observation domains, and the implications to projected uncertainty. This is discussed in two cases of model analysis: one in model parameter estimation, and another in the assessment of climate sensitivity.

Gattiker, J.

2013-12-01

422

Several generations of the ancestral Pee Dee River system have been mapped beneath the South Carolina Grand Strand coastline and adjacent Long Bay inner shelf. Deep boreholes onshore and high-resolution seismic-reflection data offshore allow for reconstruction of these paleochannels, which formed during glacial lowstands, when the Pee Dee River system incised subaerially exposed coastal-plain and continental-shelf strata. Paleochannel groups, representing different generations of the system, decrease in age to the southwest, where the modern Pee Dee River merges with several coastal-plain tributaries at Winyah Bay, the southern terminus of Long Bay. Positions of the successive generational groups record a regional, southwestward migration of the river system that may have initiated during the late Pliocene. The migration was primarily driven by barrier-island deposition, resulting from the interaction of fluvial and shoreline processes during eustatic highstands. Structurally driven, subsurface paleotopography associated with the Mid-Carolina Platform High has also indirectly assisted in forcing this migration. These results provide a better understanding of the evolution of the region and help explain the lack of mobile sediment on the Long Bay inner shelf. Migration of the river system caused a profound change in sediment supply during the late Pleistocene. The abundant fluvial source that once fed sand-rich barrier islands was cut off and replaced with a limited source, supplied by erosion and reworking of former coastal deposits exposed at the shore and on the inner shelf. ?? 2006 Geological Society of America.

Baldwin, W. E.; Morton, R. A.; Putney, T. R.; Katuna, M. P.; Harris, M. S.; Gayes, P. T.; Driscoll, N. W.; Denny, J. F.; Schwab, W. C.

2006-01-01

423

Regular Grammatical Inference: A Genetic Algorithm Approach

Grammatical inference is the problem of inferring a grammar, given a set of positive samples which the inferred grammar should\\u000a accept and a set of negative samples which the grammar should not accept. Here we apply genetic algorithm for inferring regular\\u000a languages. The genetic search is started from maximal canonical automaton built from structurally complete sample. In view\\u000a of limiting

Pravin Pawar; G. Nagaraja

2002-01-01

424

Lifted First-Order Probabilistic Inference

Most probabilistic inference algorithms are speci- fied and processed on a propositional level. In the last decade, many proposals for algorithms accept- ing first-order specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. (Poole, 2003) presented a method to perform inference di- rectly on the first-order level, but this method is

Rodrigo De Salvo Braz; Eyal Amir; Dan Roth

2005-01-01

425

Quantum inference on Bayesian networks

NASA Astrophysics Data System (ADS)

Performing exact inference on Bayesian networks is known to be #P-hard. Typically approximate inference techniques are used instead to sample from the distribution on query variables given the values e of evidence variables. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time O (nmP(e)-1), depending critically on P (e), the probability that the evidence might occur in the first place. By implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking O (n2mP(e)-1/2) time per sample. We exploit the Bayesian network's graph structure to efficiently construct a quantum state, a q-sample, representing the intended classical distribution, and also to efficiently apply amplitude amplification, the source of our speedup. Thus, our speedup is notable as it is unrelativized—we count primitive operations and require no blackbox oracle queries.

Low, Guang Hao; Yoder, Theodore J.; Chuang, Isaac L.

2014-06-01

426

Quantifying the multi-scale performance of network inference algorithms.

Abstract Graphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the "wisdom of crowds" network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales. PMID:25153244

Oates, Chris J; Amos, Richard; Spencer, Simon E F

2014-10-01

427

An introduction to causal inference.

This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. PMID:20305706

Pearl, Judea

2010-01-01

428

Quaternary volcanic unrest has provided heat for episodic hydrothermal circulation in the Long Valley caldera, including the present-day hydrothermal system, which has been active over the past 40 kyr. The most recent period of crustal unrest in this region of east-central California began around 1980 and has included periods of intense seismicity and ground deformation. Uplift totaling more than 0.7

Christopher D. Farrar; Michael L. Sorey; Evelyn Roeloffs; Devin L. Galloway; James F. Howle; Ronald Jacobson

2003-01-01

429

NASA Technical Reports Server (NTRS)

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

2012-01-01

430

Species lists have been collected for forested and related scrubland areas throughout Canterbury. The total number of species involved is about 350. The patterns of distributions reflect the influence of one or a combination of several of the following factors: 1. The Pleistocene glaciation and subsequent climate amelioration. 2. The differential migration potential and length of life of beech trees and podocarp/hardwood species. 3. Climate deterioration within the last millenillm. 4. Widespread fires from 500 to 800 years ago. 5. The existing climate, especially effective rainfall, with relation to topography and degree of exposure. 6. The advent of Europeans. Felling and burning. The effects of grazing animals. Distribution patterns influenced by the post-Pleistocene mild climate are found in the presence of mosaics of podocarp and broadleaved hardwood tree species and their associated herbs, vines, shrubs and ferns throughout lowland Canterbury.. These inclu&: PTERIDOPHYTES. Mecodiul1I sanguinolentum, '~M. rarum, tM. villosum, M. fiabellatum. *M. flexlwsum, *Meryngillm multifidum,

C. J. Burrows

431

Quaternary volcanic unrest has provided heat for episodic hydrothermal circulation in the Long Valley caldera, including the present-day hydrothermal system, which has been active over the past 40 kyr. The most recent period of crustal unrest in this region of east-central California began around 1980 and has included periods of intense seismicity and ground deformation. Uplift totaling more than 0.7 m has been centered on the caldera's resurgent dome, and is best modeled by a near-vertical ellipsoidal source centered at depths of 6-7 km. Modeling of both deformation and microgravity data now suggests that (1) there are two inflation sources beneath the caldera, a shallower source 7-10 km beneath the resurgent dome and a deeper source ???15 km beneath the caldera's south moat and (2) the shallower source may contain components of magmatic brine and gas. The Long Valley Exploration Well (LVEW), completed in 1998 on the resurgent dome, penetrates to a depth of 3 km directly above this shallower source, but bottoms in a zone of 100??C fluid with zero vertical thermal gradient. Although these results preclude extrapolations of temperatures at depths below 3 km, other information obtained from flow tests and fluid sampling at this well indicates the presence of magmatic volatiles and fault-related permeability within the metamorphic basement rocks underlying the volcanic fill. In this paper, we present recently acquired data from LVEW and compare them with information from other drill holes and thermal springs in Long Valley to delineate the likely flow paths and fluid system properties under the resurgent dome. Additional information from mineralogical assemblages in core obtained from fracture zones in LVEW documents a previous period of more vigorous and energetic fluid circulation beneath the resurgent dome. Although this system apparently died off as a result of mineral deposition and cooling (and/or deepening) of magmatic heat sources, flow testing and tidal analyses of LVEW water level data show that relatively high permeability and strain sensitivity still exist in the steeply dipping principal fracture zone penetrated at a depth of 2.6 km. The hydraulic properties of this zone would allow a pressure change induced at distances of several kilometers below the well to be observable within a matter of days. This indicates that continuous fluid pressure monitoring in the well could provide direct evidence of future intrusions of magma or high-temperature fluids at depths of 5-7 km. ?? 2003 Elsevier B.V. All rights reserved.

Farrar, C. D.; Sorey, M. L.; Roeloffs, E.; Galloway, D. L.; Howle, J. F.; Jacobson, R.

2003-01-01

432

NASA Astrophysics Data System (ADS)

Quaternary volcanic unrest has provided heat for episodic hydrothermal circulation in the Long Valley caldera, including the present-day hydrothermal system, which has been active over the past 40 kyr. The most recent period of crustal unrest in this region of east-central California began around 1980 and has included periods of intense seismicity and ground deformation. Uplift totaling more than 0.7 m has been centered on the caldera's resurgent dome, and is best modeled by a near-vertical ellipsoidal source centered at depths of 6-7 km. Modeling of both deformation and microgravity data now suggests that (1) there are two inflation sources beneath the caldera, a shallower source 7-10 km beneath the resurgent dome and a deeper source ˜15 km beneath the caldera's south moat and (2) the shallower source may contain components of magmatic brine and gas. The Long Valley Exploration Well (LVEW), completed in 1998 on the resurgent dome, penetrates to a depth of 3 km directly above this shallower source, but bottoms in a zone of 100°C fluid with zero vertical thermal gradient. Although these results preclude extrapolations of temperatures at depths below 3 km, other information obtained from flow tests and fluid sampling at this well indicates the presence of magmatic volatiles and fault-related permeability within the metamorphic basement rocks underlying the volcanic fill. In this paper, we present recently acquired data from LVEW and compare them with information from other drill holes and thermal springs in Long Valley to delineate the likely flow paths and fluid system properties under the resurgent dome. Additional information from mineralogical assemblages in core obtained from fracture zones in LVEW documents a previous period of more vigorous and energetic fluid circulation beneath the resurgent dome. Although this system apparently died off as a result of mineral deposition and cooling (and/or deepening) of magmatic heat sources, flow testing and tidal analyses of LVEW water level data show that relatively high permeability and strain sensitivity still exist in the steeply dipping principal fracture zone penetrated at a depth of 2.6 km. The hydraulic properties of this zone would allow a pressure change induced at distances of several kilometers below the well to be observable within a matter of days. This indicates that continuous fluid pressure monitoring in the well could provide direct evidence of future intrusions of magma or high-temperature fluids at depths of 5-7 km.

Farrar, Christopher D.; Sorey, Michael L.; Roeloffs, Evelyn; Galloway, Devin L.; Howle, James F.; Jacobson, Ronald

2003-10-01

433

NASA Astrophysics Data System (ADS)

As frequently observed in closed-conduit volcanoes, the fumarolic field of La Fossa crater at Vulcano Islands experiences episodes of rapid and remarkable changes in its geochemical features (referred as "crises") that normally last no more than a few months. Well documented episodes in literature occurred in 1988 and 1996, characterized by increase in the outlet temperature and steam output from fumarolic vents, marked variations in thermal groundwaters and diffuse CO2 emissions from soils, nevertheless their meaning remains widely debated. Here we report the chemical and isotopic (C, H, O, and He) compositions of the fumarolic fluids from La Fossa crater in the period 1999-2010. Consistent with records above, our data show that the geochemical features of the fumarole system have experienced several "crises" occurred from November 2004 to January 2005, from October 2005 to January 2006, and from October to November 2009, each lasting no more than a few months. Typical signatures of these short-term anomalies are large increments in CO2, N2, and He concentrations, coupled to increased 13C/12C isotopic ratios. Within a model of fumarolic fluids based on mixing between hydrothermal and magmatic endmembers, we have developed a novel approach to constrain chemical (He/CO2 and N2/He) and isotopic (13C/12C, D/H, and 3He/4He) ratios of the magmatic endmember during the short-term anomalies. Although much of the geochemical variability in fumaroles results from changes in mixing proportions, the magmatic fluid unquestionably shows significant variations in time. The magmatic He/CO2, N2/He, 13C/12C, and 3He/4He values throughout 1988-1996 differed from those feeding the anomaly at the end of 2004. Early clues of the new magmatic fluid appeared in 1998-1999, far from any short-term anomaly, whereas new and old magmatic fluids coexisted after 2004. We quantitatively prove that the detected geochemical changes are consistent with the degassing path of a magma having a latitic composition, and suggest the presence of two magma ponding levels at slightly different pressures, where bubble-melt decoupling can occur. The different He-isotope compositions at these levels suggest low hydraulic connectivity typical of a complex reservoir with dike and sill structures. In this framework, the so-called crises at the fumaroles are probably due to the evolving conditions in the magmatic system, such as gas buildup at the top of magma batches followed by massive discharge, activation of new degassing levels due to reorganization of the magma system, and its interplay with the stress field. These processes probably start years before a crisis. Far from crises, geochemical variations with specific signatures can suggest the onset of changes and reorganization in the magma system, and hence these phases that are apparently "not anomalous" should be evaluated for their implications in volcanic surveillance. Such a scenario explains the observed increases in both fumarole output and shallow high-frequency seismicity (due to increased pore pressure) during the anomalies, while being consistent with the concomitant absence of any deep seismicity or ground deformation.

Paonita, Antonio; Federico, Cinzia; Bonfanti, Piero; Capasso, Giorgio; Inguaggiato, Salvatore; Italiano, Franco; Madonia, Paolo; Pecoraino, Guendalina; Sortino, Francesco

2013-04-01

434

In pharmacy, racemic compounds are often problematic, because generally only one of the enantiomers possesses therapeutic activity and it is often difficult to separate them. Even though this problem is likely as old as the pharmaceutical industry, one thermodynamically obvious way of separating racemic crystals has never been studied experimentally, which is by using pressure. Data have been obtained on the equilibria of the (R)- and (S)-mandelic acid system as a function of pressure and temperature. With the use of thermodynamic arguments including the Clapeyron, Schröder, and Prigogine-Defay equations, it has been demonstrated that the conglomerate (crystals of separated enantiomers) becomes more stable than the racemic compound at approximately 0.64 GPa and 460 K. Even though this pressure is still higher than at the bottom of the Mariana Trench, there are no technical obstacles to produce such conditions, making pressure a viable option for separating enantiomers. PMID:22047025

Rietveld, Ivo B; Barrio, Maria; Tamarit, Josep-Lluis; Do, Bernard; Céolin, René

2011-12-15

435

Larval dispersal is a crucial factor for fish recruitment. For fishes with relatively small-bodied larvae, drift has the potential to play a more important role than active habitat selection in determining larval dispersal; therefore, we expect small-bodied fish larvae to be poorly associated with habitat characteristics. To test this hypothesis, we used as model yellow perch (Perca flavescens), whose larvae are among the smallest among freshwater temperate fishes. Thus, we analysed the habitat association of yellow perch larvae at multiple spatial scales in a large shallow fluvial lake by explicitly modelling directional (e.g. due to water currents) and non-directional (e.g. due to aggregation) spatial patterns. This allowed us to indirectly assess the relative roles of drift (directional process) and potential habitat choice on larval dispersal. Our results give weak support to the drift hypothesis, whereas yellow perch show a strong habitat association at unexpectedly small sizes, when compared to other systems. We found consistent non-directional patterns in larvae distributions at both broad and medium spatial scales but only few significant directional components. The environmental variables alone (e.g. vegetation) generally explained a significant and biologically relevant fraction of the variation in fish larvae distribution data. These results suggest that (i) drift plays a minor role in this shallow system, (ii) larvae display spatial patterns that only partially covary with environmental variables, and (iii) larvae are associated to specific habitats. By suggesting that habitat association potentially includes an active choice component for yellow perch larvae, our results shed new light on the ecology of freshwater fish larvae and should help in building more realistic recruitment models. PMID:23185585

Bertolo, Andrea; Blanchet, F. Guillaume; Magnan, Pierre; Brodeur, Philippe; Mingelbier, Marc; Legendre, Pierre

2012-01-01

436

A search for short-lived {sup 10}Be in 21 calcium-aluminum-rich inclusions (CAIs) from Isheyevo, a rare CB/CH chondrite, showed that only 5 CAIs had {sup 10}B/{sup 11}B ratios higher than chondritic correlating with the elemental ratio {sup 9}Be/{sup 11}B, suggestive of in situ decay of this key short-lived radionuclide. The initial ({sup 10}Be/{sup 9}Be){sub 0} ratios vary between {approx}10{sup -3} and {approx}10{sup -2} for CAI 411. The initial ratio of CAI 411 is one order of magnitude higher than the highest ratio found in CV3 CAIs, suggesting that the more likely origin of CAI 411 {sup 10}Be is early solar system irradiation. The low ({sup 26}Al/{sup 27}Al){sub 0} [{<=} 8.9 Multiplication-Sign 10{sup -7}] with which CAI 411 formed indicates that it was exposed to gradual flares with a proton fluence of a few 10{sup 19} protons cm{sup -2}, during the earliest phases of the solar system, possibly the infrared class 0. The irradiation conditions for other CAIs are less well constrained, with calculated fluences ranging between a few 10{sup 19} and 10{sup 20} protons cm{sup -2}. The variable and extreme value of the initial {sup 10}Be/{sup 9}Be ratios in carbonaceous chondrite CAIs is the reflection of the variable and extreme magnetic activity in young stars observed in the X-ray domain.

Gounelle, Matthieu [Laboratoire de Mineralogie et de Cosmochimie du Museum, CNRS and Museum National d'Histoire Naturelle, UMR 7202, CP52, 57 rue Cuvier, F-75005 Paris (France); Chaussidon, Marc; Rollion-Bard, Claire, E-mail: gounelle@mnhn.fr [Centre de Recherches Petrographiques et Geochimiques, CRPG-CNRS, BP 20, F-54501 Vandoeuvre-les-Nancy Cedex (France)

2013-02-01

437

Larval dispersal is a crucial factor for fish recruitment. For fishes with relatively small-bodied larvae, drift has the potential to play a more important role than active habitat selection in determining larval dispersal; therefore, we expect small-bodied fish larvae to be poorly associated with habitat characteristics. To test this hypothesis, we used as model yellow perch (Perca flavescens), whose larvae are among the smallest among freshwater temperate fishes. Thus, we analysed the habitat association of yellow perch larvae at multiple spatial scales in a large shallow fluvial lake by explicitly modelling directional (e.g. due to water currents) and non-directional (e.g. due to aggregation) spatial patterns. This allowed us to indirectly assess the relative roles of drift (directional process) and potential habitat choice on larval dispersal. Our results give weak support to the drift hypothesis, whereas yellow perch show a strong habitat association at unexpectedly small sizes, when compared to other systems. We found consistent non-directional patterns in larvae distributions at both broad and medium spatial scales but only few significant directional components. The environmental variables alone (e.g. vegetation) generally explained a significant and biologically relevant fraction of the variation in fish larvae distribution data. These results suggest that (i) drift plays a minor role in this shallow system, (ii) larvae display spatial patterns that only partially covary with environmental variables, and (iii) larvae are associated to specific habitats. By suggesting that habitat association potentially includes an active choice component for yellow perch larvae, our results shed new light on the ecology of freshwater fish larvae and should help in building more realistic recruitment models. PMID:23185585

Bertolo, Andrea; Blanchet, F Guillaume; Magnan, Pierre; Brodeur, Philippe; Mingelbier, Marc; Legendre, Pierre

2012-01-01

438

Information Theory, Inference and Learning Algorithms

NASA Astrophysics Data System (ADS)

Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

Mackay, David J. C.

2003-10-01

439

Automated Generation of State Abstraction Functions using Data Invariant Inference

advantage of regular lan- guage mining algorithms, such as k-tail [2], or its variants [11], [10] to produce manual specification and maintenance effort, which is costly for systems that change frequently that automatically infer models from execution traces [2], [3], [11], [12]. FSM-based models can be mined by means

Harman, Mark

440

Multimodal Inference for Driver-Vehicle Interaction Tevfik Metin Sezgin

Multimodal Inference for Driver-Vehicle Interaction Tevfik Metin Sezgin College of Engineering KoÃ§ Cambridge, CB3 0FD, UK Peter.Robinson@cl.cam.ac.uk ABSTRACT In this paper we present a novel system for driver-vehicle interaction which combines speech recognition with facial- expression recognition

Sezgin, Metin

441

Iteratively Inferring Gene Regulatory Networks with Virtual Knockout Experiments

In this paper we address the problem of flnding gene regu- latory networks from experimental DNA microarray data. We introduce enhancements to an Evolutionary Algorithm optimization process to in- fer the parameters of the non-linear system given by the observed data more reliably and precisely. Due to the limited number of available data the inferring problem is under-determined and ambiguous.

Christian Spieth; Felix Streichert; Nora Speer; Andreas Zell

2004-01-01

442

KIT REPORT 111 Deriving Inference Rules for Description Logics

be investigated under different perspectives. The aim of this report is to provide the basis for a tighterKIT REPORT 111 Deriving Inference Rules for Description Logics: a Rewriting Approach into Sequent combination of theoretical investigations with issues arising in the actual implementation of DL systems. We

Wichmann, Felix

443

Bayesian multimodel inference for dose-response studies

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

Link, W.A.; Albers, P.H.

2007-01-01

444

Inferring Dynamic Credentials for R^ole-based Trust Management Daniele Gorla

Inferring Dynamic Credentials for R^ole-based Trust Management Daniele Gorla Dip. di Informatica is the r^ole-based trust-management language RT0, a formalism inspired by logic programming that handles Terms Languages, Security. Keywords trust-management, r^ole-based access control, infer- ence systems

Gorla, Daniele

445

NASA Astrophysics Data System (ADS)

Monogenetic basaltic cinder cones are the most abundant volcanic landform on Earth. While typically short-lived, cinder cone eruptions often display a range of eruption styles, including Strombolian, violent Strombolian, and even sub-Plinian activity. However, the processes driving explosive cinder cone eruptions remain poorly understood. In this study we investigate the volatile (H2O, CO2), major, and trace element chemistry of olivine-hosted melt inclusions from the tephra of 'Cinder Cone,' Lassen Volcanic NP, to better understand basaltic cinder cone eruptions and their underlying plumbing systems. Erupted in 1666 C.E., Cinder Cone is a young, un-vegetated cinder cone with well-preserved lava flows and tephra deposits. We have divided the tephra sequence, previously described by Heiken (1978) as Units 1, 2 and 3, into nine fall samples (LCC-9 through LCC-1). From these nine, we have obtained data from four tephra samples that span the sequence, one each corresponding with Units 1 and 3, and two from Unit 2. Olivine-hosted melt inclusions from the tephra at Cinder Cone trapped some of the most volatile-rich (1.7-3.4 wt% H2O, 530-1375 ppm CO2) and primitive (8.4-9.7 wt% MgO; olivine hosts Fo88-90) melts yet measured in the Cascade Arc (Ruscitto et al., 2010, EPSL). The melt inclusions, however, do not show evidence of the temporal changes in composition seen in whole rock and bulk tephra data that result from crustal contamination. Nearly all of the analyzed melt inclusions have lower SiO2 (50.4 wt.%) and higher TiO2 contents (0.8-0.95 wt%) than the whole-rock compositions (53-60 wt% and 0.5-0.85 wt.%, respectively). The range in H2O and CO2 concentrations likewise remains remarkably constant throughout the tephra deposit, with only the basal-most unit having a few higher H2O values. The differences between the whole-rock and melt inclusion compositions suggest that olivine crystallized from a primitive parental magma as it was rising to the surface, prior to the crustal contamination experienced by the magma near the surface. Additionally, the contamination must have occurred with little cooling of the magma, because cooling would have caused more olivine to form, with melt inclusions showing the effects of contamination and likely lower pressures. At Cinder Cone, we do not see any evidence for formation of a shallow magma reservoir beneath the volcano later in the eruption, as has been found at Jorullo and Parícutin in Mexico. Further detailed investigation of the Cinder Cone tephra sequence will better constrain our understanding of the underlying plumbing system.

Walowski, K. J.; Wallace, P. J.; Cashman, K. V.; Clynne, M. A.

2011-12-01

446

The establishment of a national control programme (NCP) in Uganda has led to routine treatment of intestinal schistosomiasis with praziquantel in the communities along Lake Albert. However, because regular water contact remains a way of life for these populations, re-infection continues to mitigate the sustainability of the chemotherapy-based programme. A six-month longitudinal study was conducted in one Lake Albert community with the aim of characterizing water contact exposure and infection among mothers and their young preschool-aged children as the latter are not yet formally included within the NCP. At baseline the cohort of 37 mothers, 36 preschool-aged children had infection prevalences of 62% and 67%, respectively, which diminished to 20% and 29%, respectively, at the 6-month post-treatment follow-up. The subjects wore global positioning system (GPS) datalogging devices over a 3-day period shortly after baseline, allowing for the estimation of time spent at the lakeshore as an exposure metric, which was found to be associated with prevalence at follow-up (OR = 2.1, P = 0.01 for both mothers and young children and odds ratio (OR) = 4.4, P = 0.01 for young children alone). A social network of interpersonal interactions was also derived from the GPS data, and the exposures were positively associated both with the number and duration of peer interaction, suggesting the importance of socio-cultural factors associated with water contact behaviour. The findings illustrate reduction in both prevalence and intensity of infection in this community after treatment as well as remarkably high rates of water contact exposure and re-infection, particularly among younger children. We believe that this should now be formally considered within NCP, which may benefit from more in-depth ethnographic exploration of factors related to water contact as this should provide new opportunities for sustaining control. PMID:23242675

Seto, Edmund Y W; Sousa-Figueiredo, José C; Betson, Martha; Byalero, Chris; Kabatereine, Narcis B; Stothard, J Russell

2012-11-01

447

The presence of two morphotypes of Arctic charr Salvelinus alpinus was confirmed via morphological variation and otolith strontium (Sr) within three open-lake systems of southern Baffin Island, Nunavut, Canada: Qinngu (LH001), Iqalugaarjuit Lake (PG082) and Qasigiat (PG015). Analysis of otolith Sr indicates that a component of each S. alpinus population within lakes LH001 and PG082 is migratory (large-maturing S. alpinus), whereas another component is lake-resident (small-maturing S. alpinus). Alternatively, small and large maturing S. alpinus may both inhabit tidal habitats during their lifetime in lake PG015. Three morphological characters were identified by principal factor analysis (PFA) as characters that were different between maturity groups for all lakes studied: eye diameter, pectoral fin length and pelvic fin length. As well, upper jaw length (LH001 and PG082) and fork depth (PG015) were identified in PFA as traits that differed between morphs. Univariate tests of morphological characters identified by PFA demonstrated maturity group differences with the exception of eye diameter in Lake PG015 and upper jaw length and pelvic fin length in lake LH001. No difference was found in the MANOVA test of upper and lower gill raker number between small-maturing and undeveloped fish within all lakes studied. Clear morphological variation observed between small-maturing and undeveloped fish in all three lakes of the study suggests ecological niche separation between morphotypes. This is the first documented case of lake-resident S. alpinus use of the tidal habitat in the presence of a migratory large-maturing morphotype. PMID:20738626

Loewen, T N; Gillis, D; Tallman, R F

2009-10-01

448

Dopamine, Affordance and Active Inference

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level. PMID:22241972

Friston, Karl J.; Shiner, Tamara; FitzGerald, Thomas; Galea, Joseph M.; Adams, Rick; Brown, Harriet; Dolan, Raymond J.; Moran, Rosalyn; Stephan, Klaas Enno; Bestmann, Sven

2012-01-01

449

Quantum Inference on Bayesian Networks

Performing exact inference on Bayesian networks is known to be #P-hard. Typically approximate inference techniques are used instead to sample from the distribution on query variables given the values $e$ of evidence variables. Classically, a single unbiased sample is obtained from a Bayesian network on $n$ variables with at most $m$ parents per node in time $\\mathcal{O}(nmP(e)^{-1})$, depending critically on $P(e)$, the probability the evidence might occur in the first place. By implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking $\\mathcal{O}(n2^mP(e)^{-\\frac12})$ time per sample. We exploit the Bayesian network's graph structure to efficiently construct a quantum state, a q-sample, representing the intended classical distribution, and also to efficiently apply amplitude amplification, the source of our speedup. Thus, our speedup is notable as it is unrelativized -- we count primitive operations and require no blackbox oracle queries.

Guang Hao Low; Theodore J. Yoder; Isaac L. Chuang

2014-02-28

450

NASA Astrophysics Data System (ADS)

In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Yilmaz, Isik; Kaynar, Oguz

2010-05-01

451

Artificial intelligent techniques for optimizing water allocation in a reservoir watershed

NASA Astrophysics Data System (ADS)

This study proposes a systematical water allocation scheme that integrates system analysis with artificial intelligence techniques for reservoir operation in consideration of the great uncertainty upon hydrometeorology for mitigating droughts impacts on public and irrigation sectors. The AI techniques mainly include a genetic algorithm and adaptive-network based fuzzy inference system (ANFIS). We first derive evaluation diagrams through systematic interactive evaluations on long-term hydrological data to provide a clear simulation perspective of all possible drought conditions tagged with their corresponding water shortages; then search the optimal reservoir operating histogram using genetic algorithm (GA) based on given demands and hydrological conditions that can be recognized as the optimal base of input-output training patterns for modelling; and finally build a suitable water allocation scheme through constructing an adaptive neuro-fuzzy inference system (ANFIS) model with a learning of the mechanism between designed inputs (water discount rates and hydrological conditions) and outputs (two scenarios: simulated and optimized water deficiency levels). The effectiveness of the proposed approach is tested on the operation of the Shihmen Reservoir in northern Taiwan for the first paddy crop in the study area to assess the water allocation mechanism during drought periods. We demonstrate that the proposed water allocation scheme significantly and substantially avails water managers of reliably determining a suitable discount rate on water supply for both irrigation and public sectors, and thus can reduce the drought risk and the compensation amount induced by making restrictions on agricultural use water.

Chang, Fi-John; Chang, Li-Chiu; Wang, Yu-Chung

2014-05-01

452

Inferences about Action Engage Action Systems

ERIC Educational Resources Information Center

Verbal descriptions of actions activate compatible motor responses [Glenberg, A. M., & Kaschak, M. P. (2002). Grounding language in action. "Psychonomic Bulletin & Review, 9", 558-565]. Previous studies have found that the motor processes for manual rotation are engaged in a direction-specific manner when a verb disambiguates the direction of…

Taylor, Lawrence J.; Lev-Ari, Shiri; Zwaan, Rolf A.

2008-01-01

453

A Lego System for Conditional Inference

Conditioning on the observed data is an important and exible design principle for statistical test procedures. Although generally applicable, permutation tests currently in use are limited to the treatment of special cases, such as contingency tables or K-sample problems. A new theoret- ical framework for permutation tests opens up the way to a unied and generalized view. We argue that

Torsten Hothorn; Kurt Hornik; Mark A. van de Wiel; Achim Zeileis

2006-01-01

454

18.441 Statistical Inference, Spring 2002

Reviews probability and introduces statistical inference. Point and interval estimation. The maximum likelihood method. Hypothesis testing. Likelihood-ratio tests and Bayesian methods. Nonparametric methods. Analysis of ...

Hardy, Michael

455

Statistical Inference, Distinguishability of Quantum States, And Quantum Entanglement

We argue from the point of view of statistical inference that the quantum relative entropy is a good measure for distinguishing between two quantum states (or two classes of quantum states) described by density matrices. We extend this notion to describe the amount of entanglement between two quantum systems from a statistical point of view. Our measure is independent of the number of entangled systems and their dimensionality.

V. Vedral; M. B. Plenio; K. Jacobs; P. L. Knight

1997-03-15

456

Modification of Hazen's equation in coarse grained soils by soft computing techniques

NASA Astrophysics Data System (ADS)

Hazen first proposed a Relationship between coefficient of permeability (k) and effective grain size (d10) was first proposed by Hazen, and it was then extended by some other researchers. However many attempts were done for estimation of k, correlation coefficients (R2) of the models were generally lower than ~0.80 and whole grain size distribution curves were not included in the assessments. Soft computing techniques such as; artificial neural networks, fuzzy inference systems, genetic algorithms, etc. and their hybrids are now being successfully used as an alternative tool. In this study, use of some soft computing techniques such as Artificial Neural Networks (ANNs) (MLP, RBF, etc.) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for prediction of permeability of coarse grained soils was described, and Hazen's equation was then modificated. It was found that the soft computing models exhibited high performance in prediction of permeability coefficient. However four different kinds of ANN algorithms showed similar prediction performance, results of MLP was found to be relatively more accurate than RBF models. The most reliable prediction was obtained from ANFIS model.

Kaynar, Oguz; Yilmaz, Isik; Marschalko, Marian; Bednarik, Martin; Fojtova, Lucie

2013-04-01

457

inference Michael I. Jordan jordan@cs.berkeley.edu Division of Computer Science and Department of StatisticsJordan and Weiss: Graphical Models: Probabilistic inference 1 Graphical models: Probabilistic Hebrew University RUNNING HEAD: Probabilistic inference in graphical models Correspondence: Michael I

Jordan, Michael I.

458

Synaptic and nonsynaptic plasticity approximating probabilistic inference.

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert. PMID:24782758

Tully, Philip J; Hennig, Matthias H; Lansner, Anders

2014-01-01

459

Statistics and Causal Inference PAUL W. HOLLAND*

Statistics and Causal Inference PAUL W. HOLLAND* Problems involving causal inference have dogged conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about in the most unexpected places, for example, "If the statistics cannot relate cause and effect, they can

Fitelson, Branden

460

Forward and Backward Inference in Spatial Cognition

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of ‘lower-level’ computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus. PMID:24348230

Penny, Will D.; Zeidman, Peter; Burgess, Neil

2013-01-01

461

The Reasoning behind Informal Statistical Inference

ERIC Educational Resources Information Center

Informal statistical inference (ISI) has been a frequent focus of recent research in statistics education. Considering the role that context plays in developing ISI calls into question the need to be more explicit about the reasoning that underpins ISI. This paper uses educational literature on informal statistical inference and philosophical…

Makar, Katie; Bakker, Arthur; Ben-Zvi, Dani

2011-01-01

462

Computational Inference of Neural Information Flow Networks

Computational Inference of Neural Information Flow Networks V. Anne Smith1[Â¤ , Jing Yu1,2[Â¤ , Tom V, Hartemink AJ, Jarvis ED (2006) Computational inference of neural information flow networks. PLoS Comput Biol connectivity networks. But traffic congestion, power blackouts, packet routing, computation, and animal

Jarvis, Erich D.

463

Bayesian Density Estimation and Inference Using Mixtures

We describe and illu