Note: This page contains sample records for the topic neuro-fuzzy inference systems from Science.gov.
While these samples are representative of the content of Science.gov,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of Science.gov
to obtain the most current and comprehensive results.
Last update: August 15, 2014.
1

Seizure prediction using adaptive neuro-fuzzy inference system.  

PubMed

In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour. PMID:24110134

Rabbi, Ahmed F; Azinfar, Leila; Fazel-Rezai, Reza

2013-01-01

2

Genetic tracker with adaptive neuro-fuzzy inference system for multiple target tracking  

Microsoft Academic Search

In this paper, a genetic tracker with adaptive neuro-fuzzy inference system (GT-ANFIS) is presented for multiple target tracking (MTT). First, the data association problem, formulated as an N-dimensional assignment problem, is solved using the genetic algorithm (GA), and then the inaccuracies in the estimation are corrected by the adaptive neuro-fuzzy inference system (ANFIS). The performances of the GT-ANFIS, the joint

Ilke Turkmen; Kerim Guney

2008-01-01

3

Adaptive neuro-fuzzy inference system based maximum power point tracking of a solar PV module  

Microsoft Academic Search

This paper presents and analyses the operation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) based maximum power point tracker (MPPT) for solar photovoltaic (SPV) energy generation system. The MPPT works on the principle of adjusting the voltage of the solar PV modules by changing the duty ratio of the boost converter. The duty ratio of boost converter is calculated for

A. Iqbal; H. Abu-Rub; S. M. Ahmed

2010-01-01

4

A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system  

Microsoft Academic Search

Eighteen anthropometric measurements were taken in standing and sitting positions, from 387 subjects between 15 and 17 years old. “Adaptive Neuro-Fuzzy Inference System (ANFIS)” was used to estimate anthropometric measurements as an alternative to stepwise regression analysis. Six outputs (shoulder width, hip width, knee height, buttock-popliteal height, popliteal height, and height) were selected for estimation purpose. The results showed that

M. Dursun Kaya; A. Samet Hasiloglu; Mahmut Bayramoglu; Hakki Yesilyurt; A. Fahri Ozok

2003-01-01

5

Adaptive neuro-fuzzy inference system for oscillometric blood pressure estimation  

Microsoft Academic Search

This paper presents a novel approach using principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS) for estimation of blood pressure (BP) from oscillometric waveforms. The proposed method consists of three stages. In the first stage, the oscillation amplitudes (OAs) of the oscillometric waveforms are represented as a function of the cuff pressure. In the second stage, the PCA

Mohamad Forouzanfar; Hilmi R. Dajani; Voicu Z. Groza; Miodrag Bolic; Sreeraman Rajan

2010-01-01

6

Adaptive Neuro-Fuzzy Inference System for Computing the Resonant Frequency of Circular Microstrip Antennas.  

National Technical Information Service (NTIS)

A new method for computing the resonant frequency of the circular microstrip antenna, based on the adaptive neuro-fuzzy inference system (ANFIS), is presented. A hybrid learning algorithm is used to identify the parameters of ANFIS. The results of the new...

K. Guney N. Sarikaya

2004-01-01

7

Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents  

Microsoft Academic Search

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart

Elif Derya Übeyli

2009-01-01

8

Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders  

Microsoft Academic Search

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is presented for detection of ophthalmic arterial (OA) disorders. Decision making was performed in two stages: feature extraction using the discrete wavelet transform (DWT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of OA Doppler signals

Elif Derya Übeyli

2008-01-01

9

Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals  

Microsoft Academic Search

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were

Elif Derya Übeyli; ?nan Güler

2005-01-01

10

Optimized Detection of Tar Content in the Manufacturing Process Using Adaptive Neuro-Fuzzy Inference Systems  

Microsoft Academic Search

The purpose of this study is to model and optimize the detection of tar in cigarettes during the manufacturing process and show that low yield cigarettes contain similar levels of nicotine as compared to high yield cigarettes while B (Benzene), T(toluene) and X (xylene) (BTX) levels increase with increasing tar yields. A neuro-fuzzy system which comprises a fuzzy inference structure

Zikrija Avdagic; Lejla Begic Fazlic; Samim Konjicija

2009-01-01

11

Battery state-of-charge (SOC) estimation using adaptive neuro-fuzzy inference system (ANFIS)  

Microsoft Academic Search

A battery is a quite complex and nonlinear system comprising interacting physical and chemical processes although it seems deceptively simple. State-of-charge (SOC), a parameter to describe how much energy battery has, is a key factor in battery management and its estimation is an important and challenging task. We develop an adaptive neuro-fuzzy inference system (ANFIS) to achieve the goal. First

C. H. Cai; D. Du; Z. Y. Liu

2003-01-01

12

Adaptive neuro-fuzzy inference system (ANFIS) digital predistorter for RF power amplifier linearization  

Microsoft Academic Search

This paper describes an adaptive digital predistorter (ADP) for RF power amplifier (PA) linearization using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS predistorter (PD) employs the advantage of real-time modeling of the PA's responses in determining the PD's functions. The amplitude and phase corrections for the PD are represented in an easy-to-understand fuzzy if-then rule, while the parameters involved

Kok Chew Lee; Peter Gardner

2006-01-01

13

Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems  

Microsoft Academic Search

A new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the ANFIS classifiers learned how to differentiate a new case in the domain. The six ANFIS classifiers were used to detect the six erythemato-squamous diseases when 34

Elif Derya Übeyl?; ?nan Güler

2005-01-01

14

Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition.  

PubMed

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

2014-01-01

15

Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition  

PubMed Central

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.

Subhi Al-batah, Mohammad; Mat Isa, Nor Ashidi; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

2014-01-01

16

Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes  

Microsoft Academic Search

The Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means “cessation of breath” during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. Decision making was

Elif Derya Übeyli; Dean Cvetkovic; Gerard Holland; Irena Cosic

2010-01-01

17

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System  

PubMed Central

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated.

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

18

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System.  

PubMed

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

19

A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system  

Microsoft Academic Search

This paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged\\/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output,

K. T. Chau; K. C. Wu; C. C. Chan

2004-01-01

20

Human action recognition using meta-cognitive neuro-fuzzy inference system.  

PubMed

We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort. PMID:23186277

Subramanian, K; Suresh, S

2012-12-01

21

Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system  

NASA Astrophysics Data System (ADS)

SummaryIn this paper, the methodology of using adaptive neuro-fuzzy inference systems (ANFIS) for flood quantile estimation at ungauged sites is presented. The proposed approach has the system identification and interpretability of fuzzy models and the learning capability of artificial neural networks (ANNs). The structure of the ANFIS is identified using the subtractive clustering algorithm. A hybrid learning algorithm consisting of back-propagation and least-squares estimation is used for system training. The ANFIS approach provides an integrated mechanism for identifying the hydrological regions, generating knowledge from the data, providing flood estimates and self-tuning to achieve the optimal performance. The proposed approach is applied to 151 catchments in the province of Quebec, Canada, and is compared to the ANN approach, the nonlinear regression (NLR) approach and the nonlinear regression with regionalization approach (NLR-R). A jackknife procedure is used for the evaluation of the performances of the three approaches. Results indicate that the ANFIS approach has a much better generalization capability than the NLR and NLR-R approaches and is comparable to the ANN approach.

Shu, C.; Ouarda, T. B. M. J.

2008-01-01

22

Diagnosis of renal failure disease using Adaptive Neuro-Fuzzy Inference System.  

PubMed

Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the useful and powerful neural network approaches for the solution of function approximation and pattern recognition problems in the last decades. In this paper, the diagnosis of renal failure disease is investigated using ANFIS approach. Totally the raw data of 112 patients is obtained from Istanbul and Cerrahpasa Medical Faculties of Istanbul University, Turkey. Sixty-four of them are related to renal failures and the rest data belong to healthy persons. In ANFIS model, three rules and Gaussian membership functions are chosen, where rules are determined by the subtractive clustering method. Seven parameters of the patients are considered for the input of the system. These are: Blood Urea Nitrogen (BUN), Creatinine, Uric Acid, Potassium (K), Calcium (Ca), Phosphorus (P) and age. We try to decide whether the patient is ill or not. We have reached 100% success in ANFIS and have better results compared to Support Vector Machine (SVM) and Artificial Neural Networks (ANN). PMID:20703607

Akgundogdu, Abdurrahim; Kurt, Serkan; Kilic, Niyazi; Ucan, Osman N; Akalin, Nilgun

2010-12-01

23

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters  

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

24

Modelling Dissolved Pollutants in Krishna River Using Adaptive Neuro Fuzzy Inference Systems  

NASA Astrophysics Data System (ADS)

Water quality models are used to describe the discharge concentration relationships in the river. Number of models exists to simulate the pollutant loads in a river, of which some of them are based on simple cause effect relationships and others on highly sophisticated physical and mathematical approaches that require extensive data inputs. Fuzzy rule based modeling extensively used in other disciplines, is attempted in the present study for modeling water quality with respect of dissolved pollutants in Krishna river flowing in Southern part of India. Adaptive Neuro Fuzzy Inference Systems (ANFIS), a recent development in the area of neuro-computing, based on the concept of fuzzy sets is used to model highly non-linear relationships and are capable of adaptive learning. This paper presents the results of the application of ANFIS for modeling dissolved pollutants in the Krishna River. The application and validation of the models is carried out using water quality and flow data obtained from the monitoring stations on the river. The results indicate that the models are quite successful in simulating the physical processes of the relationships between discharge and concentrations.

Matli, C. S.; Umamahesh, N. V.

2014-05-01

25

Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.  

PubMed

Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature ofAMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs. PMID:23193592

Fernandes, Fabiano C; Rigden, Daniel J; Franco, Octavio L

2012-01-01

26

Spring rainfall prediction based on remote linkage controlling using adaptive neuro-fuzzy inference system (ANFIS)  

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

27

Prediction of forced expiratory volume in spirometric pulmonary function test using adaptive neuro fuzzy inference system.  

PubMed

Spirometry is the most frequently performed clinical test to assess the dynamics of pulmonary function in human subjects. It measures airflow from fully inflated lungs through forced expiratory maneuver and generates large data set. However, these investigations often result in incomplete data sets due to the inability of the children and patients to perform this test. Hence, there is a requirement for prediction of significant parameters from the available incomplete data set. In this work, the results of model based prediction of two such significant parameters, Forced Expiratory Volume in one second (FEV1) and, Forced Expiratory Volume in six seconds (FEV6), are reported. The measured spirometric parameters are given as inputs to the Adaptive Neuro Fuzzy Inference System (ANFIS) which classifies data sets using fuzzy system based multilayer architecture. Triangular, Trapezoidal, Gaussian, Pi and Gbell membership functions are used to train and test the prediction process. The performance of the model is evaluated by computing their prediction error statistics of average value, standard deviation and root mean square. Results show that ANFIS model is capable of predicting FEV1 and FEV6 in both normal and abnormal subjects. Trapezoidal membership function predicted FEV1 with high precision and accuracy using a set of 21 rules. Similar prediction accuracy is observed in FEV6 using Gaussian membership function. Further, it is observed that prediction accuracy is found to be high for normal subjects with better correlation with measured values. It appears that this method is useful in enhancing diagnostic relevance of spirometric investigations in case of children and patients who are not able to perform the test as FEV1 and FEV6 are the useful indices to characterize pulmonary abnormalities. PMID:22846326

Mythili, A; Sujatha, C M; Srinivasan, S; Ramakrishnan, S

2012-01-01

28

Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists  

Microsoft Academic Search

This paper proposes a new method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate physicochemical descriptors of certain chemical compounds for their appropriate biological activities in terms of QSAR models with the aid of artificial neural network (ANN) approach combined with the principle of fuzzy logic. The ANFIS was utilized to predict NMDA (N-methyl-d-Aspartate) receptor binding activities of phencyclidine (PCP) derivatives.

Erdem Buyukbingol; Arzu Sisman; Murat Akyildiz; Ferda Nur Alparslan; Adeboye Adejare

2007-01-01

29

An application of adaptive neuro-fuzzy inference system to landslide susceptibility mapping (Klang valley, Malaysia)  

NASA Astrophysics Data System (ADS)

Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. Recently, the Klang Valley area of Selangor state has faced numerous landslide and mudflow events and much damage occurred in these areas. However, only little effort has been made to assess or predict these events which resulted in serious damages. Through scientific analyses of these landslides, one can assess and predict landslide-susceptible areas and even the events as such, and thus reduce landslide damages through proper preparation and/or mitigation. For this reason , the purpose of the present paper is to produce landslide susceptibility maps of a part of the Klang Valley areas in Malaysia by employing the results of the adaptive neuro-fuzzy inference system (ANFIS) analyses. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments and NDVI were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary land-use planning purposes. As a final conclusion, the results obtained from the study showed that the ANFIS modeling is a very useful and powerful tool for the regional landslide susceptibility assessments. However, the results to be obtained from the ANFIS modeling should be assessed carefully because the overlearning may cause misleading results. To prevent overlerning, the numbers of membership functions of inputs and the number of training epochs should be selected optimally and carefully.

Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan

2010-05-01

30

Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition.  

PubMed

The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs. PMID:24109851

Becerra, Miguel A; Orrego, Diana A; Delgado-Trejos, Edilson

2013-01-01

31

Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region  

NASA Astrophysics Data System (ADS)

Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ? 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

2012-04-01

32

Evaluation of the failure rates of transmission lines during hurricanes using a neuro-fuzzy system  

Microsoft Academic Search

This paper proposes a method to evaluate the impact of extreme weather on the failure rates of transmission lines. The method is based on a neuro-fuzzy system: adaptive neuro-fuzzy inference system (ANFIS). ANFIS is a popular neuro-fuzzy system and it has the configuration of an artificial neural network (ANN) and functions as a fuzzy inference system (FIS). Actually, ANFIS uses

Yong Liu; Chanan Singh

2010-01-01

33

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls  

PubMed Central

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.

Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

2014-01-01

34

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls.  

PubMed

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

35

Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning  

ERIC Educational Resources Information Center

With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy

Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun

2012-01-01

36

An adaptive neuro fuzzy power system stabilizer for damping inter-area oscillations in power systems  

Microsoft Academic Search

An adaptive neuro-fuzzy inference system (ANFIS) based PSS is proposed in this paper. The controller is essentially divided into two sub-systems, a recursive least square identifier for the generator and an adaptive neuro fuzzy PSS to damp the oscillations. The PSS is coupled to a single machine in every area and the parameters of this PSS are tuned online in

As. Venugopal; G. Radman; M. Abdelrahman

2004-01-01

37

Neuro-Fuzzy Based Modeling for Photovoltaic Power Supply System  

Microsoft Academic Search

Due to the increasing need for intelligent systems, the adaptive neuro-fuzzy inference system (ANFIS) has recently attracted the attention of researchers in various scientific and engineering areas. The purpose of this work is to present the modeling of a photovoltaic power supply (PVPS) system using an ANFIS. For the modeling of the PVPS system, it is required to find suitable

A. Mellit; S. A. Kalogirou

2006-01-01

38

Fuzzy logic and adaptive neuro-fuzzy inference system for characterization of contaminant exposure through selected biomarkers in African catfish.  

PubMed

This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies. PMID:22752811

Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C

2013-03-01

39

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system  

SciTech Connect

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems] [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering] [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control] [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)] [Electric Power Research Inst., Palo Alto, CA (United States)

1997-12-01

40

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process  

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

41

Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS  

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

42

Using adaptive neuro fuzzy inference system in developing an electrical arc furnace simulator  

Microsoft Academic Search

This paper presents the use of adaptive neurofuzzy inference systems (ANFIS) in simulating the regulator control loop of the electrical arc furnace (EAF). The regulator loop is the core part of steel making EAF, which controls positioning of the electrodes. The non-linearity and complexity of EAF makes it very difficult to use the classical mathematical modeling techniques in building the

F. Janabi-Sharifi; G. Jorjani; I. Hassanzadeh

2005-01-01

43

Adaptive neuro-fuzzy inference system (ANFIS): a new approach to predictive modeling in QSAR applications: a study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists.  

PubMed

This paper proposes a new method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate physicochemical descriptors of certain chemical compounds for their appropriate biological activities in terms of QSAR models with the aid of artificial neural network (ANN) approach combined with the principle of fuzzy logic. The ANFIS was utilized to predict NMDA (N-methyl-d-Aspartate) receptor binding activities of phencyclidine (PCP) derivatives. A data set of 38 drug-like compounds was coded with 1244 calculated molecular structure descriptors (clustered in 20 data sets) which were obtained from several sources, mainly from Dragon software. Prior to the progress to the ANFIS system, descriptors from the best subsets were selected using unsupervised forward selection (UFS) to eliminate redundancy and multicollinearity followed by fuzzy linear regression algorithm (FLR) which was used for variable selection. ANFIS was applied to train the final descriptors (Mor22m, E3s, R3v+, and R1e+) using a hybrid algorithm consisting of back-propagation and least-square estimation while the optimum number and shape of related functions were obtained through the subtractive clustering algorithm. Comparison of the proposed method with traditional methods, that is, multiple linear regression (MLR) and partial least-square (PLS) was also studied and the results indicated that the ANFIS model obtained from data sets achieved satisfactory accuracy. PMID:17434739

Buyukbingol, Erdem; Sisman, Arzu; Akyildiz, Murat; Alparslan, Ferda Nur; Adejare, Adeboye

2007-06-15

44

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department.  

PubMed

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

45

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

Microsoft Academic Search

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

46

Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness  

Microsoft Academic Search

A manufacturing system is oriented towards higher production rate, quality, and reduced cost and time to make a product. Surface\\u000a roughness is an index for determining the quality of machined products and is influenced by the cutting parameters. Surface\\u000a roughness prediction in machining is being attempted with many methodologies, yet there is a need to develop robust, autonomous\\u000a and accurate

S. Kumanan; C. P. Jesuthanam; R. Ashok Kumar

2008-01-01

47

Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis.  

PubMed

Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively. PMID:20703710

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, Wen-Ming; Li, R K; Wang, Tzu-Hao

2012-04-01

48

A QSAR study for modeling of 8-azaadenine analogues proposed as A1 adenosine receptor antagonists using genetic algorithm coupling adaptive neuro-fuzzy inference system (ANFIS).  

PubMed

A quantitative structure activity relationship (QSAR) study of 8-azaadenine, as antagonists for the A1 receptor, is described. A genetic algorithm (GA) method was used as the feature selection tool, and an adaptive neuro-fuzzy inference system (ANFIS) was employed for feature mapping. The best descriptors (GATS4v and BELv7) were applied to train the ANFIS model. The optimum number and shape of related functions were obtained through a subtractive clustering algorithm. The ability and robustness of the GA-ANFIS model in predicting the affinity of 8-azaadenine derivatives (pK(i)) are illustrated by validation techniques of Leave One Out, heuristic and randomized methods. The results have indicated that the proposed model of ANFIS in this work is superior over two other methods, radial basis function (RBF) and multiple linear regression (MLR). PMID:20702945

Afiuni-Zadeh, Somaieh; Azimi, Gholamhassan

2010-01-01

49

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages  

NASA Astrophysics Data System (ADS)

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

50

Skin Cancer Recognition by Using a Neuro-Fuzzy System  

PubMed Central

Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%.

Salah, Bareqa; Alshraideh, Mohammad; Beidas, Rasha; Hayajneh, Ferial

2011-01-01

51

Neuro-fuzzy approaches for identification and control of nonlinear systems  

Microsoft Academic Search

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

52

Bee Algorithm and Adaptive Neuro-Fuzzy Inference System as Tools for QSAR Study Toxicity of Substituted Benzenes to Tetrahymena pyriformis.  

PubMed

A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis. PMID:24638918

Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin

2014-06-01

53

An exploratory investigation of an adaptive neuro fuzzy inference system (ANFIS) for estimating hydrometeors from TRMM/TMI in synergy with TRMM/PR  

NASA Astrophysics Data System (ADS)

The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.

Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei; Gupta, Manika

2014-08-01

54

Prediction of effect of natural antioxidant compounds on hazelnut oil oxidation by adaptive neuro-fuzzy inference system and artificial neural network.  

PubMed

In this study, natural compounds including gallic acid, ellagic acid, quercetin, ?-carotene, and retinol were used as antioxidant agents in order to prevent and decrease oxidation in hazelnut oil. Quercetin showed the strongest antioxidative effect among the antioxidative agents, during storage. The accuracy of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models was studied to estimate the oil samples' peroxide value (PV), free fatty acid (FFA), and iodine values (IV). The root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R(2)) statistics were used to evaluate the models' accuracy. Comparison of the models showed that the ANFIS model performed better than the ANN and multiple linear regressions (MLR) models for estimating the PV, FFA, and IV. The values of R(2) and RMSE were found to be 0.9966 and 2.51, 0.6269 and 88.55, 0.5120 and 101.8 for the ANFIS, ANN, and MLR models for PV in testing period, respectively. The MLR was found to be insufficient for estimating various properties of the oil samples. PMID:22417373

Yalcin, Hasan; Ozturk, Ismet; Karaman, Safa; Kisi, Ozgur; Sagdic, Osman; Kayacier, Ahmed

2011-05-01

55

Quantitative structure-activity relationship analysis of human neutrophil elastase inhibitors using shuffling classification and regression trees and adaptive neuro-fuzzy inference systems.  

PubMed

The purpose of this study was to develop quantitative structure-activity relationship models for N-benzoylindazole derivatives as inhibitors of human neutrophil elastase. These models were developed with the aid of classification and regression trees (CART) and an adaptive neuro-fuzzy inference system (ANFIS) combined with a shuffling cross-validation technique using interpretable descriptors. More than one hundred meaningful descriptors, representing various structural characteristics for all 51 N-benzoylindazole derivatives in the data set, were calculated and used as the original variables for shuffling CART modelling. Five descriptors of average Wiener index, Kier benzene-likeliness index, subpolarity parameter, average shape profile index of order 2 and folding degree index selected by the shuffling CART technique have been used as inputs of the ANFIS for prediction of inhibition behaviour of N-benzoylindazole derivatives. The results of the developed shuffling CART-ANFIS model compared to other techniques, such as genetic algorithm (GA)-partial least square (PLS)-ANFIS and stepwise multiple linear regression (MLR)-ANFIS, are promising and descriptive. The satisfactory results r2p?=?0.845, Q2(LOO)?=?0.861, r2(L25%O)?=?0.829, RMSE(LOO) ?=?0.305 and RMSE(L25%O) ?=?0.336) demonstrate that shuffling CART-ANFIS models present the relationship between human neutrophil elastase inhibitor activity and molecular descriptors, and they yield predictions in excellent agreement with the experimental values. PMID:22452268

Asadollahi-Baboli, M

2012-07-01

56

Genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system modeling of antibacterial activity of annatto dye on Salmonella enteritidis.  

PubMed

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

57

A neuro-fuzzy approach to gear system monitoring  

Microsoft Academic Search

The detection of the onset of damage in gear systems is of great importance to industry. In this paper, a new neuro-fuzzy diagnostic system is developed, whereby the strengths of three ro- bust signal processing techniques are integrated. The adopted tech- niques are: the continuous wavelet transform (amplitude) and beta kurtosis based on the overall residual signal, and the phase

Wilson Wang; Fathy Ismail; M. Farid Golnaraghi

2004-01-01

58

Neuro-fuzzy system for chaotic time series forecasting  

NASA Astrophysics Data System (ADS)

We report on an on-going study to assess potential benefits using soft computing methods in forecasting problems. Our goal is to forecast natural phenomena represented by time series that show chaotic features. We use a neuro-fuzzy system for its ability to adapt to numerical data and for the possibility to input and extract expert knowledge expressed in words. We present results of experiments designed to study how to shape a neuro-fuzzy systems to forecast chaotic time series. Our main conclusions are: (1) The neuro-fuzzy system is able to forecast a synthetic chaotic time series with high accuracy if the number of inputs and the time delay between them are chosen adequately. (2) The Takens-Mane theorem from chaos theory gives a useful lower bound on the minimal number of inputs. (3) The time delay between the inputs can not be set a priori. It has to be tuned for every different times series. (4) The number of fuzzy rules seems related to the size of the learning set and not to the structure of the chaotic dynamical system. We tentatively try to interpret the rules that the neuro-fuzzy system has learned. Finally we discuss the adequacy of the whole set of fuzzy rules to forecast locally the dynamical system.

Masulli, Francesco; Studer, Leonard

1997-10-01

59

Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system  

NASA Astrophysics Data System (ADS)

The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.

Iphar, Melih; Yavuz, Mahmut; Ak, Hakan

2008-11-01

60

Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon.  

PubMed

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

61

Comparative structure-toxicity relationship study of substituted benzenes to Tetrahymena pyriformis using shuffling-adaptive neuro fuzzy inference system and artificial neural networks.  

PubMed

The purpose of this study was to develop the structure-toxicity relationships for a large group of 268 substituted benzene to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The shuffling-adaptive neuro fuzzy inference system (Shuffling-ANFIS) has been successfully applied to select the important factors affecting the toxicity of substituted benzenes to T. pyriformis. The results of the proposed model were compared with the model of linear-free energy response surface and also the principal component analysis Bayesian-regularized neural network (PCA-BRANN) trained using the same data. The presented model shows a better statistical parameter in comparison with the previous models. The results of the model are promising and descriptive. Five descriptors of octanol-water partition coefficient (logP), bond information content (BIC0), number of R-CX-R (C-026), eigenvalue sum from Z weighted distance matrix (SEigZ) and fragment based polar surface area (PSA) selected by Shuffling-ANFIS reveal the role of hydrophobicity, electronic and steric interactions in the mechanism of toxic action. Sequential zeroing of weights (SZW) as a sensitivity analysis method revealed that the hydrophobicity and electronic interactions play a major role in toxicity of these compounds. Satisfactory results (q(2)=0.828 and RMSE=0.348) in comparison with the previous works indicate that the Shuffling-ANFIS-ANN technique is able to model a diverse chemical class with more than one mechanism of toxicity by using simple and interpretable descriptors. Shuffling-ANFIS can be used as powerful feature selection technique, because its application in prediction of toxicity potency results in good statistical and interpretable physiochemical parameters. PMID:18499226

Jalali-Heravi, Mehdi; Kyani, Anahita

2008-06-01

62

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system  

Microsoft Academic Search

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients,

M. B. Djukanovic; M. S. Calovic; B. V. Vesovic; D. J. Sobajic

1997-01-01

63

Fuzzy controller for rapid nickel-cadmium batteries charger through adaptive neuro-fuzzy inference system (ANFIS) architecture  

Microsoft Academic Search

ANFIS architecture is a class of adaptive networks, which is functionally equivalent to fuzzy inference systems. The architecture has been employed for fuzzy modeling that learns information about a data-set in order to compute the membership functions and rule-base that best follow the given input-output data. ANFIS employs hybrid learning that combines the gradient method and the least squares estimates

Arun Khosla; Shakti Kumar; K. K. Aggarwal

2003-01-01

64

Winter Rainfall Prediction Based on climatic large scale signals by Using Adaptive Neuro Fuzzy Inference System (ANFIS)  

NASA Astrophysics Data System (ADS)

This paper aims to study the relationship between climatic large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neural-fuzzy inference system was used in this study to predict rainfall in the period between Jan and March 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 1000-mb level, the temperature of 700-mb level, the thickness between 500 and 1000-mb levels, the relative humidity of 300-mb level, Outgoing Long Wave Radiation (OLR), zonal wind and meridional wind. In the selection of these regions, which include a number of locations in the Persian Gulf, the Oman Sea, the Black Sea, the Caspian, the Mediterranean, the Adriatic, the Red Sea, the Eden Gulf, the Atlantic, the Indian Ocean, and Siberia, we have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the adaptive neural-fuzzy inference system in the period 1970 -1997 has been taught. Finally, the rainfall in the period 1998-2007 has been predicted. The results show that the adaptive neural-fuzzy inference system can predict the rainfall with reasonable accuracy

Fallah Ghalhary, G. A.; Khoshhal, J.; Habibi Nokhandan, M.

2009-09-01

65

Damping Local and InterArea Oscillations with Adaptive Neuro-Fuzzy Power System Stabilizer  

Microsoft Academic Search

In this paper, the authors report on the design, simulation and validation of an adaptive neuro-fuzzy inference system (ANFIS) based power system stabilizer (PSS) for a single-machine-infinite-bus (SMIB) and a multi-machine power system and investigate its performance in damping low frequency local and inter-area oscillations. The design employs a first order Sugeno fuzzy model, whose parameters are tuned off-line through

P. Mitra; S. Chowdhury; S. K. Pal; Y. H. Song; G. A. Taylor

2006-01-01

66

Transmission Line Fault Type Classification Based on Novel Features and Neuro-fuzzy System  

Microsoft Academic Search

This article presents an adaptive neuro-fuzzy inference system and a set of novel features for the classification of transmission line fault types. The ten common types of faults, including line-to-ground faults, line-to-line faults, line-to-line-to-ground faults, and three-phase faults, are considered in this research. The proposed method employs only current waveforms, and the new features include correlation coefficients and inter-quartile ranges

Yuan Liao

2010-01-01

67

A hierarchical neuro-fuzzy system to near optimal-time trajectory planning of redundant manipulators  

Microsoft Academic Search

Summary In this paper, the problem of minimum-time trajecto ry planning is studied for a three degrees-of-freedom planar manipulator using a hiera rchical hybrid neuro-fuzzy system. A first neuro-fuzzy network named NeFIK is considered to solve the inverse kinematics problem. After a few pre-processing step s characterizing the minimum- time trajectory and the corresponding torques, a se cond neuro-fuzzy controller

Amar Khoukhi; Luc Baron; Marek Balazinski; Kudret Demirli

2008-01-01

68

A Hierarchical Neuro-Fuzzy System to Minimum-Time Trajectory Planning of Redundant Manipulators  

Microsoft Academic Search

In this paper, the problem of minimum-time trajectory planning is studied for a three degrees-of- freedom (3-DOF) planar manipulator using a hierarchical hybrid neuro-fuzzy system1. A first pre- processing step involves two components. The first component is NeFIK (for Neuro-Fuzzy Inverse Kinematics), a neuro-fuzzy network designed to learn and solve the inverse kinematics problem. The second one is an optimal

Amar KHOUKHI; Luc BARON; Marek BALAZINSKI Kudret DEMIRLI

69

Prediction of the mass gain during high temperature oxidation of aluminized nanostructured nickel using adaptive neuro-fuzzy inference system  

NASA Astrophysics Data System (ADS)

In this paper, the applicability of ANFIS as an accurate model for the prediction of the mass gain during high temperature oxidation using experimental data obtained for aluminized nanostructured (NS) nickel is presented. For developing the model, exposure time and temperature are taken as input and the mass gain as output. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the network. We have compared the proposed ANFIS model with experimental data. The predicted data are found to be in good agreement with the experimental data with mean relative error less than 1.1%. Therefore, we can use ANFIS model to predict the performances of thermal systems in engineering applications, such as modeling the mass gain for NS materials.

Hayati, M.; Rashidi, A. M.; Rezaei, A.

2012-10-01

70

A Novel Unsupervised Neuro-Fuzzy System Applied to Circuit Analysis  

Microsoft Academic Search

In this paper, for the first time, unsupervised neuro-fuzzy system is presented and is applied in circuit analysis. Usually Neuro-fuzzy systems have a learning phase in which the system is trained with input data. But if the training set is unavailable, conventional procedures encounter serious problem. Due to unsupervised character, no learning data is needed. To investigate the method, linear

Hadi Sadoghi Yazdi; Seyed Ebrahim Hosseini

71

The association forecasting of 13 variants within seven asthma susceptibility genes on 3 serum IgE groups in Taiwanese population by integrating of adaptive neuro-fuzzy inference system (ANFIS) and classification analysis methods.  

PubMed

Asthma is one of the most common chronic diseases in children. It is caused by complicated coactions between various genetic factors and environmental allergens. The study aims to integrate the concept of implementing adaptive neuro-fuzzy inference system (ANFIS) and classification analysis methods for forecasting the association of asthma susceptibility genes on 3 serum IgE groups. The ANFIS model was trained and tested with data sets obtained from 425 asthmatic subjects and 483 non-asthma subjects from the Taiwanese population. We assessed 13 single-nucleotide polymorphisms (SNPs) in seven well-known asthma susceptibility genes; firstly, the proposed ANFIS model learned to reduce input features from the 13 SNPs. And secondly, the classification will be used to classify the serum IgE groups from the simulated SNPs results. The performance of the ANFIS model, classification accuracies and the results confirmed that the integration of ANFIS and classified analysis has potential in association discovery. PMID:20703737

Wang, Cheng-Hang; Liu, Baw-Jhiune; Wu, Lawrence Shih-Hsin

2012-02-01

72

Neuro-fuzzy modeling and control  

Microsoft Academic Search

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

73

A Neuro-Fuzzy System for Characterization of Arm Movements  

PubMed Central

The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours).

Balbinot, Alexandre; Favieiro, Gabriela

2013-01-01

74

A neuro-fuzzy online fault detection and diagnosis algorithm for nonlinear and dynamic systems  

Microsoft Academic Search

This paper presents a new fault detection and diagnosis approach for nonlinear dynamic plant systems with a neuro-fuzzy based\\u000a approach to prevent developing of fault as soon as possible. By comparison of plants and neuro-fuzzy estimator outputs in\\u000a the presence of noise, residual signal is generated and compared with a predefined threshold, the fault can be detected. To\\u000a diagnose the

Mohsen Shabanian; Mohsen Montazeri

2011-01-01

75

System identification of smart structures using a wavelet neuro-fuzzy model  

NASA Astrophysics Data System (ADS)

This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar

2012-11-01

76

MIA-QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives.  

PubMed

The activities of a series of HIV reverse transcriptase inhibitor TIBO derivatives were recently modeled by using genetic function approximation (GFA) and artificial neural networks (ANN) on topological, structural, electronic, spatial and physicochemical descriptors. The prediction results were found to be superior to those previously established. In the present work, the multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) method coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS), which accounts for non-linearities, was applied on the same set of compounds previously reported. Additionally, partial least squares (PLS) and multilinear partial least squares (N-PLS) regressions were used for comparison with the MIA-QSAR/PCA-ANFIS model. The ANFIS procedure was capable of accurately correlating the inputs (PCA scores) with the bioactivities. The predictive performance of the MIA-QSAR/PCA-ANFIS model was significantly better than the MIA-QSAR/PLS and N-PLS models, as well as than the reported models based on CoMFA, CoMSIA, OCWLGI and classical descriptors, suggesting that the present methodology may be useful to solve other QSAR problems, specially those involving non-linearities. PMID:20060625

Goodarzi, Mohammad; Freitas, Matheus P

2010-04-01

77

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

PubMed

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

78

A transductive neuro-fuzzy controller: application to a drilling process.  

PubMed

Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage. PMID:20659865

Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R

2010-07-01

79

Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Models for Predicting the Weld Bead Width and Depth of Penetration from the Infrared Thermal Image of the Weld Pool  

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

80

Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system  

NASA Astrophysics Data System (ADS)

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to predict MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. 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.

Petkovi?, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Md Nasir, Mohd Hairul Nizam; Pavlovi?, Nenad T.; Akib, Shatirah

2014-07-01

81

An event-driven neuro-fuzzy model for adaptive prognosis in homeostatic systems  

Microsoft Academic Search

This paper describes recent progress in an event-driven dynamic recurrent neuro-fuzzy model that is designed to estimate and predict states of interest within the human body. Four layers are implemented in this system, each of which consists of clusters of neurons: input layer, rule-state layer, output layer, and outcome layer. Detected events are mapped as fuzzy variables in input layer

Y. Wang; J. M. Winters

2005-01-01

82

Neuro-fuzzy control for autonomous wind–diesel systems using biomass  

Microsoft Academic Search

This paper deals with the development of a neuro-fuzzy controller for a wind–diesel system composed of a stall regulated wind turbine with an induction generator connected to an ac bus-bar in parallel with a diesel generator set having a synchronous generator. A gasifier is capable of converting tons of wood chips per day into a gaseous fuel that is fed

Francisco Jurado; José R Saenz

2002-01-01

83

Inference of S-wave velocities from well logs using a Neuro-Fuzzy Logic (NFL) approach  

NASA Astrophysics Data System (ADS)

The knowledge of S-wave velocity values is important for a complete characterization and understanding of reservoir rock properties. It could help in determining fracture propagation and also to improve porosity prediction (Cuddy and Glover, 2002). Nevertheless the acquisition of S-wave velocity data is rather expensive; hence, for most reservoirs usually this information is not available. In the present work we applied a hybrid system, that combines Neural Networks and Fuzzy Logic, in order to infer S-wave velocities from porosity (?), water saturation (Sw) and shale content (Vsh) logs. The Neuro-Fuzzy Logic (NFL) technique was tested in two wells from the Guafita oil field, Apure Basin, Venezuela. We have trained the system using 50% of the data randomly taken from one of the wells, in order to obtain the inference equations (Takani-Sugeno-Kang (TSK) fuzzy model). Equations using just one of the parameters as input (i.e. ??, Sw or Vsh), combined by pairs and all together were obtained. These equations were tested in the whole well. The results indicate that the best inference (correlation between inferred and experimental data close to 80%) is obtained when all the parameters are considered as input data. An increase of the equation number of the TSK model, when one or just two parameters are used, does not improve the performance of the NFL. The best set of equations was tested in a nearby well. The results suggest that the large difference in the petrophysical and lithological characteristics between these two wells, avoid a good inference of S-wave velocities in the tested well and allowed us to analyze the limitations of the method.

Aldana, Milagrosa; Coronado, Ronal; Hurtado, Nuri

2010-05-01

84

Neuro-fuzzy techniques for traffic control  

Microsoft Academic Search

Neuro-fuzzy techniques are proposed here to control each light of an intersection, at one-second intervals. Rules, fuzzification and inference are modeled by a neural network. For each signal, the neuro-fuzzy control selects between ‘switch on’ and ‘switch off’, and presents the required action to a Petri net. A neuro-fuzzy acceleration of Forward Dynamic Programming (FDP) is obtained by enumerating controls

J. J. Henry; J. L. Farges; J. L. Gallego

1998-01-01

85

Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation  

NASA Astrophysics Data System (ADS)

The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an alarm is instigated predicting the advent of system abnormal operation. The incoming data could be compared to previous trends as well as matched to trends developed through computer simulations and stored using fuzzy learning.

Ibrahim, Wael Refaat Anis

86

A neuro-fuzzy algorithm for coordinated traffic responsive ramp metering  

Microsoft Academic Search

This paper proposes a nonlinear approach for designing traffic responsive and coordinated ramp control using a self adapting fuzzy system. An adaptive neuro-fuzzy inference system (ANFIS) is used to incorporate a hybrid learning procedure into the control system. The traffic responsive metering rate is determined in every minute by the neuro-fuzzy control algorithm. Coordination between multiple on-ramps is ensured by

Klaus Bogenberger; Hartmut Keller; Svetlana Vukanovic

2001-01-01

87

Modeling and simulation of Adaptive Neuro-Fuzzy controller for Chopper-Fed DC Motor Drive  

Microsoft Academic Search

The classical controllers algorithm is both simple and reliable, and has been applied to thousands of control loops in various industrial applications over the past 60 years (89%-90% of applications). This paper presents the neuro- fuzzy controller incorporates fuzzy logic algorithm with a five-layer artificial neural network (ANN) structure. The conventional controller is replaced by Adaptive Neuro-Fuzzy Inference System (ANFIS)

Yousif I. Al-Mashhadany

2011-01-01

88

Neuro-fuzzy intelligent controller for ship roll motion stabilization  

Microsoft Academic Search

In this paper, we present an Adaptive Network-based Fuzzy Inference System (ANFIS), based on a neuro-fuzzy controller, as a possible control mechanism for a ship stabilizing fin system. Simulation results show that ANFIS can effectively improve the ship stabilizing performance against roll motion in cases of rough sea conditions. it is a promising alternative to conventional PID controllers.

Chen Guo; Marwan A. Simaan; Zengqi Sun

2003-01-01

89

Hybrid Neuro-Fuzzy Network-Priori Knowledge Model in Temperature Control of a Gas Water Heater System  

Microsoft Academic Search

This paper presents a hybrid neuro-fuzzy network-priori knowledge model in temperature control of a gas water heater system. The hybrid model consists in a cascade connection of two blocks: an approximate first principles model (FPM) and an unknown block. The first principles model is constructed based in the balance equations of the system and in a priori knowledge. The unknown

Jose Antonio Vieira; Fernando Morgado Dias; Alexandre Manuel Mota

2005-01-01

90

NEURO-FUZZY MODELING FOR CROP YIELD PREDICTION  

Microsoft Academic Search

The purpose of this paper is to explore the dynamics of neural networks in forecasting crop (wheat) yield using remote sensing and other data. We use the Adaptive Neuro-Fuzzy Inference System (ANFIS). The input to ANFIS are several parameters derived from the crop growth simulation model (CGMS) including soil moisture content, above ground biomass, and storage organs biomass. In addition

D. Stathakis; I. Savin; T. Nègre

2006-01-01

91

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

92

An intelligent hybrid neuro-fuzzy rule-based system for prognostic decision making in prostate cancer patients  

Microsoft Academic Search

A novel hybrid neuro-fuzzy rule-based system is presented for prognostic decision making in prostate cancer patients. The results are further compared with those of multilayer feedforward backpropagation neural networks (MLFFBPNN), fuzzy k-nearest neighbour classifier (FK-NN) and logistic regression (LR).

Huseyin Seker; M. O. Odetayo; D. Petrovic; R. N. G. Naguib; F. C. Hamdy

2003-01-01

93

Multi Groups Cooperation based Symbiotic Evolution for TSK-type Neuro-Fuzzy Systems Design  

PubMed Central

In this paper, a TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution method (TNFS-MGCSE) is proposed. The TNFS-MGCSE is developed from symbiotic evolution. The symbiotic evolution is different from traditional GAs (genetic algorithms) that each chromosome in symbiotic evolution represents a rule of fuzzy model. The MGCSE is different from the traditional symbiotic evolution; with a population in MGCSE is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperate with other groups to generate the better chromosomes by using the proposed cooperation based crossover strategy (CCS). In this paper, the proposed TNFS-MGCSE is used to evaluate by numerical examples (Mackey-Glass chaotic time series and sunspot number forecasting). The performance of the TNFS-MGCSE achieves excellently with other existing models in the simulations.

Cheng, Yi-Chang; Hsu, Yung-Chi

2010-01-01

94

A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system  

SciTech Connect

This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P and O) algorithm dispositive. (author)

Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken [Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Nakamachi (Japan)

2010-12-15

95

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

96

A Neuro-Fuzzy Identification of ECG Beats  

Microsoft Academic Search

This paper presents a fuzzy rule based classifier and its application to discriminate premature ventricular contraction (PVC)\\u000a beats from normals. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to discover the fuzzy rules in order to determine\\u000a the correct class of a given input beat. The main goal of our approach is to create an interpretable classifier that also\\u000a provides

Mohammed Amine Chikh; Mohammed Ammar; Radja Marouf

97

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

PubMed

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

98

Neuro-fuzzy control of a steam boiler-turbine unit  

Microsoft Academic Search

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

99

Neuro-fuzzy controller to navigate an unmanned vehicle.  

PubMed

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

100

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets  

NASA Astrophysics Data System (ADS)

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

101

Application of rough set-based neuro-fuzzy system in NIRS-based BCI for assessing numerical cognition in classroom  

Microsoft Academic Search

Near-infrared spectroscopy (NIRS) studies have revealed that performing mental arithmetic tasks have associated event-related hemodynamic responses that are detectable. Thus NIRS-based Brain Computer Interface (BCI) has the potential for investigating how to best teach mathematics in a classroom setting. This paper presents a novel computational intelligent method of applying rough set-based neuro-fuzzy system (RNFS) in NIRS-based BCI for assessing numerical

Kai Keng Ang; Cuntai Guan; Kerry Lee; Jie Qi Lee; Shoko Nioka; Britton Chance

2010-01-01

102

In vitro–in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks  

Microsoft Academic Search

The aim of the current study was to evaluate the potential of the dynamic lipolysis model to simulate the absorption of a poorly soluble model drug compound, probucol, from three lipid-based formulations and to predict the in vitro–in vivo correlation (IVIVC) using neuro-fuzzy networks. An oil solution and two self-micro and nano-emulsifying drug delivery systems were tested in the lipolysis

Dimitrios G. Fatouros; Flemming Seier Nielsen; Dionysios Douroumis; Leontios J. Hadjileontiadis; Anette Mullertz

2008-01-01

103

Identification of trash types in ginned cotton using neuro fuzzy techniques  

Microsoft Academic Search

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

104

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

105

A neuro-fuzzy warning system for combating cybersickness in the elderly caused by the virtual environment on a TFT-LCD.  

PubMed

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

106

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

Microsoft Academic Search

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

107

A neuro-fuzzy identification of ECG beats.  

PubMed

This paper presents a fuzzy rule based classifier and its application to discriminate premature ventricular contraction (PVC) beats from normals. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to discover the fuzzy rules in order to determine the correct class of a given input beat. The main goal of our approach is to create an interpretable classifier that also provides an acceptable accuracy. The performance of the classifier is tested on MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. On the test set, we achieved an overall sensitivity and specificity of 97.92% and of 94.52% respectively. Experimental results show that the proposed approach is simple and effective in improving the interpretability of the fuzzy classifier while preserving the model performances at a satisfactory level. PMID:20703643

Chikh, Mohammed Amine; Ammar, Mohammed; Marouf, Radja

2012-04-01

108

Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach  

Microsoft Academic Search

Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component\\/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS)

Chaochao Chen; George Vachtsevanos; Marcos E. Orchard

109

A neuro-fuzzy system for looper tension control in rolling mills  

Microsoft Academic Search

A looper tension control system is common to many rolling processes. Conventional tension controllers for mill actuation systems are based on a rolling model. They therefore cannot deal effectively with unmodeled dynamics and large parameter variations that can lead to scrap runs and machinery damage. In this paper, this problem is tackled by designing a fuzzy controller that possesses different

F. Janabi-Sharifi

2005-01-01

110

Development of a neuro-fuzzy expert system for predictive maintenance  

NASA Astrophysics Data System (ADS)

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

Yen, Gary G.; Meesad, Phayung

2001-07-01

111

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

112

Classification of Sleep Stages in Infants: A Neuro Fuzzy Approach.  

National Technical Information Service (NTIS)

An ANFIS based neuro-fuzzy system to classify sleep-waking states and stages in healthy infants has been developed. The classifier takes five input patterns identified from polysomnographic recordings on 20 s frames and assigns them to one out of five pos...

J. E. Heiss C. M. Held P. A. Estevez C. A. Perez C. A. Holzmann

2001-01-01

113

Neuro-fuzzy synthesis of flight control electrohydraulic servo  

Microsoft Academic Search

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

114

An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation  

Microsoft Academic Search

In this paper, a quantum neuro-fuzzy classifier (QNFC) for classification applications is proposed. The pro- posed QNFC model is a five-layer structure, which combines the compensatory-based fuzzy reasoning method with the traditional Takagi-Sugeno-Kang (TSK) fuzzy model. The compensatory-based fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Layer

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

2008-01-01

115

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques  

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

116

Stock trading using RSPOP: a novel rough set-based neuro-fuzzy approach.  

PubMed

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model. PMID:17001989

Ang, Kai Keng; Quek, Chai

2006-09-01

117

Neuro-fuzzy decision trees.  

PubMed

Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are often criticized to result in poor learning accuracy. In this paper, we propose Neuro-Fuzzy Decision Trees (N-FDTs); a fuzzy decision tree structure with neural like parameter adaptation strategy. In the forward cycle, we construct fuzzy decision trees using any of the standard induction algorithms like fuzzy ID3. In the feedback cycle, parameters of fuzzy decision trees have been adapted using stochastic gradient descent algorithm by traversing back from leaf to root nodes. With this strategy, during the parameter adaptation stage, we keep the hierarchical structure of fuzzy decision trees intact. The proposed approach of applying backpropagation algorithm directly on the structure of fuzzy decision trees improves its learning accuracy without compromising the comprehensibility (interpretability). The proposed methodology has been validated using computational experiments on real-world datasets. PMID:16496439

Bhatt, Rajen B; Gopal, M

2006-02-01

118

Landslide susceptibility mapping using a neuro-fuzzy  

NASA Astrophysics Data System (ADS)

This paper develops and applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. A neuro-fuzzy system is based on a fuzzy system that is trained by a learning algorithm derived from the neural network theory. The learning procedure operates on local information, and causes only local modifications in the underlying fuzzy system. The study area, Boun, suffered much damage following heavy rain in 1998 and was selected as a suitable site for the evaluation of the frequency and distribution of landslides. Boun is located in the central part of Korea. Landslide-related factors such as slope, soil texture, wood type, lithology, and density of lineament were extracted from topographic, soil, forest, and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs) and analysis results were verified using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping. Various MFs were used in this study, and after verification, the difference in accuracy according to the MFs was small, between 84.81% and 84.96%. The difference was just 0.15% and therefore the choice of MFs was not important in the study. Also, compared with the likelihood ratio model, which showed 84.94%, the accuracy was similar. Thus, the ANFIS could be applied to other study areas with different data and other study methods such as cross-validation. The developed ANFIS learns the if-then rules between landslide-related factors and landslide location for generalization and prediction. It is easy to understand and interpret, therefore it is a good choice for modeling landslide susceptibility mapping, which are also of great help for planners and engineers in selecting highly susceptible areas for further detail surveys and suitable locations to implement development. Although they may be less useful at the site-specific scale, where local geological and geographic heterogeneities may prevail, the results herein may be used as basic data to assist slope management and land use planning. For the method to be more generally applied, more landslide data are needed and more case studies should be conducted.

Lee, S.; Choi, J.; Oh, H.

2009-12-01

119

Rough neuro-fuzzy structures for classification with missing data.  

PubMed

This paper presents a new approach to fuzzy classification in the case of missing data. The rough fuzzy sets are incorporated into Mamdani-type neuro-fuzzy structures, and the rough neuro-fuzzy classifier is derived. Theorems that allow the determination of the structure of a rough neuro-fuzzy classifier are given. Several experiments illustrating the performance of the rough neuro-fuzzy classifier working in the case of missing features are described. PMID:19366645

Nowicki, Robert

2009-12-01

120

Recurrent neuro-fuzzy predictors for multi-step prediction of v-i characteristics of electric arc furnaces  

Microsoft Academic Search

Presents an application of recurrent neuro-fuzzy systems to predict electric arc furnaces voltage and current. The primary objective is to investigate capability of adaptive fuzzy systems to predict the v-i characteristics of nonlinear, multivariable, complex systems such as electric furnaces. The novelties of this work are proposing a combination of recurrent neuro-fuzzy networks deemed suitable for prediction and using a

A. R. Sadeghian; J. D. Lavers

2000-01-01

121

Recognition of Handwritten Arabic words using a neuro-fuzzy network  

SciTech Connect

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

122

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

123

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

124

Adaptive neuro-fuzzy logic analysis based on myoelectric signals for multifunction prosthesis control.  

PubMed

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

125

Data center selection based on neuro-fuzzy inference systems in cloud computing environments  

Microsoft Academic Search

A high-quality service for applications in cloud computing environments is guaranteed by making efficient use of resources\\u000a in data centers. Applications should be allocated to resources suitable for the load of data centers to achieve this. The\\u000a complex and dynamic nature of the load prevents the proper selection of one of multiple data centers and fails to meet the\\u000a demands

Joon-Min Gil; Jong Hyuk Park; Young-Sik Jeong

126

Prediction model for wire bonding process through adaptive neuro-fuzzy inference system  

Microsoft Academic Search

In the wire bonding process, different combinations of parameter values will directly affect wire bonding quality. The optimal combination of these parameter values is very important to ensure the overall process quality response. Therefore, it is necessary to investigate the effects and interactive relationship of the bonding parameters on the bonding quality. This paper chooses the response factors of shear

Jian Gao; Changhong Liu; Xin Chen; Detao Zheng; Ketian Li

2009-01-01

127

Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)  

Microsoft Academic Search

The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving

Manish Kakar; Lasse Rye Aarup; Dag Rune Olsen

2005-01-01

128

Development of INPLANS: An analysis on Students’ Performance Using Neuro-Fuzzy  

Microsoft Academic Search

This paper presents a simulation of Neuro-Fuzzy application for analysing studentspsila performance based on their CPA and GPA. The analysis is an extension of our previous study, which was called an analysis on studentpsilas performance using fuzzy systems. The main function of this analysis is to support the development of intelligent planning system (INPLANS) using fuzzy systems, neural networks, and

Khalid Isa; Shamsul Mohamad; Zarina Tukiran

2008-01-01

129

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

PubMed

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

130

Hybrid Stochastic-Neuro-Fuzzy Model-Based System for In-Flight Gas Turbine Engine Diagnostics.  

National Technical Information Service (NTIS)

One key aspect when developing a real-time in-flight risk-based health management system for jet engines is the development of accurate and robust fault classifiers. Regardless of the complex uncertainty propagation in the data fusion process, the selecti...

D. M. Ghiocel J. Altmann

2001-01-01

131

Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis  

NASA Astrophysics Data System (ADS)

Modeling groundwater vulnerability reliably and cost effectively for non-point source (NPS) pollution at a regional scale remains a major challenge. In recent years, Geographic Information Systems (GIS), neural networks and fuzzy logic techniques have been used in several hydrological studies. However, few of these research studies have undertaken an extensive sensitivity analysis. The overall objective of this research is to examine the sensitivity of neuro-fuzzy models used to predict groundwater vulnerability in a spatial context by integrating GIS and neuro-fuzzy techniques. The specific objectives are to assess the sensitivity of neuro-fuzzy models in a spatial domain using GIS by varying (i) shape of the fuzzy sets, (ii) number of fuzzy sets, and (iii) learning and validation parameters (including rule weights). The neuro-fuzzy models were developed using NEFCLASS-J software on a JAVA platform and were loosely integrated with a GIS. Four plausible parameters which are critical in transporting contaminants through the soil profile to the groundwater, included soil hydrologic group, depth of the soil profile, soil structure (pedality points) of the A horizon, and landuse. In order to validate the model predictions, coincidence reports were generated among model inputs, model predictions, and well/spring contamination data for NO 3-N. A total of 16 neuro-fuzzy models were developed for selected sub-basins of Illinois River Watershed, AR. The sensitivity analysis showed that neuro-fuzzy models were sensitive to the shape of the fuzzy sets, number of fuzzy sets, nature of the rule weights, and validation techniques used during the learning processes. Compared to bell-shaped and triangular-shaped membership functions, the neuro-fuzzy models with a trapezoidal membership function were the least sensitive to the various permutations and combinations of the learning and validation parameters. Over all, Models 11 and 8 showed relatively higher coincidence with well contamination data than other models. The strength of this method is that it offers a means of dealing with imprecise data, therefore, is a viable option for regional and continental scale environmental modeling where imprecise data prevail. The neuro-fuzzy models, however, should only be used as a tool within a broader framework of GIS, remote sensing and solute transport modeling to assess groundwater vulnerability along with functional, mechanistic and stochastic models.

Dixon, B.

2005-07-01

132

Neuro-Fuzzy Based Clustering Approach For Content Based Image Retrieval Using 2D- Wavelet Transform  

Microsoft Academic Search

Abstract—In this paper we introduce neuro-fuzzy based clustering approach for content based image retrieval using 2D-wavelet transform(2D-DWT). Most of the ,image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity.To address this problem, we developed neural network -fuzzy logic cluster based approach for content based image retrieval using 2D-wavelet transform. The system

V. Balamurugan; P. Anandhakumar

2009-01-01

133

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.  

PubMed

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

134

Modelling level change in lakes using neuro-fuzzy and artificial neural networks  

NASA Astrophysics Data System (ADS)

SummaryAccurate estimation of level change in lakes and reservoirs in response to climatic variations is an important step for the development of sustainable water usage policies, particularly for complex hydrological systems such as Lake Beysehir, Turkey. In this study, level changes of Lake Beysehir were estimated using adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and a seasonal autoregressive integrated moving average (SARIMA). The ANN and ANFIS models were first trained based on observed data between 1966 and 1984, and then used to predict water level changes over the test period extending from 1985 to 1990. The performances of the different models were evaluated by comparing the corresponding values of mean squared errors (MSE) and decisive coefficients ( R2). While all models produced acceptable results, the minimum MSE value (0.0057) and the maximum R2 value (0.7930) were obtained with ANFIS model, followed by the three-layered artificial neural network model (ANN1). The lowest performance was observed with the SARIMA model.

Yarar, Alpaslan; Onucy?ld?z, Mustafa; Copty, Nadim K.

2009-02-01

135

Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.  

PubMed

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; Petkovi?, Dalibor; Hashim, Roslan; Motamedi, Shervin

2014-01-01

136

EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features.  

PubMed

In this paper, a feature extraction method through the time-series prediction based on the adaptive neuro-fuzzy inference system (ANFIS) is proposed for brain-computer interface (BCI) applications. The ANFIS time-series prediction together with multiresolution fractal feature vectors (MFFVs) is applied for feature extraction in motor imagery (MI) classification. The features are extracted from the electroencephalography (EEG) signals recorded from subjects performing left and right MI. Two ANFISs are trained to perform time-series predictions for respective left and right MI data. Features obtained from the difference of MFFVs between the predicted and actual signals are then calculated through a window of EEG signals. Finally, a simple linear classifier, namely linear discriminant analysis (LDA), is used for classification. The proposed method is estimated with classification accuracy and the area under the receiver operating characteristics curve (AUC) on six subjects from two data sets. I also assess the performance of proposed method by comparing it with well-known linear adaptive autoregressive (AAR) model, AAR time-series prediction, and neural network (NN) time-series prediction. The results indicate that ANFIS time-series prediction together with MFFV features is a promising method in MI classification. PMID:20381529

Hsu, Wei-Yen

2010-06-15

137

A neuro-fuzzy control alarm on momentum of driving behavior for detecting and combating driver fatigue  

Microsoft Academic Search

Car accidents are one of the major causes of death in modern society and driver fatigue is identified as one major risk factor. In this study, a neuro-fuzzy detecting and combating fatigue system, a possible more economical solution for detecting and combating driver fatigue, was developed. First, we surveyed and selected adaptive tendency indices (i.e. relative strength index (RSI), stochastic

Cheng-Li Liu; Shiaw-Tsyr Uang

2010-01-01

138

Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.  

PubMed

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO? concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model. PMID:23111771

Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad

2013-05-01

139

Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis  

Microsoft Academic Search

Nowadays because of the complicated nature of making decision in stock market and making real-time strategy for buying and selling stock via portfolio selection and maintenance, many research papers has involved stock price prediction issue. Low accuracy resulted by models may increase trade cost such as commission cost in more sequenced buy and sell signals because of insignificant alarms and

Akbar Esfahanipour; Werya Aghamiri

2010-01-01

140

A new approach of adaptive Neuro Fuzzy Inference System (ANFIS) modeling for yield prediction in the supply chain of Jatropha  

Microsoft Academic Search

Jatropha seed yield prediction is one of the most important factors for developing a supply chain modeling of Jatropha seed. The seeds of Jatropha curcas are generally used for the making of oil. Jatropha happens to be one of the most easily cultivable biofuel crops having a high degree of yield. The extract from its seeds, jatropha oil, is processed

S. P. Srinivasan; P. Malliga

2010-01-01

141

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

142

Neuro-fuzzy looper control with T-operator and rule tuning for rolling mills: theory and comparative study  

Microsoft Academic Search

Looper control is widely used for rolling tension control, but conventional control performance usually degrades with the rolling parameter variations. Fuzzy control systems were proposed in Janabi-Sharifi and Fan (2000) and outperformed conventional control systems in terms of disturbance rejection and decreased steady state error. In this paper, hybrid neuro-fuzzy control is proposed for the T-operator and rule-tuning. Simulation results

F. Janabi-Sharifi

2001-01-01

143

A novel self?organizing neuro?fuzzy multilayered classifier for land cover classification of a VHR image  

Microsoft Academic Search

A novel self?organizing neuro?fuzzy multilayered classifier (SONeFMUC) is introduced in this paper, with feature selection capabilities, for the classification of an IKONOS image. The structure of the proposed network is developed in a sequential fashion using the group method of data handling (GMDH) algorithm. The node models, regarded as generic classifiers, are represented by fuzzy rule?based systems, combined with a

N. E. Mitrakis; C. A. Topaloglou; T. K. Alexandridis; J. B. Theocharis; G. C. Zalidis

2008-01-01

144

Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning  

PubMed Central

Background Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. Methods The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Results Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02%) and membership functions (3.9%), thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. Conclusion The study demonstrated a feasible way to automatically perform parameter optimization of inverse treatment planning under guidance of prior knowledge without human intervention other than providing a set of constraints that have proven clinically useful in a given setting.

Stieler, Florian; Yan, Hui; Lohr, Frank; Wenz, Frederik; Yin, Fang-Fang

2009-01-01

145

A Dynamic Neuro-Fuzzy Model Providing Bio-State Estimation and Prognosis Prediction for Wearable Intelligent Assistants  

PubMed Central

Background Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents/assistants (WIAs). Methods The model structure includes the following types of signals: inputs, states, outputs and outcomes. Inputs are facts or events which have effects on patients' physiological and rehabilitative states; different classes of inputs (e.g., facts, context, medication, therapy) have different nonlinear mappings to a fuzzy "effect." States are dimensionless linguistic fuzzy variables that change based on causal rules, as implemented by a fuzzy inference system (FIS). The FIS, with rules based on expertise and evidence, essentially defines the nonlinear state equations that are implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states. Results The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes), showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain) as a function of input event patterns (e.g., medications). The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training. Conclusion A neuro-fuzzy modelling framework is developed for estimating rehabilitative change that can be applied in any field of rehabilitation if sufficient evidence and/or expert knowledge are available. It is intended to provide context-awareness of changing status through state estimation, which is critical information for WIA's to be effective.

Wang, Yu; Winters, Jack M

2005-01-01

146

Development of neuro-fuzzy models to account for temporal and spatial variations in a lumped rainfall runoff model  

NASA Astrophysics Data System (ADS)

SummaryFor many good and practical reasons, lumped rainfall-runoff models are widely used to represent a catchment's response to rainfall. However, they have some acknowledged limitation, some of which are addressed here using a neuro-fuzzy model to combine, in an optimal way, a number of lumped-sub-models. For instance, to address temporal variation, one of the sub-models in the combination may perform well under flood conditions and another under drier conditions and the neuro-fuzzy system would combine their outputs for each time-step in a manner depending on the prevailing conditions. Similarly to address spatial variation, one of the sub-models may perform well for the upland parts of the catchment and another for the lowland parts and again the neuro-fuzzy system is expected to combine the different outputs appropriately. The proposed combination method can use any lumped catchment model, but has been tested here with the simple linear model (SLM) and the soil moisture and accounting routing (SMAR) models. Eleven catchments with different hydrological and meteorological conditions have been used to assess the models with respect to temporal variations in response while one catchment is used to address the effect of spatial variation. The neuro-fuzzy combined-sub-models of SLM and SMAR modelled the temporal and spatial variation in catchment response better than the lumped version of each model. Also the SMAR model significantly outperformed the SLM either as a lumped model or as a sub-model in any of the combinations.

Nasr, Ahmed; Bruen, Michael

2008-02-01

147

Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm.  

PubMed

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

148

A neuro-fuzzy based approach to affective design  

Microsoft Academic Search

Satisfying customers’ affective needs is nowadays seen as a key point to designing a successful product. To ensure this, the\\u000a relations between physical form elements and affective responses regarding the product should be quantified. In this article,\\u000a a novel systematic approach is proposed to overcome this problem by representing the relation structure with “IF THEN” type\\u000a fuzzy rules. A neuro-fuzzy

Diyar Akay; Mustafa Kurt

2009-01-01

149

Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach  

NASA Astrophysics Data System (ADS)

Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.

Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.

2012-04-01

150

Autonomous novelty detection and object tracking in video streams using evolving clustering and Takagi-Sugeno type neuro-fuzzy system  

Microsoft Academic Search

Autonomous systems for surveillance, security, patrol, search and rescue are the focal point of extensive research and interest from defense and the security related industry, traffic control and other institutions. A range of sensors can be used to detect and track objects, but optical cameras or camcorders are often considered due to their convenience and passive nature. Tracking based on

Plamen P. Angelov; Ramin Ramezani; Xiaowei Zhou

2008-01-01

151

Assessing the Reliability of Complex Networks through Hybrid Intelligent Systems  

Microsoft Academic Search

This paper describes the application of Hybrid Intelligent Systems in a new domain: reliability of complex networks. The reliability is assessed by employing two algorithms (TREPAN and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)), both belonging to the Hybrid Intelligent Systems paradigm. TREPAN is a technique to extract linguistic rules from a trained Neural Network, whereas ANFIS is a method that combines

D. E. Torres; C. M. Rocco

152

VSS Theory Based Training of a Fuzzy Motion Control System  

Microsoft Academic Search

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

153

An adaptive system for modelling and simulation of electrical arc furnaces  

Microsoft Academic Search

Modelling and simulator development for electric arc furnaces (EAFs) are of significant importance in designing control systems and in performance optimisation of EAFs. This paper presents a method based on adaptive neuro-fuzzy inference systems (ANFIS) for modelling and simulating EAFs with the focus on the regulator loop that is used for positioning the electrodes. The effectiveness of the simulator is

F. Janabi-Sharifi; G. Jorjani

2009-01-01

154

An adaptive fusion algorithm based on ANFIS for radar\\/infrared system  

Microsoft Academic Search

In order to improve tracking ability, an adaptive fusion algorithm based on adaptive neuro-fuzzy inference system (ANFIS) for radar\\/infrared system is proposed, which combines the merits of fuzzy logic and neural network. Fuzzy adaptive fusion algorithm is a powerful tool to make the actual value of the residual covariance consistent with its theoretical value. To overcome the defect of the

Q. Yuan; C. Y. Dong; Q. Wang

2009-01-01

155

Complex dynamical system fault diagnosis based on multiple ANFIS using independent component  

Microsoft Academic Search

In this paper, an online fault diagnosis for a complex dynamical systems integrating adaptive neuro-fuzzy inference system (ANFIS) and using independent component analysis (ICA) for feature extracting is presented. In this approach, using ICA provide salient features selected from raw measured data sets. Subsequently, the most superior extracted features are fed into multiple ANFIS in order to identify different abnormal

P. Akhlaghi; A. R. Kashanipour; K. Salahshoor

2008-01-01

156

A novel ANFIS controller for maximum power point tracking in photovoltaic systems  

Microsoft Academic Search

This paper presents the design of a controller for maximum power point tracking (MPPT) of a photovoltaic system. The proposed controller relies upon an adaptive neuro-fuzzy inference system (ANFIS) which is designed as a combination of the concepts of Sugeno fuzzy model and neural network. The controller employs the ANFIS of five layers with nine fuzzy rules. Simulations with practical

Noppadol Khaehintung; Phaophak Sirisuk; Werasak Kurutach

2003-01-01

157

A combined neural network and fuzzy systems based adaptive digital predistortion for RF power amplifier linearization  

Microsoft Academic Search

Linearization of nonlinear RF power amplifiers (PA) is an important issue when spectrally efficient modulation signals are used in mobile communications. Adaptive digital predistortion (ADP) is one promising linearization technique that can be employed. This paper presents a combined neural network and fuzzy systems based ADP for RF PA linearization. This hybrid approach employed is called adaptive neuro-fuzzy inference system

K. C. Lee; P. Gardner

2004-01-01

158

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area  

NASA Astrophysics Data System (ADS)

This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.

Oh, Hyun-Joo; Pradhan, Biswajeet

2011-09-01

159

A Neuro-Fuzzy modeling for prediction of solar cycles 24 and 25  

NASA Astrophysics Data System (ADS)

The paper presents a Neuro-Fuzzy model to predict the features of the forthcoming sunspot cycles 24 and 25. The sunspot time series were analyzed with the proposed model. It is optimized based on Backpropagation scheme and applied to the yearly smoothed sunspot numbers. The appropriate number of network inputs for the sunspots data series is obtained based on sequential forward search for the Neuro-Fuzzy model. According to the model prediction the maximum amplitudes of the cycles 24 and 25 will occur in the year 2013 and year 2022 with peaks of 101±8 and 90.7±8, respectively. The correlation and error analysis are discussed to ensure the performance of the proposed Neuro-Fuzzy approach as a predictor for sunspot time series. The correlation coefficient between Neuro-Fuzzy model forecasted sunspot number values with the actual ones is 0.96.

Attia, Abdel-Fattah; Ismail, Hamed A.; Basurah, Hassan M.

2013-03-01

160

An expert system for predicting longitudinal dispersion coefficient in natural streams by using ANFIS  

Microsoft Academic Search

Longitudinal dispersion coefficient in rivers and natural streams usually is estimated by simple inaccurate empirical relations, because of the complexity of the phenomena. So, in this study using adaptive neuro-fuzzy inference system (ANFIS), which have the ability of perception and realization of phenomenon without need for mathematical governing equations, a new flexible tool is developed to predict the longitudinal dispersion

Hossien Riahi-madvar; Seyed Ali Ayyoubzadeh; Ehsan Khadangi; Mohammad Mehdi Ebadzadeh

2009-01-01

161

On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques  

Microsoft Academic Search

In this paper, we propose two new neuro-fuzzy schemes, one for classification and one for clustering problems. The classification scheme is based on Simpson's fuzzy min-max method (1992, 1993) and relaxes some assumptions he makes. This enables our scheme to handle mutually nonexclusive classes. The neuro-fuzzy clustering scheme is a multiresolution algorithm that is modeled after the mechanics of human

Anupam Joshi; Narendran Ramakrishman; Elias N. Houstis; John R. Rice

1997-01-01

162

Self adaptive neuro-fuzzy control of FES-assisted paraplegics indoor rowing exercise  

Microsoft Academic Search

This paper describes the development of a self adaptive neuro-fuzzy control mechanism for FES-assisted indoor rowing exercise (FES-rowing). The FES-rowing is introduced as a total body exercise for rehabilitation of function of lower body through the application of functional electrical stimulation (FES). The neuro-fuzzy control technique is a control technique that combines fuzzy logic controller and a neural network, which

Z. Hussain; S. Z. Yahaya; R. Boudville; K. A. Ahmad; M. H. Mohd Noor

2011-01-01

163

Automatic determination of diseases related to lymph system from lymphography data using principles component analysis (PCA), fuzzy weighting pre-processing and ANFIS  

Microsoft Academic Search

It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of lymph diseases, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on lymph diseases using principles component analysis (PCA), fuzzy weighting pre-processing and adaptive neuro-fuzzy inference

Kemal Polat; Salih Günes

2007-01-01

164

Correlation Between Miocene Global Climatic Changes (d18O) and Magnetic Properties, Using Neuro Fuzzy Logic Analysis  

NASA Astrophysics Data System (ADS)

We have used the hybrid algorithm of neuro fuzzy logic (NFL), to establish a correlation between global climatic changes (benthic foraminiferal d18O data), experimental S-ratios and magnetic susceptibility (?), in 44 samples of the Colombian stratigraphic well Saltarín 1A (Llanos foreland basin). ? and S-ratios were linked to global d18O data based on a constant accumulation rate for the stratigraphic interval flanked by the two age constrains available. A good inference (over 64%) is obtained using 4 fuzzy clusters or TKS type relationships. A stronger correlation is perhaps prevented by the likely influence of local and regional tectonic events and climatic changes that could have affected the Colombian Llanos foreland basin during Miocene times. For the Guayabo and León lithologies, it seems that the late diagenesis of the primary magnetic minerals and the assumption of a constant accumulation rate might have a minor influence on these results.

Costanzo, Vincenzo; da Silva, Ana; Hurtado, Nuri

2010-05-01

165

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

166

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

PubMed

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

167

A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region  

NASA Astrophysics Data System (ADS)

Three different data driven models were used for forecasting river flow.We compared the performance of three models in the semiarid mountain region.SVM model performed better than ANN and ANFIS in the river flow forecasting.

He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun

2014-02-01

168

Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers  

Microsoft Academic Search

The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion

Karim Salahshoor; Mojtaba Kordestani; Majid S. Khoshro

2010-01-01

169

Adaptive neuro-fuzzy inference systems (ANFIS) application to investigate potential use of natural ventilation in new building designs in Turkey  

Microsoft Academic Search

Natural ventilation in living and working places provides both circulation of clear air and a decrease of indoor temperature, especially during hot summer days. In addition to openings, the dimension ratio and position of buildings play a significant role to obtain a uniform indoor air velocity distribution. In this study, the potential use of natural ventilation as a passive cooling

Tahir Ayata; Ertu?rul Çam; Osman Y?ld?z

2007-01-01

170

Comparison of a Multi output Adaptative Neuro-Fuzzy Inference System (MANFIS) and Multi Layer Perceptron (MLP) in Cloud Computing Provisioning  

Microsoft Academic Search

Cloud computing has changed the way that computing is delivered and used, turning it into a utility like water or electricity. In this context, many challenges and opportunities appear to make the Cloud a stable, accessible and trustworthy environment. Resource provisioning in the Cloud must be dynamic and able to adapt to changing needs. In this paper, a provisioning method

Carlos Oberdan Rolim; Fernando Schubert; Anubis G. M. Rossetto; Valderi R. Q. Leithardt; Cláudio F. R. Geyer; Carlos B. Westphall

171

A New Technique for Temperature and Humidity Profile Retrieval From Infrared-Sounder Observations Using the Adaptive Neuro-Fuzzy Inference System  

Microsoft Academic Search

The accuracy of the atmospheric profiles of temperature and humidity, retrieved from infrared-sounder observations using physical retrieval algorithms, depends directly on the quality of the first-guess profiles. In the past, forecasts from the numerical-weather-prediction models were extensively used as the first guess. During the past few years, the first guess for physical retrieval is being estimated using regression techniques from

Kottayil S. Ajil; Pradeep Kumar Thapliyal; Munn V. Shukla; Pradip K. Pal; Prakash C. Joshi; Ranganath R. Navalgund

2010-01-01

172

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

173

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

174

EMG-based neuro-fuzzy control of a 4DOF upper-limb power-assist exoskeleton.  

PubMed

We have been developing a 4DOF exoskeleton robot system in order to assist shoulder vertical motion, shoulder horizontal motion, elbow motion, and forearm motion of physically weak persons such as elderly, injured, or disabled persons. The robot is directly attached to a user's body and activated based on EMG (Electromyogram) signals of the user's muscles, since the EMG signals directly reflect the user's motion intention. A neuro-fuzzy controller has been applied to control the exoskeleton robot system. In this paper, controller adaptation method to user's EMG signals is proposed. A motion indicator is introduced to indicate the motion intention of the user for the controller adaptation. The experimental results show the effectiveness of the proposed method. PMID:18002635

Kiguchi, Kazuo; Imada, Yasunobu; Liyanage, Manoj

2007-01-01

175

Comparative evaluation of pattern recognition algorithms: statistical, neural, fuzzy, and neuro-fuzzy techniques  

NASA Astrophysics Data System (ADS)

Pattern recognition by fuzzy, neural, and neuro-fuzzy approaches, has gained popularity partly because of intelligent decision processes involved in some of the above techniques, thus providing better classification and partly because of simplicity in computation required by these methods as opposed to traditional statistical approaches for complex data structures. However, the accuracy of pattern classification by various methods is often not considered. This paper considers the performance of major fuzzy, neural, and neuro-fuzzy pattern recognition algorithms and compares their performances with common statistical methods for the same data sets. For the specific data sets chosen namely the Iris data set, an the small Soybean data set, two neuro-fuzzy algorithms, AFLC and IAFC, outperform other well- known fuzzy, neural, and neuro-fuzzy algorithms in minimizing the classification error and equal the performance of the Bayesian classification. AFLC, and IAFC also demonstrate excellent learning vector quantization capability in generating optimal code books for coding and decoding of large color images at very low bit rates with exceptionally high visual fidelity.

Mitra, Sunanda; Castellanos, Ramiro

1998-10-01

176

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

National Technical Information Service (NTIS)

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/A...

H. A. Malki R. Langari S. Kottapalli

2003-01-01

177

Design of a neuro-fuzzy controller for speed control applied to AC servo motor  

Microsoft Academic Search

In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and an artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules that are created by an expert. To adapt the more precise modeling, error backpropagation learning of adjusting the link-weight of fuzzy

Sang Hoon Kim; Lark Kyo Kim

2001-01-01

178

A 92mW real-time traffic sign recognition system with robust light and dark adaptation  

Microsoft Academic Search

A traffic sign recognition system that is robust under various lighting condition is proposed with an image enhancement preprocessor and a recognition processor. The image enhancement preprocessor performs the Multi-scale Retinex (MSR) algorithm for robust light and dark adaptation. It includes a mixed-mode Adaptive Neuro-Fuzzy Inference System (ANFIS) engine that performs online optimizations for various scenes. The recognition processor performs

Junyoung Park; Joonsoo Kwon; Jinwook Oh; Seungjin Lee; Hoi-Jun Yoo

2011-01-01

179

Predicting performance of a ground-source heat pump system using fuzzy weighted pre-processing-based ANFIS  

Microsoft Academic Search

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

180

Streamflow Forecasting Using Nuero-Fuzzy Inference System  

NASA Astrophysics Data System (ADS)

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.

Nanduri, U. V.; Swain, P. C.

2005-12-01

181

Neuro-fuzzy speed control of traveling-wave type ultrasonic motor drive using frequency and phase modulation.  

PubMed

This paper presents a Fuzzy Neural Network (FNN) control system for a traveling-wave ultrasonic motor (TWUSM) driven by a dual mode modulation non-resonant driving circuit. First, the motor configuration and the proposed driving circuit of a TWUSM are introduced. To drive a TWUSM effectively, a novel driving circuit, that simultaneously employs both the driving frequency and phase modulation control scheme, is proposed to provide two-phase balance voltage for a TWUSM. Since the dynamic characteristics and motor parameters of the TWUSM are highly nonlinear and time-varying, a FNN control system is therefore investigated to achieve high-precision speed control. The proposed FNN control system incorporates neuro-fuzzy control and the driving frequency and phase modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented by a low-cost digital signal processor based microcontroller, hence reducing the system hardware size and cost. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a conventional proportional-integral control system. PMID:18501903

Chen, Tien-Chi; Yu, Chih-Hsien; Chen, Chun-Jung; Tsai, Mi-Ching

2008-07-01

182

Protein contact map prediction using multi-stage hybrid intelligence inference systems.  

PubMed

Proteins are one of the most important molecules in organisms. Protein function can be inferred from its 3D structure. The gap between the number of discovered protein sequences and the number of structures determined by the experimental methods is increasing. Accurate prediction of protein contact map is an important step toward the reconstruction of the protein's 3D structure. In spite of continuous progress in developing contact map predictors, highly accurate prediction is still unresolved problem. In this paper, we introduce a new predictor, JUSTcon, which consists of multiple parallel stages that are based on adaptive neuro-fuzzy inference System (ANFIS) and K nearest neighbors (KNNs) classifier. A smart filtering operation is performed on the final outputs to ensure normal connectivity behaviors of amino acids pairs. The window size of the filter is selected by a simple expert system. The dataset was divided into testing dataset of 50 proteins and training dataset of 450 proteins. The system produced an average accuracy of 45.2% for the sequence separation of six amino acids. In addition, JUSTcon outperformed SVMcon and PROFcon predictors in the cases of large separation distances. JUSTcon produced an average accuracy of 15% for the sequence separation of 24 amino acids after applying it on CASP9 targets. PMID:22079474

Abu-Doleh, Anas A; Al-Jarrah, Omar M; Alkhateeb, Asem

2012-02-01

183

A hybrid neuro-fuzzy analytical approach to mode choice of global logistics management  

Microsoft Academic Search

Abstract This paper presents a hybrid neuro-fuzzy methodology,to identify appropriate,global logistics (GL) operational modes used for global supply,chain management.,The proposed,methodological,framework,includes three main,developmental phases: (1) establishment of a GL strategic hierarchy, (2) formulation of GL-mode identification rules, and (3) develop- ment,of a GL-mode choice model. By integrating advanced,multi-criteria decision-making,(MCDM) techniques including fuzzy analytical hierarchy process (Fuzzy-AHP), Fuzzy-MCDM, and the technique for

Jiuh-biing Sheu

2008-01-01

184

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS  

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

185

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

186

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

187

Neuro-fuzzy approaches for pipeline condition assessment  

Microsoft Academic Search

Recent advances in electronics, transducers, ultrasonic and computing technologies, have led to the development of inspection systems for underground facilities such as water lines, sewer pipes, oil and gas pipelines. Recent inspection technologies have been developed that require no human entry into underground structures; they are now fully automated, from data acquisition to data analysis, and eventually to condition assessment,

S. Kumar; F. Taheri

2007-01-01

188

Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy  

NASA Astrophysics Data System (ADS)

SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.

Kurtulus, Bedri; Razack, Moumtaz

2010-02-01

189

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

190

Neuro-fuzzy chip to handle complex tasks with analog performance.  

PubMed

This paper presents a mixed-signal neuro-fuzzy controller chip which, in terms of power consumption, input-output delay, and precision, performs as a fully analog implementation. However, it has much larger complexity than its purely analog counterparts. This combination of performance and complexity is achieved through the use of a mixed-signal architecture consisting of a programmable analog core of reduced complexity, and a strategy, and the associated mixed-signal circuitry, to cover the whole input space through the dynamic programming of this core. Since errors and delays are proportional to the reduced number of fuzzy rules included in the analog core, they are much smaller than in the case where the whole rule set is implemented by analog circuitry. Also, the area and the power consumption of the new architecture are smaller than those of its purely analog counterparts simply because most rules are implemented through programming. The paper presents a set of building blocks associated to this architecture, and gives results for an exemplary prototype. This prototype, called multiplexing fuzzy controller (MFCON), has been realized in a CMOS 0.7 /spl mu/m standard technology. It has two inputs, implements 64 rules, and features 500 ns of input to output delay with 16-mW of power consumption. Results from the chip in a control application with a dc motor are also provided. PMID:18244584

de Jesus Navas-Gonzalez, R; Vidal-Verdu, F; Rodriguez-Vazquez, A

2003-01-01

191

Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications  

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

192

Assessment of uncertainty to estimate burned area from different spatial resolution satellite imagery using a neuro-fuzzy classifier  

NASA Astrophysics Data System (ADS)

Remote sensing data from different instruments (AVHRR, MODIS, LANDSAT) and spatial resolutions (30m, 500m, 1Km, 4Km) were used to assess the impact of the spatial resolution in burned area mapping. Uncertainty was estimated with a neuro-fuzzy classifier. High resolution remote sensing images (Landsat5/TM) and ground data were used initially to select diverse scenes affected by the fire. The study region was located in the north-west region of the Iberian Peninsula, where several fires occurred in August 2006. A pixel approach neuro-fuzzy classifier was designed to identify burned areas on those high resolution scenes but only using those bands in similar spectral region, comparable between sensors. The classifier was applied to all of the images in order to compute the burned area uncertainty driven by the image resolution. Results show the inverse relationship between the spatial resolution of the images and the burned areas in terms of uncertainty. Burned pixel neighbourhood conditions could be used by the classifier in order to improve uncertainty burned area estimations.

Rafael Garcia-Lazaro, Jose; Arbelo, Manuel; Moreno-Ruiz, Jose A.; Piedra-Fernandez, Ja

193

Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak.  

PubMed

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters in the urban area are important due to health impact. Artificial intelligent techniques are successfully used in modelling of highly complex and non-linear phenomena. In this study, adaptive neuro-fuzzy logic method has been proposed to estimate the impact of meteorological factors on SO2 and total suspended particular matter (TSP) pollution levels over an urban area. The model forecasts satisfactorily the trends in SO2 and TSP concentration levels, with performance between 75-90% and 69-80 %, respectively. PMID:16310825

Yildirim, Yilmaz; Bayramoglu, Mahmut

2006-06-01

194

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

195

Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISI D2 steel  

Microsoft Academic Search

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

196

Type Inference for COBOL Systems  

Microsoft Academic Search

Types are a good starting point for various software reengi- neering tasks. Unfortunately, programs requiring reengi- neering most desperately are written in languages without an adequate type system (such as COBOL). To solve this problem, we propose a method of automated type inference for these languages. The main ingredients are that if vari- ables are compared using some relational operator

Arie Van Deursen; Leon Moonen

1998-01-01

197

Identification of critical genes in microarray experiments by a Neuro-Fuzzy approach.  

PubMed

Gene expression profiling by microarray technology is usually difficult to interpret into a simpler pattern. One approach to resolve the complexity of gene expression profiles is the application of artificial neural networks (ANNs). A potential difficulty in this strategy, however, is that the non-linear nature of ANN makes it essentially a 'black-box' computation process. Addition of a fuzzy logic approach is useful because it can complement ANN by explicitly specifying membership function during computation. We employed a hybrid approach of neural network and fuzzy logic to further analyze a published microarray study of gene responses to eight bacteria in human macrophages. The original analysis by hierarchical clustering found common gene responses to all bacteria but did not address individual responses. Our method allowed exploration of the gene response of the host to individual bacterium. We implemented a two-layer, feed-forward neural network containing the principle of 'competitive learning' (i.e. 'winner-take-all'). The weights of the trained neural network were fed into a fuzzy logic inference system. A new measurement, called the impact rating (IR) was also introduced to explore the degree of importance of each gene. To assess the reliability of the IR value, a bootstrap re-sampling method was applied to the dataset and a confidence level for each IR was obtained. Our approach has successfully uncovered the unique features of host response to individual bacterium. Further, application of gene ontology (GO) annotation to the genes of high IR values in each response has suggested new biological pathways for individual host-pathogen interactions. PMID:16987708

Chen, Chin-Fu; Feng, Xin; Szeto, Jack

2006-10-01

198

Recognition of gestures in Arabic sign language using neuro-fuzzy systems  

Microsoft Academic Search

Hand gestures play an important role in communication between people during their daily lives. But the extensive use of hand gestures as a mean of communication can be found in sign languages. Sign language is the basic communication method between deaf people. A translator is usually needed when an ordinary person wants to communicate with a deaf one. The work

Omar M. Al-jarrah; Alaa Halawani

2001-01-01

199

A data-driven genetic neuro-fuzzy system to PVT properties prediction  

Microsoft Academic Search

Pressure-Volume-Temperature (PVT) properties are very important in reservoir engineering computations. There are many approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Soft computing techniques and especially artificial neural networks had been utilized in the last decade by researchers to develop more accurate PVT correlations. Unfortunately, the developed neural networks correlations are often limited providing

Amar Khoukhi; Saeed Alboukhitan

2010-01-01

200

Flood Forecasting in River System Using ANFIS  

SciTech Connect

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. [Dept. of Civil Eng., NIT, Silchar (India)

2010-10-26

201

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm  

Microsoft Academic Search

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

Sunil K. Sinha; Fakhri Karray

2002-01-01

202

Input selection for ANFIS learning  

Microsoft Academic Search

We present a quick and straightfoward way of input selection for neuro-fuzzy modeling using adaptive neuro-fuzzy inference systems (ANFIS). The method is tested on two real-world problems: the nonlinear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identification using the Box and Jenkins gas furnace data

Jyh-shing Roger Jang

1996-01-01

203

Performance enhancement of low-cost, high-accuracy, state estimation for vehicle collision prevention system using ANFIS  

NASA Astrophysics Data System (ADS)

In this paper, a low-cost navigation system that fuses the measurements of the inertial navigation system (INS) and the global positioning system (GPS) receiver is developed. First, the system's dynamics are obtained based on a vehicle's kinematic model. Second, the INS and GPS measurements are fused using an extended Kalman filter (EKF) approach. Subsequently, an artificial intelligence based approach for the fusion of INS/GPS measurements is developed based on an Input-Delayed Adaptive Neuro-Fuzzy Inference System (IDANFIS). Experimental tests are conducted to demonstrate the performance of the two sensor fusion approaches. It is found that the use of the proposed IDANFIS approach achieves a reduction in the integration development time and an improvement in the estimation accuracy of the vehicle's position and velocity compared to the EKF based approach.

Saadeddin, Kamal; Abdel-Hafez, Mamoun F.; Jaradat, Mohammad A.; Jarrah, Mohammad Amin

2013-12-01

204

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

205

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

206

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

207

Single board system for fuzzy inference  

NASA Technical Reports Server (NTRS)

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

Symon, James R.; Watanabe, Hiroyuki

1991-01-01

208

Hybrid Intelligent Systems for Stock Market Analysis  

Microsoft Academic Search

The use of intelligent systems for stock market predictions has been widely established. This paper deals with the application of hybridized soft computing techniques for automated stock market forecasting and trend analysis. We make use of a neural network for one day ahead stock forecasting and a neuro-fuzzy system for analyzing the trend of the predicted stock values. To demonstrate

Ajith Abraham; Baikunth Nath; P. K. Mahanti

2001-01-01

209

Application of interval and fuzzy techniques to integrated navigation systems  

Microsoft Academic Search

The paper deals with the development of a new algorithm to be used by an INS (Integrated Navigation System) for carrying out accurate position estimation for different types of surface vehicles, including cars and ships. The proposed algorithm combines a neuro-fuzzy Kalman filter with a map matching method, in order to improve the effective real-time system performance when a GPS

Antonio Tiano; Antonio Zirilli; Fausto Pizzocchero

2001-01-01

210

Inference problems in multilevel secure database management systems  

Microsoft Academic Search

An inference channel in a database is a means by which one can infer data classified at a high level from data classified at a low level. The in-ference problem is the problem of detecting and removing inference chan-nels. It is clear that inference problems are of vital interest to the designers and users of secure databases. Database management sys-tems

S. Jajodia; C. Meadows

1995-01-01

211

An Ada inference engine for expert systems  

NASA Technical Reports Server (NTRS)

The purpose is to investigate the feasibility of using Ada for rule-based expert systems with real-time performance requirements. This includes exploring the Ada features which give improved performance to expert systems as well as optimizing the tradeoffs or workarounds that the use of Ada may require. A prototype inference engine was built using Ada, and rule firing rates in excess of 500 per second were demonstrated on a single MC68000 processor. The knowledge base uses a directed acyclic graph to represent production lines. The graph allows the use of AND, OR, and NOT logical operators. The inference engine uses a combination of both forward and backward chaining in order to reach goals as quickly as possible. Future efforts will include additional investigation of multiprocessing to improve performance and creating a user interface allowing rule input in an Ada-like syntax. Investigation of multitasking and alternate knowledge base representations will help to analyze some of the performance issues as they relate to larger problems.

Lavallee, David B.

1986-01-01

212

Application of Soft Computing in Coherent Communications Phase Synchronization.  

National Technical Information Service (NTIS)

The use of soft computing techniques in coherent communications phase synchronization provides an alternative to analytical or hard computing methods. This paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for phase synchroniza...

J. T. Drake N. R. Prasad

2000-01-01

213

A new transformed input-domain ANFIS for highly nonlinear system modeling and prediction  

Microsoft Academic Search

In two or more-dimensional systems where the components of the sample data are strongly correlated, it is not proper to divide the input space into several subspaces without considering the correlation. In this paper, we propose the usage of the method of principal component in order to uncorrelate and remove any redundancy from the input space or the adaptive-neuro fuzzy

Elsaid Mohamed Abdelrahim; Takashi Yahagi

2001-01-01

214

ANFIS based modelling and control of non-linear systems : a tutorial  

Microsoft Academic Search

This work is an attempt to illustrate the utility and effectiveness of soft-computing approaches in handling the modelling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, and evolutionary algorithms) in a complementary hybrid framework for solving real world problems. The present work concentrates on the pioneering neuro-fuzzy

Mouloud Azzedine Denaï; Frank Palis; Abdelhafid Zeghbib

2004-01-01

215

Inference aggregation detection in database management systems  

Microsoft Academic Search

The author identifies inference aggregation and cardinality aggregation as two distinct aspects of the aggregation problem. He develops the concept of a semantic relationship graph to describe the relationships between data and then presents inference aggregation as the problem of finding alternative paths between vertices on the graph. He presents an algorithm for processing the semantic relationship graph to discover

T. H. Hinke

1988-01-01

216

Inference by replication in densely connected systems.  

PubMed

An efficient Bayesian inference method for problems that can be mapped onto dense graphs is presented. The approach is based on message passing where messages are averaged over a large number of replicated variable systems exposed to the same evidential nodes. An assumption about the symmetry of the solutions is required for carrying out the averages; here we extend the previous derivation based on a replica-symmetric- (RS)-like structure to include a more complex one-step replica-symmetry-breaking-like (1RSB-like) ansatz. To demonstrate the potential of the approach it is employed for studying critical properties of the Ising linear perceptron and for multiuser detection in code division multiple access (CDMA) under different noise models. Results obtained under the RS assumption in the noncritical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also observed. While the 1RSB ansatz is not required for the original problems, it was applied to the CDMA signal detection problem with a more complex noise model that exhibits RSB behavior, resulting in an improvement in performance. PMID:17995074

Neirotti, Juan P; Saad, David

2007-10-01

217

Inference by replication in densely connected systems  

SciTech Connect

An efficient Bayesian inference method for problems that can be mapped onto dense graphs is presented. The approach is based on message passing where messages are averaged over a large number of replicated variable systems exposed to the same evidential nodes. An assumption about the symmetry of the solutions is required for carrying out the averages; here we extend the previous derivation based on a replica-symmetric- (RS)-like structure to include a more complex one-step replica-symmetry-breaking-like (1RSB-like) ansatz. To demonstrate the potential of the approach it is employed for studying critical properties of the Ising linear perceptron and for multiuser detection in code division multiple access (CDMA) under different noise models. Results obtained under the RS assumption in the noncritical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also observed. While the 1RSB ansatz is not required for the original problems, it was applied to the CDMA signal detection problem with a more complex noise model that exhibits RSB behavior, resulting in an improvement in performance.

Neirotti, Juan P.; Saad, David [The Neural Computing Research Group, Aston University, Birmingham B4 7ET (United Kingdom)

2007-10-15

218

Evaluating functional network inference using simulations of complex biological systems  

Microsoft Academic Search

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

219

Understanding COBOL Systems using Inferred Types  

Microsoft Academic Search

In a typical COBOLprogram, the data division consists of 50% of the lines of code. Automatic type inference can help to un- derstand the large collections of variable declarations co n- tained therein, showing how variables are related based on their actual usage. The most problematic aspect of type infe r- ence is pollution, the phenomenon that types become too

Arie Van Deursen; Leon Moonen

1999-01-01

220

Grammatical Inference Methodology for Control Systems  

Microsoft Academic Search

Machine Learning is a computational methodology that provides automatic means of improving programmed tasks from experience. As a subfield of Machine Learning, Grammatical Inference (GI) attempts to learn structural models, such as grammars, from diverse data patterns, such as speech, artificial and natural languages, sequences provided by bioinformatics databases, amongst others. Here we are interested in identifying artificial languages from

ABOUBEKEUR HAMDI-CHERIF; CHAFIA KARA-MOHAMMED

2009-01-01

221

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

222

LOWER LEVEL INFERENCE CONTROL IN STATISTICAL DATABASE SYSTEMS  

SciTech Connect

An inference is the process of transforming unclassified data values into confidential data values. Most previous research in inference control has studied the use of statistical aggregates to deduce individual records. However, several other types of inference are also possible. Unknown functional dependencies may be apparent to users who have 'expert' knowledge about the characteristics of a population. Some correlations between attributes may be concluded from 'commonly-known' facts about the world. To counter these threats, security managers should use random sampling of databases of similar populations, as well as expert systems. 'Expert' users of the DATABASE SYSTEM may form inferences from the variable performance of the user interface. Users may observe on-line turn-around time, accounting statistics. the error message received, and the point at which an interactive protocol sequence fails. One may obtain information about the frequency distributions of attribute values, and the validity of data object names from this information. At the back-end of a database system, improved software engineering practices will reduce opportunities to bypass functional units of the database system. The term 'DATA OBJECT' should be expanded to incorporate these data object types which generate new classes of threats. The security of DATABASES and DATABASE SySTEMS must be recognized as separate but related problems. Thus, by increased awareness of lower level inferences, system security managers may effectively nullify the threat posed by lower level inferences.

Lipton, D.L.; Wong, H.K.T.

1984-02-01

223

Fuzzy exemplar-based inference system for flood forecasting  

NASA Astrophysics Data System (ADS)

Fuzzy inference systems have been successfully applied in numerous fields since they can effectively model human knowledge and adaptively make decision processes. In this paper we present an innovative fuzzy exemplar-based inference system (FEIS) for flood forecasting. The FEIS is based on a fuzzy inference system, with its clustering ability enhanced through the Exemplar-Aided Constructor of Hyper-rectangles algorithm, which can effectively simulate human intelligence by learning from experience. The FEIS exhibits three important properties: knowledge extraction from numerical data, knowledge (rule) modeling, and fuzzy reasoning processes. The proposed model is employed to predict streamflow 1 hour ahead during flood events in the Lan-Yang River, Taiwan. For the purpose of comparison the back propagation neural network (BPNN) is also performed. The results show that the FEIS model performs better than the BPNN. The FEIS provides a great learning ability, robustness, and high predictive accuracy for flood forecasting.

Chang, Li-Chiu; Chang, Fi-John; Tsai, Ya-Hsin

2005-02-01

224

New strategy combining backward inference with forward inference in monitoring and diagnosing techniques for hydrodynamic bearing-rotor systems  

Microsoft Academic Search

A new strategy for the techniques of monitoring and diagnosing hydrodynamic bearing-rotor systems is recommended in this paper. The strategy integrates the traditional technique of spectrum and statistics analysis (backward inference) with the application of knowledge and information of rotor-bearing dynamics and tribology (forward inference). This strategy has been used to develop a monitoring and diagnosing system for rotating machinery

You-Yun Zhang; You-Bai Xie

1994-01-01

225

Fuzzy inference to risk assessment on nuclear engineering systems  

Microsoft Academic Search

This paper presents a nuclear case study, in which a fuzzy inference system (FIS) is used as alternative approach in risk analysis. The main objective of this study is to obtain an understanding of the aging process of an important nuclear power system and how it affects the overall plant safety. This approach uses the concept of a pure fuzzy

Antonio César Ferreira Guimarães; Celso Marcelo Franklin Lapa

2007-01-01

226

Diagnosis of arthritis through fuzzy inference system.  

PubMed

Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process. PMID:20927572

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

2012-06-01

227

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

228

A New Neuro-FDS Definition for Indirect Adaptive Control of Unknown Nonlinear Systems Using a Method of Parameter Hopping  

Microsoft Academic Search

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

Yiannis S. Boutalis; Dimitris C. Theodoridis; Manolis A. Christodoulou

2009-01-01

229

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

230

Evaluation of fuzzy inference systems using fuzzy least squares  

NASA Technical Reports Server (NTRS)

Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.

Barone, Joseph M.

1992-01-01

231

A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke  

PubMed Central

Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of <10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke.

Roy, Serge H.; Cheng, M. Samuel; Chang, Shey-Sheen; Moore, John; De Luca, Gianluca; Nawab, S. Hamid; De Luca, Carlo J.

2010-01-01

232

Robust adaptive intelligent sliding model control for a class of uncertain chaotic systems with unknown time-delay  

Microsoft Academic Search

In this paper, a robust adaptive intelligent sliding model control (RAISMC) scheme for a class of uncertain chaotic systems\\u000a with unknown time-delay is proposed. A sliding surface dynamic is appropriately constructed to guarantee the reachability\\u000a of the specified sliding surface. Within this scheme, neuro-fuzzy network (NFN) is utilized to approximate the unknown continuous\\u000a function. The robust controller is an adaptive

Yousef Farid; Nooshin Bigdeli

233

Training of fuzzy inference systems by combining variable structure systems technique and Levenberg-Marquardt algorithm  

Microsoft Academic Search

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

234

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

Microsoft Academic Search

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

235

Modified ANFIS architecture - improving efficiency of ANFIS technique  

Microsoft Academic Search

Adaptive neuro-fuzzy inference systems (ANFIS), fusing the capabilities of artificial neural networks and fuzzy inference systems, offer a lot of space for solving different kinds of problems, and are especially efficient in the domain of signal prediction. However, the ANFIS technique is sometimes notated as being computationally expensive. The paper, after considering the conventional ANFIS architecture, brings up a modified

Branimir B. JovanoviC; Irini S. Reljin; Branimir D. Reljin

2004-01-01

236

Power system dynamic load modeling using adaptive-network-based fuzzy inference system  

Microsoft Academic Search

The representation of the dynamic characteristics of power system loads is widely used for obtaining power system operations, controls and stability limits and becomes a critical factor in power system dynamic performance. In this paper, the performance of power system dynamic load modeling using adaptive-network-base fuzzy inference system (ANFIS) is compared with traditional architectures. The ANFIS models can represent nonlinear

A. Oonsivilai; M. E. El-Hawary

1999-01-01

237

Automatic control of biomass gasifiers using fuzzy inference systems.  

PubMed

A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated. PMID:16697183

Sagüés, C; García-Bacaicoa, P; Serrano, S

2007-03-01

238

A Hypermedia Inference Language as an Alternative to Rule-based Expert Systems  

Microsoft Academic Search

This paper reports on the development of a hypermedia inference language designed to strengthen the ability of hypermedia systems to be used effectively in applications that might otherwise require cumbersome rule-based expert systems. The inference language grew out of a primitive query language which provided the mechanism for navigation in a hypertext system. As the language gained logical and computational

Gerry Stahl

1991-01-01

239

Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System  

NASA Astrophysics Data System (ADS)

Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.

Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.

2009-04-01

240

Neuro-fuzzy based constraint programming  

Microsoft Academic Search

Constraint programming models appear in many sciences including mathematics, engineering and physics. These problems aim at optimizing a cost function joint with some constraints. Fuzzy constraint programming has been developed for treating uncertainty in the setting of optimization problems with vague constraints. In this paper, a new method is presented into creation fuzzy concept for set of constraints. Unlike to

Hadi Sadoghi Yazdi; S. E. Hosseini; Mehri Sadoghi Yazdi

2010-01-01

241

Prediction of Earth rotation parameters by fuzzy inference systems  

NASA Astrophysics Data System (ADS)

The short-term prediction of Earth rotation parameters (ERP) (length-of-day and polar motion) is studied up to 10 days by means of ANFIS (adaptive network based fuzzy inference system). The prediction is then extended to 40 days into the future by using the formerly predicted values as input data. The ERP C04 time series with daily values from the International Earth Rotation Service (IERS) serve as the data base. Well-known effects in the ERP series, such as the impact of the tides of the solid Earth and the oceans or seasonal variations of the atmosphere, were removed a priori from the C04 series. The residual series were used for both training and validation of the network. Different network architectures are discussed and compared in order to optimize the network solution. The results of the prediction are analyzed and compared with those of other methods. Short-term ERP values predicted by ANFIS show root-mean-square errors which are equal to or even lower than those from the other considered methods. The presented method is easy to use.

Akyilmaz, O.; Kutterer, H.

2004-09-01

242

Perturbation Biology: Inferring Signaling Networks in Cellular Systems  

PubMed Central

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.

Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2013-01-01

243

DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction  

Microsoft Academic Search

This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised\\/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and

Nikola K. Kasabov; Qun Song

2002-01-01

244

An integrated fuzzy inference based monitoring, diagnostic, and prognostic system  

NASA Astrophysics Data System (ADS)

To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable "grab bag" of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1.2-11.4 hours with 95% confidence intervals (CI) from 0.67-32.02 hours, which are significantly better than the population based prognoser estimates with errors of ˜45 hours and 95% CIs of ˜162 hours.

Garvey, Dustin

245

The Dynamic Behavioral Model of RF Power Amplifiers With the Modified ANFIS  

Microsoft Academic Search

The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems including RF power amplifiers (PAs). The modified ANFIS (MANFIS) architecture is simpler than that of ANFIS, but with nearly the same performance for modeling nonlinear systems. In this paper, the MANFIS is applied to model RF PAs with memory effects. The simulation and

Jianfeng Zhai; Jianyi Zhou; Lei Zhang; Jianing Zhao; Wei Hong

2009-01-01

246

A Modular Artificial Intelligence Inference Engine System (MAIS) for support of on orbit experiments  

NASA Technical Reports Server (NTRS)

This paper describes a Modular Artificial Intelligence Inference Engine System (MAIS) support tool that would provide health and status monitoring, cognitive replanning, analysis and support of on-orbit Space Station, Spacelab experiments and systems.

Hancock, Thomas M., III

1994-01-01

247

A computer simulator for steel plant electrical arc furnace regulator  

Microsoft Academic Search

The function of the simulator is to imitate the behavior of the regulator loop, which is the main component of the Electrical Arc Furnace (EAF) control systems. In the past, the use of artificial intelligence methods, and in particular, the Adaptive Neuro Fuzzy Inference System (ANFIS) were successfully applied in the modeling and control of the EAF components individually. This

Behzad Jorjani

2006-01-01

248

Moving attention from the road: A new methodology for the driver distraction evaluation using machine learning approaches  

Microsoft Academic Search

This work describes an approach to develop a model of driver's distraction induced by an on-board information system basing on vehicle data. Machine learning techniques have been adopted to find the model able to better predict distraction, given a target value. Results pointed in favor of a model obtained with the ANFIS (adaptive neuro fuzzy inference system) technique. Further investigations

Fabio Tango; Caterina Calefato; Luca Minin; Luca Canovi

2009-01-01

249

Hybrid intelligent scenario generator for business strategic planning by using ANFIS  

Microsoft Academic Search

The aim of this study is to investigate a new method for generating scenarios in order to cope with the data shortage and linguistic expression of experts in scenario planning. The proposed hybrid intelligent scenario generator uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to deal with uncertain inputs. In this methodology, the strengths of expert systems, fuzzy logic and Artificial

Sorousha Moayer; Parisa A. Bahri

2009-01-01

250

Clinical Decision Support Systems: a Review of Knowledge Representation and Inference under Uncertainties  

Microsoft Academic Search

This paper provides a literature review in clinical decision support systems (CDSSs) with a focus on the way knowledge bases are constructed, and how inference mechanisms and group decision making methods are used in CDSSs. Particular attention is paid to the uncertainty handling capability of the commonly used knowledge representation and inference schemes. The definition of what constitute good CDSSs

GUILAN KONG; DONG-LING XU; JIAN-BO YANG

2008-01-01

251

Controlling FD and MVD Inferences in Multilevel Relational Database Systems  

Microsoft Academic Search

The authors investigate the inference problems due to functional dependencies (FD) and multivalued dependencies (MVD) in a multilevel relational database (MDB) with attribute and record classification schemes, respectively. The set of functional dependencies to be taken into account in order to prevent FD-compromises is determined. It is proven that incurring minimum information loss to prevent compromises is an NP-complete problem.

Tzong-an Su; Gultekin Özsoyoglu

1991-01-01

252

Intellec System: Shell for expert systems creation with fuzzy inference machine developed in prolog  

Microsoft Academic Search

This article presents the implementation of a Shell for the development of Fuzzy Specialist Systems. This Shell is being developed using the C++ and Visual Prolog languages. It allows the creation of a group of fuzzy rules. The Visual Prolog language is being used for the implementation of the inference engine. The C ++ is being used for the development

C. R. Pamplona Filho; M. J. Cunha; F. M. de Azevedo; G. L. Ferrari

2010-01-01

253

DYNAMICAL INFERENCE FROM A KINEMATIC SNAPSHOT: THE FORCE LAW IN THE SOLAR SYSTEM  

SciTech Connect

If a dynamical system is long-lived and non-resonant (that is, if there is a set of tracers that have evolved independently through many orbital times), and if the system is observed at any non-special time, it is possible to infer the dynamical properties of the system (such as the gravitational force or acceleration law) from a snapshot of the positions and velocities of the tracer population at a single moment in time. In this paper, we describe a general inference technique that solves this problem while allowing (1) the unknown distribution function of the tracer population to be simultaneously inferred and marginalized over, and (2) prior information about the gravitational field and distribution function to be taken into account. As an example, we consider the simplest problem of this kind: we infer the force law in the solar system using only an instantaneous kinematic snapshot (valid at 2009 April 1.0) for the eight major planets. We consider purely radial acceleration laws of the form a{sub r} = -A [r/r{sub 0}]{sup -a}lpha, where r is the distance from the Sun. Using a probabilistic inference technique, we infer 1.989 < alpha < 2.052 (95% interval), largely independent of any assumptions about the distribution of energies and eccentricities in the system beyond the assumption that the system is phase-mixed. Generalizations of the methods used here will permit, among other things, inference of Milky Way dynamics from Gaia-like observations.

Bovy, Jo; Hogg, David W. [Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003 (United States); Murray, Iain, E-mail: jo.bovy@nyu.ed [Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4 (Canada)

2010-03-10

254

A Hypermedia Inference Language as an Alternative to Rule-based Expert Systems  

Microsoft Academic Search

Abstract This paper,reports on the development,of a hypermedia,inference language,designed,to strengthen the ability of hypermedia systems to be used effectively in applications that might otherwise require cumbersome,rule-based expert systems. The inference language,grew out of a primitive query language,which provided the mechanism,for navigation in a hypertext system. As the language gained logical and computational capabilities it became increasingly embedded in the nodes

G. Stahl; R. Mccall; G. Peper

1992-01-01

255

Predicting foaming slag quality in electric arc furnace using power quality indices and ANFIS  

Microsoft Academic Search

Foaming slag quality is an important parameter that can be used to improve the efficiency and quality of electric arc furnace process. However due to its fast and unpredictable changes, its quality is difficult to control. In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) is used to determine slag quality based on power quality indices in electric arc

Amir Parsapoor; Behzad Mirzaeian Dehkordi; Mehdi Moallem

2010-01-01

256

Predicting Foaming Slag Quality in Electric Arc Furnace Using Power Quality Indices and Fuzzy Method  

Microsoft Academic Search

In this paper, a new method based on adaptive neuro fuzzy inference system (ANFIS) and fuzzy logic is presented to de- termine the slag quality in electric arc furnace using power quality indices. To train ANFIS, all electrical power quality parameters are measured for 13 meltings using a power quality analyzer. Twelve different sets of power quality parameters are examined

Behzad Mirzaeian Dehkordi; Mehdi Moallem; Amir Parsapour

2011-01-01

257

Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs  

Microsoft Academic Search

This paper presents a novel method for fault diagnosis based on empirical mode decomposition (EMD), an improved distance evaluation technique and the combination of multiple adaptive neuro-fuzzy inference systems (ANFISs). The method consists of three stages. First, prior to feature extraction, some preprocessing techniques, like filtration, demodulation and EMD are performed on vibration signals to acquire more fault characteristic information.

Yaguo Lei; Zhengjia He; Yanyang Zi; Qiao Hu

2007-01-01

258

Application of ANFIS to Phase Estimation for Multiple Phase Shift Keying.  

National Technical Information Service (NTIS)

The paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for estimating phase in Multiple Phase Shift Keying (M-PSK) modulation. A brief overview of communications phase estimation is provided. The modeling of both general open-lo...

J. T. Drake N. R. Prasad

2000-01-01

259

Applying nonlinear generalized autoregressive conditional heteroscedasticity to compensate ANFIS outputs tuned by adaptive support vector regression  

Microsoft Academic Search

Volatility clustering degrades the efficiency and effectiveness of time series prediction and gives rise to large residual errors. This is because volatility clustering suggests a time series where successive disturbances, even if uncorrelated, are yet serially dependent. To overcome volatility clustering problems, an adaptive neuro-fuzzy inference system (ANFIS) is combined with a nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) model that

Bao Rong Chang

2006-01-01

260

Transmission line distance protection using ANFIS and positive sequence components  

Microsoft Academic Search

Conventional distance relays are affected by variables such as source impedance, power angle, fault resistance, etc. This paper presents a new scheme for distance relay using adaptive neuro fuzzy inference system (ANFIS) and positive sequence components of voltage\\/current waveforms that can reduce the impact of those variables. The change of currents is used for the detection of faults on transmission

Hassan Khorashadi-Zadeh; Zuyi Li

2007-01-01

261

Control and Optimization of a Sensor Manufacturing Process  

Microsoft Academic Search

This paper presents a feedback control optimization framework for a sensor development and fabrication process. The existing sensor adaptive neuro-fuzzy inference system intelligent model was configured in a closed-loop feedback control framework to optimally automate the sensor manufacturing process. Three main sensor manufacturing components were assumed to be the process inputs while the sensor relative optical density was considered as

Muhittin Yilmaz

2011-01-01

262

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

263

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

264

Extending Hypermedia with an Inference Language: an Alternative to Rule-Based Expert Systems  

Microsoft Academic Search

Abstract Hypermedia,systems can provide an alternative to expert systems in domains that call for navigation rather than inference. This paper reports on research to extend the hypermedia model to include an inferencing capability, so that the benefits of this approach can be gained in applications that require a mix of navigation and inference. An application in the domain,of academic,advising was,developed,to

G Stahl; R Mccall; G Peper

1992-01-01

265

Cloud-computing-based framework for multi-camera topology inference in smart city sensing system  

Microsoft Academic Search

This paper proposes a cloud-computing-based algorithmic framework which is scalable and adaptive to online smart city video sensing system. One of the most cost-expensive works in such a system is to infer the topology structure of video camera network, thus spatio-temporal relationship inference for large-scale camera network is simulated on a cloud-computing platform to validate the proposed framework. The simulation

Ye Wen; Xiaokang Yang; Yi Xu

2010-01-01

266

Inferring the Gibbs state of a small quantum system  

SciTech Connect

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

267

EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment.  

PubMed

Motion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of ~82%. PMID:24808604

Chin-Teng Lin; Shu-Fang Tsai; Li-Wei Ko

2013-10-01

268

Parameter and Structure Inference for Nonlinear Dynamical Systems  

NASA Technical Reports Server (NTRS)

A great many systems can be modeled in the non-linear dynamical systems framework, as x = f(x) + xi(t), where f() is the potential function for the system, and xi is the excitation noise. Modeling the potential using a set of basis functions, we derive the posterior for the basis coefficients. A more challenging problem is to determine the set of basis functions that are required to model a particular system. We show that using the Bayesian Information Criteria (BIC) to rank models, and the beam search technique, that we can accurately determine the structure of simple non-linear dynamical system models, and the structure of the coupling between non-linear dynamical systems where the individual systems are known. This last case has important ecological applications.

Morris, Robin D.; Smelyanskiy, Vadim N.; Millonas, Mark

2006-01-01

269

Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems  

Microsoft Academic Search

Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS has significantly more descriptive power than an HMM by using continuous hidden states. However, the use of SLDS models in practical applications is challenging for several reasons. First, exact inference in SLDS models is computationally intractable. Second, the geometric duration model induced

Sang Min Oh; James M. Rehg; Tucker R. Balch; Frank Dellaert

2008-01-01

270

Seizure detection in intracranial EEG using a fuzzy inference system.  

PubMed

In this paper, we present a fuzzy rule-based system for the automatic detection of seizures in the intracranial EEG (IEEG) recordings. A total of 302.7 hours of the IEEG with 78 seizures, recorded from 21 patients aged between 10 and 47 years were used for the evaluation of the system. After preprocessing, temporal, spectral, and complexity features were extracted from the segmented IEEGs. The results were thresholded using the statistics of a reference window and integrated spatio-temporally using a fuzzy rule-based decision making system. The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11 s. The results from the automatic system correlate well with the visual analysis of the seizures by the expert. This system may serve as a good seizure detection tool for monitoring long-term IEEG with relatively high sensitivity and low false detection rate. PMID:19963525

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

2009-01-01

271

FINDS: A fault inferring nonlinear detection system. User's guide  

NASA Technical Reports Server (NTRS)

The computer program FINDS is written in FORTRAN-77, and is intended for operation on a VAX 11-780 or 11-750 super minicomputer, using the VMS operating system. The program detects, isolates, and compensates for failures in navigation aid instruments and onboard flight control and navigation sensors of a Terminal Configured Vehicle aircraft in a Microwave Landing System environment. In addition, FINDS provides sensor fault tolerant estimates for the aircraft states which are then used by an automatic guidance and control system to land the aircraft along a prescribed path. FINDS monitors for failures by evaluating all sensor outputs simultaneously using the nonlinear analytic relationships between the various sensor outputs arising from the aircraft point mass equations of motion. Hence, FINDS is an integrated sensor failure detection and isolation system.

Lancraft, R. E.; Caglayan, A. K.

1983-01-01

272

Lab-on-a-Chip Molecular Inference System  

Microsoft Academic Search

Molecular computation on DNA performs calculations using nan- otechnology means during chemical reactions. 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-on-a-chips. This work presents an approach to implementation of logic systems on chips. It requires the unique represen- tation of

Piotr Wasiewicz; Jan J. Mulawka

273

Inferring Personal Information from Demand-Response Systems  

Microsoft Academic Search

Current and upcoming demand-response systems provide increasingly detailed power-consumption data to utilities and a growing array of players angling to assist consumers in understanding and managing their energy use. The granularity of this data, as well as new players' entry into the energy market, creates new privacy concerns. The detailed per-household consumption data that advanced metering systems generate reveals information

Mikhail A. Lisovich; Deirdre K. Mulligan; Stephen B. Wicker

2010-01-01

274

Fuzzy Inference Integrating 3D Measuring System with Adaptive Sensing Strategy  

Microsoft Academic Search

This chapter deals with a 3-D measurement system applied to a curved metal surface carving system, and a sensor integration\\u000a method based on fuzzy inference and adaptive sensing strategy. The measurement system consists of two different sensors. One\\u000a is a LED displacement sensor, while the other is a vision system. The LED displacement sensor’s spot-light is used as a part

Koji Shimojima; Toshio Fukuda; Fumihito Arai; Hideo Matsuura

275

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

276

Operational Risk Management using a Fuzzy Logic Inference System  

Microsoft Academic Search

Operational Risk (OR) results from endogenous and exogenous risk factors, as diverse and complex to assess as human resources and technology, which may not be properly measured using traditional quantitative approaches. Engineering has faced the same challenges when designing practical solutions to complex multifactor and non-linear systems where human reasoning, expert knowledge or imprecise information are valuable inputs. One of

Alejandro Reveiz; Carlos Léon

2009-01-01

277

Operational Risk Management Using a Fuzzy Logic Inference System  

Microsoft Academic Search

Operational Risk (OR) results from endogenous and exogenous risk factors, as diverse and complex to assess as human resources and technology, which may not be properly measured using traditional quantitative approaches. Engineering has faced the same challenges when designing practical solutions to complex multifactor and non-linear systems where human reasoning, expert knowledge, and imprecise information are valuable inputs. One of

Alejandro Reveiz; Leon Carlos

2010-01-01

278

Streamflow Forecasting Using Nuero-Fuzzy Inference System  

Microsoft Academic Search

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall

U. V. Nanduri; P. C. Swain

2005-01-01

279

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

Microsoft Academic Search

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

280

Patent analysis-based fuzzy inference system for technological strategy planning  

Microsoft Academic Search

This paper describes a technological strategy planning method that integrates patent analysis techniques with a fuzzy inference system (FIS). The method differentiates itself from the traditional technological management decision-making tools in its knowledge base. Instead of eliciting knowledge from domain experts, the proposed method adopts global patent databases as sources of knowledge for strategy planning. The patent analysis techniques are

W.-D. Yu; S.-S. Lo

2009-01-01

281

Functional equivalence between radial basis function networks and fuzzy inference systems  

Microsoft Academic Search

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

282

Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series  

Microsoft Academic Search

The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA)

M. EROL KESKIN; DILEK TAYLAN; ÖZLEM TERZI

2006-01-01

283

Large-Scale Optimization for Bayesian Inference in Complex Systems  

SciTech Connect

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; Marzouk, Youssef [MIT

2013-11-12

284

Depth of anesthesia estimation by adaptive-network-based fuzzy inference system  

Microsoft Academic Search

One effective way to estimate the depth of anesthesia (DOA) from EEG is proposed. The scheme applies an adaptive-network-based fuzzy inference system (ANFIS) to integrate the extracted EEG characteristics such as complexity measure, approximate entropy, and spectral edge frequency for decision-making. The system was trained and tested using EEG data collected from three dog experiments under propofol anesthesia. The accuracy

Xu-Sheng Zhang; R. J. Roy

1999-01-01

285

Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster  

PubMed Central

Background Reaction-diffusion systems are frequently used in systems biology to model developmental and signalling processes. In many applications, count numbers of the diffusing molecular species are very low, leading to the need to explicitly model the inherent variability using stochastic methods. Despite their importance and frequent use, parameter estimation for both deterministic and stochastic reaction-diffusion systems is still a challenging problem. Results We present a Bayesian inference approach to solve both the parameter and state estimation problem for stochastic reaction-diffusion systems. This allows a determination of the full posterior distribution of the parameters (expected values and uncertainty). We benchmark the method by illustrating it on a simple synthetic experiment. We then test the method on real data about the diffusion of the morphogen Bicoid in Drosophila melanogaster. The results show how the precision with which parameters can be inferred varies dramatically, indicating that the ability to infer full posterior distributions on the parameters can have important experimental design consequences. Conclusions The results obtained demonstrate the feasibility and potential advantages of applying a Bayesian approach to parameter estimation in stochastic reaction-diffusion systems. In particular, the ability to estimate credibility intervals associated with parameter estimates can be precious for experimental design. Further work, however, will be needed to ensure the method can scale up to larger problems.

2010-01-01

286

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

287

Physical connectivity in the Mesoamerican Barrier Reef System inferred from 9 years of ocean color observations  

Microsoft Academic Search

Ocean color images acquired from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) from 1998 to 2006 were used to examine\\u000a the patterns of physical connectivity between land and reefs, and among reefs in the Mesoamerican Barrier Reef System (MBRS)\\u000a in the northwestern Caribbean Sea. Connectivity was inferred by tracking surface water features in weekly climatologies and\\u000a a time series of weekly

I. Soto; S. Andréfouët; C. Hu; F. E. Muller-Karger; C. C. Wall; J. Sheng; B. G. Hatcher

2009-01-01

288

Predicting Spring rainfall Based on Remote Linkage controlling using Adaptive Neural-Fuzzy Inference System (ANFIS)  

NASA Astrophysics Data System (ADS)

This paper aims to study the relationship between climatic large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neural-fuzzy inference system 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 1000-mb level, the temperature of 700-mb level, the thickness between 500 and 1000-mb levels, the relative humidity of 300-mb level and precipitable water. In the selection of these regions, which include a number of locations in the Persian Gulf, the Oman Sea, the Black Sea, the Caspian, the Mediterranean, the Adriatic, the Red Sea, the Eden Gulf, the Atlantic, the Indian Ocean, and Siberia, we have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the adaptive neural-fuzzy inference system in the period 1970 -1997 has been taught. Finally, the rainfall in the period 1998-2007 has been predicted. The results show that the adaptive neural-fuzzy inference system can predict the rainfall with reasonable accuracy in 90 percent of the years

Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.

2009-04-01

289

Inference system using softcomputing and mixed data applied in metabolic pathway datamining.  

PubMed

This paper describes the development of an inference system used for the identification of genes that encode enzymes of metabolic pathways. Input sequence alignment values are used to classify the best candidate genes for inclusion in a metabolic pathway map. The system workflow allows the user to provide feedback, which is stored in conjunction with analysed sequences for periodic retraining. The construction of the system involved the study of several different classifiers with various topologies, data sets and parameter normalisation data models. Experimental results show an excellent prediction capability with the classifiers trained with mixed data providing the best results. PMID:22479819

Arredondo, Tomás; Candel, Diego; Leiva, Mauricio; Dombrovskaia, Lioubov; Agulló, Loreine; Seeger, Michael

2012-01-01

290

Evaluation of Probabilistic and Logical Inference for a SNP Annotation System  

PubMed Central

Genome wide association studies (GWAS) are an important approach to understanding the genetic mechanisms behind human diseases. Single nucleotide polymorphisms (SNPs) are the predominant markers used in genome wide association studies, and the ability to predict which SNPs are likely to be functional is important for both a priori and a posteriori analyses of GWA studies. This article describes the design, implementation and evaluation of a family of systems for the purpose of identifying SNPs that may cause a change in phenotypic outcomes. The methods described in this article characterize the feasibility of combinations of logical and probabilistic inference with federated data integration for both point and regional SNP annotation and analysis. Evaluations of the methods demonstrate the overall strong predictive value of logical, and logical with probabilistic, inference applied to the domain of SNP annotation.

Shen, Terry H.; Tarczy-Hornoch, Peter; Detwiler, Landon T.; Cadag, Eithon; Carlson, Christopher S.

2010-01-01

291

Video-based cargo fire verification system with fuzzy inference engine for commercial aircraft  

NASA Astrophysics Data System (ADS)

Conventional smoke detection systems currently installed onboard aircraft are often subject to high rates of false alarms. Under current procedures, whenever an alarm is issued the pilot is obliged to release fire extinguishers and to divert to the nearest airport. Aircraft diversions are costly and dangerous in some situations. A reliable detection system that minimizes false-alarm rate and allows continuous monitoring of cargo compartments is highly desirable. A video-based system has been recently developed by Goodrich Corporation to address this problem. The Cargo Fire Verification System (CFVS) is a multi camera system designed to provide live stream video to the cockpit crew and to perform hotspot, fire, and smoke detection in aircraft cargo bays. In addition to video frames, the CFVS uses other sensor readings to discriminate between genuine events such as fire or smoke and nuisance alarms such as fog or dust. A Mamdani-type fuzzy inference engine is developed to provide approximate reasoning for decision making. In one implementation, Gaussian membership functions for frame intensity-based features, relative humidity, and temperature are constructed using experimental data to form the system inference engine. The CFVS performed better than conventional aircraft smoke detectors in all standardized tests.

Sadok, Mokhtar; Zakrzewski, Radek; Zeliff, Bob

2005-02-01

292

Nested sampling for parameter inference in systems biology: application to an exemplar circadian model  

PubMed Central

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.

2013-01-01

293

Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System  

NASA Astrophysics Data System (ADS)

This paper presents a study on welding penetration control of fixed pipe in Tungsten Inert Gas (TIG) welding using fuzzy inference system. The welding penetration control is essential to the production quality welds with a specified geometry. For pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small diameter pipes progresses. Having welded pipe in fixed position, obviously, the excessive arc current yields burn through of metals; in contrary, insufficient arc current produces imperfect welding. In order to avoid these errors and to obtain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Simulation of welding control using fuzzy inference system was constructed to simulate the welding control process. The simulation result shows that fuzzy controller was suitable for controlling the welding speed and appropriate to be implemented into the welding system. A series of experiments was conducted to evaluate the performance of the fuzzy controller. The experimental results show the effectiveness of the control system that is confirmed by sound welds.

Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo

294

Development of rainfall runoff models using Takagi Sugeno fuzzy inference systems  

NASA Astrophysics Data System (ADS)

SummaryThis study explores the application of Takagi-Sugeno fuzzy inference systems to rainfall-runoff modelling. The models developed intend to describe the non-linear relationship between rainfall as input and runoff as output to the real system using a system based approach. Two types of fuzzy models are proposed, where the first type is intended to account for the effect of changes in catchment wetness in the rainfall-runoff transformation and the second type incorporates seasonality as a source of non-linearity in this relationship. The models developed are applied to data from six catchments of diverse climatic characteristics. The results of the fuzzy models are compared with those of the Simple Linear Model, the Linear Perturbation Model and the Nearest Neighbour Linear Perturbation Model, which use similar input information. The results of this study indicate that fuzzy inference systems are a suitable alternative to the traditional methods for modelling the non-linear relationship between rainfall and runoff.

Jacquin, Alexandra P.; Shamseldin, Asaad Y.

2006-09-01

295

Multiplex genotyping system for efficient inference of matrilineal genetic ancestry with continental resolution  

PubMed Central

Background In recent years, phylogeographic studies have produced detailed knowledge on the worldwide distribution of mitochondrial DNA (mtDNA) variants, linking specific clades of the mtDNA phylogeny with certain geographic areas. However, a multiplex genotyping system for the detection of the mtDNA haplogroups of major continental distribution that would be desirable for efficient DNA-based bio-geographic ancestry testing in various applications is still missing. Results Three multiplex genotyping assays, based on single-base primer extension technology, were developed targeting a total of 36 coding-region mtDNA variants that together differentiate 43 matrilineal haplo-/paragroups. These include the major diagnostic haplogroups for Africa, Western Eurasia, Eastern Eurasia and Native America. The assays show high sensitivity with respect to the amount of template DNA: successful amplification could still be obtained when using as little as 4 pg of genomic DNA and the technology is suitable for medium-throughput analyses. Conclusions We introduce an efficient and sensitive multiplex genotyping system for bio-geographic ancestry inference from mtDNA that provides resolution on the continental level. The method can be applied in forensics, to aid tracing unknown suspects, as well as in population studies, genealogy and personal ancestry testing. For more complete inferences of overall bio-geographic ancestry from DNA, the mtDNA system provided here can be combined with multiplex systems for suitable autosomal and, in the case of males, Y-chromosomal ancestry-sensitive DNA markers.

2011-01-01

296

Intelligent AVR and PSS with Adaptive hybrid learning algorithm  

Microsoft Academic Search

The paper presents a step-by-step design methodology of an adaptive neuro-fuzzy inference system (ANFIS) based automatic voltage regulator (AVR) and power system stabilizer (PSS) and also demonstrates its performance in a single-machine-infinite-bus and a multi-machine power system through digital simulation. The design employs a zero and a first order Sugeno fuzzy model, whose parameters are tuned off-line through hybrid learning

P. Mitra; S. P. Chowdhury; S. K. Pal; P. A. Crossley

2008-01-01

297

A novel approach for ANFIS modelling based on full factorial design  

Microsoft Academic Search

Adaptive neural network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modelling and control of ill-defined and uncertain systems. ANFIS is based on the input–output data pairs of the system under consideration. The size of the input–output data set is very crucial when the data available is very less and the generation of data is a

Mrinal Buragohain; Chitralekha Mahanta

2008-01-01

298

Type 1 Diabetes Regulated by ANFIS at Molecular Levels  

Microsoft Academic Search

\\u000a Soft computing based controller is designed for Type 1 diabetes modeled at molecular levels. Rough rule base is generated\\u000a with subtractive clustering which is followed by its refinement by parameter tuning. As a result, an Adaptive Neuro-Fuzzy\\u000a Inference System (ANFIS) is created. Simulation results are in accordance with the behavior of the healthy human blood glucose\\u000a system.

L. Kovács; A. György; B. Benyó; A. Kovács

299

Levenberg-Marquardt method for ANFIS learning  

Microsoft Academic Search

Presents the results of applying the Levenberg-Marquardt method (K. Levenberg, 1944, and D.W. Marquardt, 1963), which is a popular nonlinear least-squares method, to the ANFIS (Adaptive Neuro-Fuzzy Inference System) architecture proposed by Jang (IEEE Trans. on Systems, Man and Cybernctics, vol. 23, no. 3, pp 665-685, May 1993). Through empirical studies, we discuss the strengths and weaknesses of using such

Jyh-Shing Roger Jang; E. Mizutani

1996-01-01

300

Rule-base derivation for intensive care ventilator control using ANFIS  

Microsoft Academic Search

In recent years, much research has been done on the use of fuzzy systems in medicine. The fuzzy rule-bases have usually been derived after extensive discussion with the clinical experts. This takes a lot of time from the clinical experts and the knowledge engineers. This paper presents the use of the adaptive neuro-fuzzy inference system (ANFIS) in rule-base derivation for

H.-F. Kwok; Derek A. Linkens; Mahdi Mahfouf; G. H. Mills

2003-01-01

301

ANFIS-based diagnosis and location of stator interturn faults in PM brushless DC motors  

Microsoft Academic Search

An automatic scheme for fault diagnosis and location of stator-winding interturns in permanent-magnet brushless dc motors is presented. System performances under healthy and faulty operation are obtained via a discrete-time model. Waveform of the electromagnetic torque is monitored and processed using discrete Fourier transform and short-time Fourier transform to derive proper diagnostic indices. Two adaptive neuro-fuzzy inference systems (ANFIS) are

M. A. Awadallah; M. M. Morcos

2004-01-01

302

Predicting injection profiles using ANFIS  

Microsoft Academic Search

Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfields’ performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptive-network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems are presented. The two

Mingzhen Wei; Baojun Bai; Andrew H. Sung; Qingzhong Liu; Jiachun Wang; Martha E. Cather

2007-01-01

303

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

304

An adaptive predictor for dynamic system forecasting  

NASA Astrophysics Data System (ADS)

A reliable and real-time predictor is very useful to a wide array of industries to forecast the behaviour of dynamic systems. In this paper, an adaptive predictor is developed based on the neuro-fuzzy approach to dynamic system forecasting. An adaptive training technique is proposed to improve forecasting performance, accommodate different operation conditions, and prevent possible trapping due to local minima. The viability of the developed predictor is evaluated by using both gear system condition monitoring and material fatigue testing. The investigation results show that the developed adaptive predictor is a reliable and robust forecasting tool. It can capture the system's dynamic behaviour quickly and track the system's characteristics accurately. Its performance is superior to other classical forecasting schemes.

Wang, Wilson

2007-02-01

305

Design considerations for flight test of a fault inferring nonlinear detection system algorithm for avionics sensors  

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

306

Design considerations for flight test of a fault inferring nonlinear detection system algorithm for avionics sensors  

NASA Technical Reports Server (NTRS)

This paper summarizes the modifications made 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. 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 an update frequency, are also presented.

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

1986-01-01

307

Regulatory interactions for iron homeostasis in Aspergillus fumigatus inferred by a Systems Biology approach  

PubMed Central

Background In System Biology, iterations of wet-lab experiments followed by modelling approaches and model-inspired experiments describe a cyclic workflow. This approach is especially useful for the inference of gene regulatory networks based on high-throughput gene expression data. Experiments can verify or falsify the predicted interactions allowing further refinement of the network model. Aspergillus fumigatus is a major human fungal pathogen. One important virulence trait is its ability to gain sufficient amounts of iron during infection process. Even though some regulatory interactions are known, we are still far from a complete understanding of the way iron homeostasis is regulated. Results In this study, we make use of a reverse engineering strategy to infer a regulatory network controlling iron homeostasis in A. fumigatus. The inference approach utilizes the temporal change in expression data after a change from iron depleted to iron replete conditions. The modelling strategy is based on a set of linear differential equations and offers the possibility to integrate known regulatory interactions as prior knowledge. Moreover, it makes use of important selection criteria, such as sparseness and robustness. By compiling a list of known regulatory interactions for iron homeostasis in A. fumigatus and softly integrating them during network inference, we are able to predict new interactions between transcription factors and target genes. The proposed activation of the gene expression of hapX by the transcriptional regulator SrbA constitutes a so far unknown way of regulating iron homeostasis based on the amount of metabolically available iron. This interaction has been verified by Northern blots in a recent experimental study. In order to improve the reliability of the predicted network, the results of this experimental study have been added to the set of prior knowledge. The final network includes three SrbA target genes. Based on motif searching within the regulatory regions of these genes, we identify potential DNA-binding sites for SrbA. Our wet-lab experiments demonstrate high-affinity binding capacity of SrbA to the promoters of hapX, hemA and srbA. Conclusions This study presents an application of the typical Systems Biology circle and is based on cooperation between wet-lab experimentalists and in silico modellers. The results underline that using prior knowledge during network inference helps to predict biologically important interactions. Together with the experimental results, we indicate a novel iron homeostasis regulating system sensing the amount of metabolically available iron and identify the binding site of iron-related SrbA target genes. It will be of high interest to study whether these regulatory interactions are also important for close relatives of A. fumigatus and other pathogenic fungi, such as Candida albicans.

2012-01-01

308

Inference of Disease-Related Molecular Logic from Systems-Based Microarray Analysis  

PubMed Central

Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage.

Varadan, Vinay; Anastassiou, Dimitris

2006-01-01

309

Inference of disease-related molecular logic from systems-based microarray analysis.  

PubMed

Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage. PMID:16789819

Varadan, Vinay; Anastassiou, Dimitris

2006-06-16

310

Hybrid control strategy for five-fingered smart prosthetic hand  

Microsoft Academic Search

This paper presents a hybrid of soft computing or control technique of adaptive neuro-fuzzy inference system (ANFIS) and hard computing or control technique of finite-time linear quadratic optimal control for the 14 degrees of freedom (DOFs), five-fingered smart prosthetic hand. In particular, ANFIS is used for inverse kinematics, and the optimal control is used for feedback linearized dynamics to minimize

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

2009-01-01

311

Application of ANFIS to Phase Estimation for Multiple Phase Shift Keying  

NASA Technical Reports Server (NTRS)

The paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for estimating phase in Multiple Phase Shift Keying (M-PSK) modulation. A brief overview of communications phase estimation is provided. The modeling of both general open-loop, and closed-loop phase estimation schemes for M-PSK symbols with unknown structure are discussed. Preliminary performance results from simulation of the above schemes are presented.

Drake, Jeffrey T.; Prasad, Nadipuram R.

2000-01-01

312

Intelligent modeling and prediction of nanostructural behavior of humidity sensors  

Microsoft Academic Search

This paper presents the nanostructural characterization of a humidity sensor and obtains its intelligent models by using artificial neural networks and adaptive neuro-fuzzy inference systems. Three major processing components are manipulated via the sol-gel spin-coating technique to prepare an active element to detect humidity variations. The corresponding nanostructural analysis of the humidity sensor using X-ray powder diffraction generated the lattice

Muhittin Yilmaz; Jingbo Liu; Wei-Da Hao

2010-01-01

313

WiFi indoor location determination via ANFIS with PCA methods  

Microsoft Academic Search

This paper proposes the WiFi indoor location determination method based on adaptive neuro-fuzzy inference system (ANFIS) with principal component analysis (PCA). It reduces the WiFi signal vectors dimensions and saves the storage cost and simplifies the fuzzy rules generated by subtractive clustering method for ANFIS training. In the off-line phase, the received signal strength (RSS) or signal to noise ratio

Yubin Xu; Mu Zhou; Lin Ma

2009-01-01

314

An ANFIS-based Transformer Insulation Fault Diagnosis Method Using Emotional Learning  

Microsoft Academic Search

To tackle the flaws in transformer fault diagnosis such as long computing time, weak generalized ability and fuzzy knowledge acquisition difficulty, a self-adaptive neuro-fuzzy inference system (ANFIS) is proposed based on emotional learning in this paper. The method can automatically adapt itself to the change of input information characteristics, and compensate for the flaws of the imperfectness of the 3-ratio-code.

Hongsheng Su

2007-01-01

315

Pairwise ANFIS Approach to Determining the Disorder Degree of Obstructive Sleep Apnea Syndrome  

Microsoft Academic Search

Obstructive sleep apnea syndrome (OSAS) is an important disease that affects both the right and the left cardiac ventricle.\\u000a This paper presents a novel classification method called pairwise ANFIS based on Adaptive Neuro-Fuzzy Inference System (ANFIS)\\u000a and one against all method for detecting the obstructive sleep apnea syndrome. In order to extract the features related with\\u000a OSAS, we have used

Kemal Polat; Sebnem Yosunkaya; Salih Günes

2008-01-01

316

Automatic training of ANFIS networks  

Microsoft Academic Search

In the present paper an automatic training procedure for adaptive neuro-fuzzy inference system (ANFIS) networks is presented. The initialization of the net is carried out by the ?-min-max fuzzy clustering procedure, which is a modified version of the original min-max technique by Simpson (1993). Parameter ? affects the number, position and size of resulting clusters. Since different P values yield

A. Rizzi; F. M. Frattale Mascioli; G. Martinelli

1999-01-01

317

Fuzzy control strategies in human operator and sport modeling  

Microsoft Academic Search

The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex\\u000a dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for\\u000a human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal\\u000a control. As an application of the presented fuzzy strategies,

Tijana T. Ivancevic; Bojan Jovanovic; Sasa Markovic

2010-01-01

318

A Novel Data Preprocessing Method for the Modeling and Prediction of Freeze-Drying Behavior of Apples: Multiple Output–Dependent Data Scaling (MODDS)  

Microsoft Academic Search

In the present study, the freeze drying behavior of apples have been modeled and predicted. Because freeze-drying is a very expensive and complex process, modeling of the freeze-drying process is a challenging task. In this study, a novel data scaling method called multiple output–dependent data scaling (MODDS) has been proposed and combined with an adaptive neuro-fuzzy inference system (ANFIS) to

Kemal Polat; Volkan Kirmaci

2012-01-01

319

Application of MR damper in structural control using ANFIS method  

Microsoft Academic Search

Protection of civil engineering structures from excessive vibration due to uncontrollable events such as earthquake has increasingly been of importance for the purpose of maintaining structural integrity and serviceability. This paper presents the development of an adaptive neuro-fuzzy inference system (ANFIS) controller for reduction of environmentally induced vibration in multiple-degree-of-freedom (MDOF) building structure with MR damper. The LQG control method

Zhi Q. Gu; S. Olutunde Oyadiji

2008-01-01

320

Portable inference engine: An extended CLIPS for real-time production systems  

NASA Technical Reports Server (NTRS)

The present C-Language Integrated Production System (CLIPS) architecture has not been optimized to deal with the constraints of real-time production systems. Matching in CLIPS is based on the Rete Net algorithm, whose assumption of working memory stability might fail to be satisfied in a system subject to real-time dataflow. Further, the CLIPS forward-chaining control mechanism with a predefined conflict resultion strategy may not effectively focus the system's attention on situation-dependent current priorties, or appropriately address different kinds of knowledge which might appear in a given application. Portable Inference Engine (PIE) is a production system architecture based on CLIPS which attempts to create a more general tool while addressing the problems of real-time expert systems. Features of the PIE design include a modular knowledge base, a modified Rete Net algorithm, a bi-directional control strategy, and multiple user-defined conflict resolution strategies. Problems associated with real-time applications are analyzed and an explanation is given for how the PIE architecture addresses these problems.

Le, Thach; Homeier, Peter

1988-01-01

321

Evaluation of a dual processor implementation for a fault inferring nonlinear detection system  

NASA Technical Reports Server (NTRS)

The design of a modified fault inferring nonlinear detection system (FINDS) algorithm for a dual-processor configured flight computer is described. The algorithm was changed in order to divide it into its translational dynamics and rotational kinematics and to use it for parallel execution on the flight computer. The FINDS consists of: (1) a no-fail filter (NFF), (2) a set of test-of-mean detection tests, (3) a bank of first order filters to estimate failure levels in individual sensors, and (4) a decision function. NFF filter performance using flight recorded sensor data is analyzed using a filter autoinitialization routine. The failure detection and isolation capability of the partitioned algorithm is evaluated. A multirate implementation for the bias-free and bias filter gain and covariance matrices is discussed.

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

1987-01-01

322

Application of artificial intelligence models in water quality forecasting.  

PubMed

The real-time data of the continuous water quality monitoring station at the Pyeongchang river was analyzed separately during the rainy period and non-rainy period. Total organic carbon data observed during the rainy period showed a greater mean value, maximum value and standard deviation than the data observed during the non-rainy period. Dissolved oxygen values during the rainy period were lower than those observed during the non-rainy period. It was analyzed that the discharge due to rain fall from the basin affects the change of the water quality. A model for the forecasting of water quality was constructed and applied using the neural network model and the adaptive neuro-fuzzy inference system. Regarding the models of levenberg-marquardt neural network, modular neural network and adaptive neuro-fuzzy inference system, all three models showed good results for the simulation of total organic carbon. The levenberg-marquardt neural network and modular neural network models showed better results than the adaptive neuro-fuzzy inference system model in the forecasting of dissolved oxygen. The modular neural network model, which was applied with the qualitative data of time in addition to quantitative data, showed the least error. PMID:18702288

Yeon, I S; Kim, J H; Jun, K W

2008-06-01

323

Grain Size Estimation of Superalloy Inconel 718 After Upset Forging by a Fuzzy Inference System  

NASA Astrophysics Data System (ADS)

A fuzzy logic inference system was designed to predict the grain size of Inconel 718 alloy after upset forging. The system takes as input the original grain size, temperature, and reduction rate at forging and predicts the final grain size at room temperature. It is assumed that the system takes into account the effects that the heterogeneity of deformation and grain growth exerts in this particular material. Experimental trials were conducted in a factory that relies on upset forging to produce preforms for ring rolling. The grain size was reported as ASTM number, as this value is used on site. A first attempt was carried out using a series of 15 empirically based set of rules; the estimation error with these was above two ASTM numbers; which is considered to be very high. The system was modified and expanded to take into account 28 rules; the estimation error of this new system resulted to be close to one ASTM number, which is considered to be adequate for the prediction.

Toro, Luis; Cavazos, Alberto; Colás, Rafael

2009-12-01

324

Development of an on-line diagnosis system for rotor vibration via model-based intelligent inference  

PubMed

An on-line fault detection and isolation technique is proposed for the diagnosis of rotating machinery. The architecture of the system consists of a feature generation module and a fault inference module. Lateral vibration data are used for calculating the system features. Both continuous-time and discrete-time parameter estimation algorithms are employed for generating the features. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed method is implemented on a digital signal processor. Experiments carried out for a rotor kit and a centrifugal fan indicate the potential of the proposed techniques in predictive maintenance. PMID:10641641

Bai; Hsiao; Tsai; Lin

2000-01-01

325

Neuro-fuzzy approach to mode transitioning in aerospace applications  

Microsoft Academic Search

A real-time adaptation scheme is proposed for the online customization of mode transition controllers designed off-line via blending local mode controllers. It consists of the desired transition trajectory model, the active plant model and the mode transition controller. The active plant model, which incorporates local mode information, is initially trained off-line to capture the desired transition trajectory and controls. Afterwards,

George Vachtsevanos

2001-01-01

326

Optimization of Hybrid Electric Cars by Neuro-Fuzzy Networks  

Microsoft Academic Search

In this paper, the problem of the optimization of energetic\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009flows in hybrid electric vehicles is faced. We consider a hybrid electric \\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009vehicle\\u0009equipped with batteries, a thermal engine (or fuel cells), ultracapacitors\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009and an electric engine. The energetic flows are optimized by using a\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009control strategy based on the prediction of short-term and medium-term\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009vehicle states (energy consumption, vehicle

Fabio Massimo Frattale Mascioli; Antonello Rizzi; Massimo Panella; Claudia Bettiol

2007-01-01

327

Combining Fuzzy Knowledge and Data for Neuro-Fuzzy Modeling  

Microsoft Academic Search

The aim of this paper is to contribute to a central issue in neural networkthat is of combining expert knowledge and observations (data) for learning. Itis generally known that neural networks, as other adaptive models, have goodlearning and generalization capabilities because of their statistical consistency.However, such consistency is theoretically valid only for large size trainingsets. To enhance learning with small

Abderrahim Labbi; Eric Gauthier Leibniz

328

Using building blocks to design analog neuro-fuzzy controllers  

Microsoft Academic Search

We present a parallel architecture for fuzzy controllers and a methodology for their realization as analog CMOS chips for low- and medium-precision applications. These chips can be made to learn through the adaptation of electrically controllable parameters guided by a dedicated hardware-compatible learning algorithm. Our designs emphasize simplicity at the circuit level-a prerequisite for increasing processor complexity and operation speed.

Fernando Vidal-Verdu; A. Rodriguez-Vazquez

1995-01-01

329

Application of Soft Computing in Coherent Communications Phase Synchronization  

NASA Technical Reports Server (NTRS)

The use of soft computing techniques in coherent communications phase synchronization provides an alternative to analytical or hard computing methods. This paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for phase synchronization in coherent communications systems utilizing Multiple Phase Shift Keying (MPSK) modulation. A brief overview of the M-PSK digital communications bandpass modulation technique is presented and it's requisite need for phase synchronization is discussed. We briefly describe the hybrid platform developed by Jang that incorporates fuzzy/neural structures namely the, Adaptive Neuro-Fuzzy Interference Systems (ANFIS). We then discuss application of ANFIS to phase estimation for M-PSK. The modeling of both explicit, and implicit phase estimation schemes for M-PSK symbols with unknown structure are discussed. Performance results from simulation of the above scheme is presented.

Drake, Jeffrey T.; Prasad, Nadipuram R.

2000-01-01

330

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

NASA Astrophysics Data System (ADS)

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

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

2013-03-01

331

Early history of Earth's crust-mantle system inferred from hafnium isotopes in chondrites.  

PubMed

The 176Lu to 176Hf decay series has been widely used to understand the nature of Earth's early crust-mantle system. The interpretation, however, of Lu-Hf isotope data requires accurate knowledge of the radioactive decay constant of 176Lu (lambda176Lu), as well as bulk-Earth reference parameters. A recent calibration of the lambda176Lu value calls for the presence of highly unradiogenic hafnium in terrestrial zircons with ages greater than 3.9 Gyr, implying widespread continental crust extraction from an isotopically enriched mantle source more than 4.3 Gyr ago, but does not provide evidence for a complementary depleted mantle reservoir. Here we report Lu-Hf isotope measurements of different Solar System objects including chondrites and basaltic eucrites. The chondrites define a Lu-Hf isochron with an initial 176Hf/177Hf ratio of 0.279628 +/- 0.000047, corresponding to lambda176Lu = 1.983 +/- 0.033 x 10-11 yr-1 using an age of 4.56 Gyr for the chondrite-forming event. This lambda176Lu value indicates that Earth's oldest minerals were derived from melts of a mantle source with a time-integrated history of depletion rather than enrichment. The depletion event must have occurred no later than 320 Myr after planetary accretion, consistent with timing inferred from extinct radionuclides. PMID:12606997

Bizzarro, Martin; Baker, Joel A; Haack, Henning; Ulfbeck, David; Rosing, Minik

2003-02-27

332

Performance analysis of a fault inferring nonlinear detection system algorithm with integrated avionics flight data  

NASA Technical Reports Server (NTRS)

This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.

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

1985-01-01

333

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

PubMed

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

334

Children's Inferential Responses to a Wordless Picture Book: Development and Use of a Classification System for Verbalized Inference.  

ERIC Educational Resources Information Center

High, middle, and low readers in kindergarten, second, and fourth grades participated in a study of inferential comprehension. The Jett-Simpson Classification System for Verbalized Inference was developed, its reliability was established, and children's open responses to a wordless picture book, "Frog Goes to Dinner," were analyzed using it.…

Jett-Simpson, Mary

335

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

336

Subsethood-product fuzzy neural inference system (SuPFuNIS).  

PubMed

A new subsethood-product fuzzy neural inference system (SuPFuNIS) is presented in this paper. It has the flexibility to handle both numeric and linguistic inputs simultaneously. Numeric inputs are fuzzified by input nodes which act as tunable feature fuzzifiers. Rule based knowledge is easily translated directly into a network architecture. Connections in the network are represented by Gaussian fuzzy sets. The novelty of the model lies in a combination of tunable input feature fuzzifiers; fuzzy mutual subsethood-based activation spread in the network; use of the product operator to compute the extent of firing of a rule; and a volume-defuzzification process to produce a numeric output. Supervised gradient descent is employed to train the centers and spreads of individual fuzzy connections. A subsethood-based method for rule generation from the trained network is also suggested. SuPFuNIS can be applied in a variety of application domains. The model has been tested on Mackey-Glass time series prediction, Iris data classification, Hepatitis medical diagnosis, and function approximation benchmark problems. We also use a standard truck backer-upper control problem to demonstrate how expert knowledge can be used to augment the network. The performance of SuPFuNIS compares excellently with other various existing models. PMID:18244458

Paul, S; Kumar, S

2002-01-01

337

Adaptive network-based fuzzy inference system for assessment of lower limb peripheral vascular occlusive disease.  

PubMed

Detecting lower limb peripheral vascular occlusive disease (PVOD) early is important for patients to prevent disabling claudication, ischaemic rest pain and gangrene. According to previous research, the pulse timing and shape distortion characteristics of photoplethysmography (PPG) signals tend to increase with disease severity and calibrated amplitude decreases with vascular diseases. However, this is not a reliable method of evaluating the condition of PVOD because of noise effect. In this paper, an adaptive network-based fuzzy inference system (ANFIS) is proposed to assess lower limb PVOD based on PPG signals. PPG signals are non-invasively recorded from the right and left sides at the big toe sites from twenty subjects, including normal condition (Nor), lower-grade disease (LG), and higher-grade disease (HG) groups. The number of each group is 10, 8 and 2 respectively, and the ages ranged from 24 to 65 years. With the time-domain technique, the parameters for the absolute bilateral differences (right-to-left side of foot) in pulse delay and amplitude were extracted for analyzing ANFIS. The results indicated that ANFIS based on three timing parameters base bilateral differences, including ?PTTf and ?PTTp, and ?RT has a high rate and noise tolerance of PVOD assessment. PMID:20703718

Du, Yi-Chun; Lin, Chia-Hung

2012-02-01

338

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

NASA Astrophysics Data System (ADS)

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. Itsmain 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. Prior to its application to real Gaia data the accuracy of these methods cannot be assessed definitively. But as an example of the current performance, we can attain internal accuracies (rms residuals) on F, G, K, M dwarfs and giants at G = 15 (V = 15-17) for a wide range of metallicites and interstellar extinctions of around 100 K in effective temperature (Teff), 0.1 mag in extinction (A0), 0.2 dex in metallicity ([Fe/H]), and 0.25 dex in surface gravity (log g). The accuracy is a strong function of the parameters themselves, varying by a factor of more than two up or down over this parameter range. After its launch in December 2013, Gaia will nominally observe for five years, during which the system we describe will continue to evolve in light of experience with the real data.

Bailer-Jones, C. A. L.; Andrae, R.; 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.

2013-11-01

339

Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene regulatory network inference  

PubMed Central

Parameter estimation in dynamic systems finds applications in various disciplines, including system biology. The well-known expectation-maximization (EM) algorithm is a popular method and has been widely used to solve system identification and parameter estimation problems. However, the conventional EM algorithm cannot exploit the sparsity. On the other hand, in gene regulatory network inference problems, the parameters to be estimated often exhibit sparse structure. In this paper, a regularized expectation-maximization (rEM) algorithm for sparse parameter estimation in nonlinear dynamic systems is proposed that is based on the maximum a posteriori (MAP) estimation and can incorporate the sparse prior. The expectation step involves the forward Gaussian approximation filtering and the backward Gaussian approximation smoothing. The maximization step employs a re-weighted iterative thresholding method. The proposed algorithm is then applied to gene regulatory network inference. Results based on both synthetic and real data show the effectiveness of the proposed algorithm.

2014-01-01

340

Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene regulatory network inference.  

PubMed

Parameter estimation in dynamic systems finds applications in various disciplines, including system biology. The well-known expectation-maximization (EM) algorithm is a popular method and has been widely used to solve system identification and parameter estimation problems. However, the conventional EM algorithm cannot exploit the sparsity. On the other hand, in gene regulatory network inference problems, the parameters to be estimated often exhibit sparse structure. In this paper, a regularized expectation-maximization (rEM) algorithm for sparse parameter estimation in nonlinear dynamic systems is proposed that is based on the maximum a posteriori (MAP) estimation and can incorporate the sparse prior. The expectation step involves the forward Gaussian approximation filtering and the backward Gaussian approximation smoothing. The maximization step employs a re-weighted iterative thresholding method. The proposed algorithm is then applied to gene regulatory network inference. Results based on both synthetic and real data show the effectiveness of the proposed algorithm. PMID:24708632

Jia, Bin; Wang, Xiaodong

2014-01-01

341

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

PubMed

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

342

Chapter 1 Spatial Soil Information Systems and Spatial Soil Inference Systems: Perspectives for Digital Soil Mapping  

Microsoft Academic Search

Given the relative dearth of, and the huge demand for, quantitative spatial soil information, it is timely to develop and implement methodologies for its provision. We suggest that digital soil mapping, which can be defined as the creation, and population of spatial soil information systems (SSINFOS) by the use of field and laboratory observational methods, coupled with spatial and non-spatial

P. Lagacherie; A. B. McBratney

2006-01-01

343

Information Warfare-Worthy Jamming Attack Detection Mechanism for Wireless Sensor Networks Using a Fuzzy Inference System  

PubMed Central

The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes.

Misra, Sudip; Singh, Ranjit; Rohith Mohan, S. V.

2010-01-01

344

Information warfare-worthy jamming attack detection mechanism for wireless sensor networks using a fuzzy inference system.  

PubMed

The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307

Misra, Sudip; Singh, Ranjit; Rohith Mohan, S V

2010-01-01

345

Spatial Variation on a Direction of Maximum Horizontal Compression in and around the Atotsugawa Fault System, Japan, Inferred from Shear Wave Splitting  

Microsoft Academic Search

We investigated a shear wave splitting of micro earthquakes occurred in and around the Atotsugawa fault system, Central Japan, to infer a spatial variation of the direction of maximum horizontal compression (Shmax). Leading shear wave polarization directions (LSPD) indicate that the maximum compressional axis trends WNW-ESE, which is nearly consistent with the direction of the regional Shmax inferred from geodetic

T. Mizuno; Y. Kuwahara; H. Ito; K. Imanishi; T. Takeda

2005-01-01

346

Integral Transforms and Bayesian Inference in the Identification of Variable Thermal Conductivity in Two-Phase Dispersed Systems  

Microsoft Academic Search

This work illustrates the use of Bayesian inference in the estimation of spatially variable thermal conductivity for one-dimensional heat conduction in heterogeneous media, such as particle-filled composites and other two-phase dispersed systems, by employing a Markov chain Monte Carlo (MCMC) method, through the implementation of the Metropolis-Hastings algorithm. The direct problem solution is obtained analytically via integral transforms, and the

Carolina P. Naveira-Cotta; Helcio R. B. Orlande; Renato M. Cotta

2010-01-01

347

Astrophysical Site of the Origin of the Solar System Inferred from Extinct Radionuclide Abundances  

NASA Astrophysics Data System (ADS)

Extinct radionuclides in the solar abundance distribution (SAD) provide a basis with which to characterize the molecular cloud environment in which the solar system formed 4566±2 Ma ago. The low abundance of the longer-lived r-process radionuclide 129I(T½ = 16 Ma) indicates a long (˜ 102 Ma) isolation time from energetic interstellar medium (ISM) reservoirs containing most of the Galaxy's budget of freshly-synthesized Type II supernova products. However, the abundances of the shorter-lived species 60Fe (T½ = 1.5 Ma), 53Mn (T½ = 3.7 Ma), and 107Pd (T½ = 6.5 Ma) are consistent with late admixture of freshly synthesized Type II supernova products. The fit for these species is based on an average yield distribution obtained by decomposition of the SAD. The apparent timescale contradiction is resolved in a simple two timescale molecular cloud self-contamination model consistent with formation of the Sun in an old evolved stellar complex at the eroding boundary of a molecular cloud interacting with an adjacent OB association. Admixture of an ˜10-5 to ˜10-6 mass fraction of Type II supernova ejecta into the presolar cloud dominates the shorter-lived species and 107Pd, whereas longer- lived 129I preserves information on the longer timescale constraining the mean isolation/condensation/ accretion age of the molecular material in the protosolar reservoir. The inferred model age of nucleosynthetic isolation in the long timescale is consistent with cyclicity in the nucleosynthesis rate in an orbiting ISM parcel controlled by galactic spiral structure and beads-on-a-string organization of star formation in "stellar complexes" in arms. Abundant 26Al (T½ = 0.7 Ma) in the early solar system at ˜102 times the model prediction may point to 26Al/27Al ratio of ˜0.2 in the source, or an ˜102 times greater mixing fraction for pre-explosion winds over postexplosion ejecta. A mass-losing low-mass asymptotic giant branch (AGB) star model can be tuned to account for 41Ca, 26Al, 60Fe, and 107Pd, but fails for 53Mn, requires unusual s-process conditions, and is a priori improbable. Another alternate hypothesis, cosmic-ray spallation in an OB association, is limited as a radionuclide source by LiBeB overproduction, except for improbably fine-tuned conditions. Supernova self-contamination may be a widespread process in evolved star-forming regions, but mixing dynamics and their relation to star formation are poorly understood.

Harper, Charles L., Jr.

1996-08-01

348

A novel optimization procedure for training of fuzzy inference systems by combining variable structure systems technique and Levenberg-Marquardt algorithm  

Microsoft Academic Search

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

349

Crustal deformation in the central California coast region inferred from Global Positioning System data  

NASA Astrophysics Data System (ADS)

The Central California Coast Region (CCCR), defined here as the area from north of Point Piedras Blancas (36°N) south to Point Arguello (34.6°N) and west of the Rinconada and East Huasna faults, is a structurally complex region cut by several subparallel, late Quaternary faults. Despite relatively low rates of deformation inferred from geologic studies of the CCCR, the occurrence of the 2003 Mw 6.5 San Simeon earthquake southeast of Point Piedras Blancas highlights the need to better understand the ongoing patterns of deformation here as a means for assessing the seismic hazard. Geological and geophysical data from this region have been interpreted as evidence for ongoing transpression due to the clockwise rotation of the Transverse Ranges which would predict crustal contraction normal to the plate boundary. However an alternative interpretation concludes that the region instead experiences the active westward transfer of right-lateral strike-slip motion in a left-stepping fashion which would result in northwest-southeast contraction. Geodetic data can be used to elucidate how strain is currently partitioned between shear parallel to the San Andreas Fault (SAF) and contraction within the CCCR and to identify actively deforming structures. We use a newly compiled Global Positioning System (GPS) secular velocity field for the CCCR as well as GPS velocities for the greater southern California region from the SCEC Crustal Motion Map v.4 and the EarthScope Plate Boundary Observatory velocity solution to constrain block models of deformation. We solve for the rotation of fault-bounded blocks, fault slip rates, and internal strain within blocks. Results thus far indicate that the data do not require substantial slip on the Rinconada fault (for which the estimated slip rate is ~2 mm/yr) or on the Oceanic and West Huasna faults that bound the eastern edge of the CCCR in an alternative block configuration (for which the estimated slip rate is <1 mm/yr). The data also do not suggest that significant internal contractional strain is accumulating either normal to the plate boundary or parallel to the major regional strike slip faults. Ongoing work is focused on exploring different block geometries in an effort to better constrain internal block strain rates and slip rates on the Hosgri, Rinconada, and San Andreas faults as well as to assess the sensitivity of these rate estimates to model fault geometry and observations. These results will contribute to the development of a coherent regional tectonic model and help to characterize the potential seismic sources in the region.

Murray-Moraleda, J. R.; Thatcher, W. R.; Onishi, C. T.; Svarc, J. L.

2011-12-01

350

Hydrothermal system beneath Aso volcano as inferred from self-potential mapping and resistivity structure  

NASA Astrophysics Data System (ADS)

We conducted self-potential (SP) surveys sequentially from part to part over the central cones of Aso volcano since August 1998 by December 2001. The compiled SP map revealed large SP anomalies on the central cones. The main feature of the SP map is a 'W-shaped' profile along the NS-transect over the central cones. It is probable that this characteristic SP profile is produced by the combination of hydrothermal upwelling in the middle and topographic effect. A positive anomaly showing a large concentric pattern has appeared after correcting the topographic effect. To evaluate this SP anomaly, we implemented a numerical code that calculates electric potential produced by arbitrarily positioned current sources and sinks in any three-dimensional resistivity structure. A layered structure obtained from a time-domain electromagnetic (TDEM) field experiment was used for the resistivity model. The estimated current source is 300 A, being located in a conductive layer around the sea level. Meanwhile, sinks were estimated to sit on a circular area corresponding to the marginal part of the conductive layer. Water and heat budget study gives a lower limit of water mass transfer from depth to the bottom of the crater lake of Nakadake. This value was used to estimate the equivalent current in either case of electro-kinetic (EK) [Mizutani, H., Ishido, T., 1976. A new interpretation of magnetic field variation associated with the Matsushiro earthquakes, J. Geomag. Geoelectr., 28, 179-188.] or rapid fluid disruption (RFD) process [Johnston, M.J.S., Byerlee, J.D., Lockner, D., 2001. Rapid fluid disruption: A source for self-potential anomalies on volcanoes, J. Geophys. Res. 106(B3), 4327-4335.]. This comparison suggests that the former process is preferable to explain the observed SP anomaly. From these results we infer a large-scale hydrothermal system beneath the central cones of Aso volcano, in which the fluid flow initiates from the surrounding area, converging to the central vent to transport the heat and materials up to the crater lake of Nakadake through a vapor-filled conduit.

Hase, Hideaki; Hashimoto, Takeshi; Sakanaka, Shin'ya; Kanda, Wataru; Tanaka, Yoshikazu

2005-05-01

351

Bayesian inference.  

PubMed

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

352

Inferring Trust  

Microsoft Academic Search

In this paper we discuss Liau's logic of Belief, Inform and Trust (BIT), which captures the use of trust to infer beliefs from ac- quired information. However, the logic does not capture the derivation of trust from other notions. We therefore suggest the following two ex- tensions. First, like Liau we observe that trust in information from an agent depends

Mehdi Dastani; Andreas Herzig; Joris Hulstijn; Leendert W. N. Van Der Torre

2004-01-01

353

Visualization in Java, the performance of control system, based on fuzzy inference system, with a front-end built in Visual Basic  

Microsoft Academic Search

The paper presents a technique to visualize the performance of a control system fine-tuned by fuzzy inference. The control problem picked for this paper is the problem of controlling the swing of a crane when it is in motion. The conventional model for controlling the swing in cranes is based on differential equations or discrete transforms and is unable to

D. Kaur; K. Shah

2001-01-01

354

Inductive Inference: Theory and Methods  

Microsoft Academic Search

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

355

Ecological immunology of mosquito-malaria interactions: Of non-natural versus natural model systems and their inferences.  

PubMed

There has been a recent shift in the literature on mosquito/Plasmodium interactions with an increasingly large number of theoretical and experimental studies focusing on their population biology and evolutionary processes. Ecological immunology of mosquito-malaria interactions - the study of the mechanisms and function of mosquito immune responses to Plasmodium in their ecological and evolutionary context - is particularly important for our understanding of malaria transmission and how to control it. Indeed, describing the processes that create and maintain variation in mosquito immune responses and parasite virulence in natural populations may be as important to this endeavor as describing the immune responses themselves. For historical reasons, Ecological Immunology still largely relies on studies based on non-natural model systems. There are many reasons why current research should favour studies conducted closer to the field and more realistic experimental systems whenever possible. As a result, a number of researchers have raised concerns over the use of artificial host-parasite associations to generate inferences about population-level processes. Here I discuss and review several lines of evidence that, I believe, best illustrate and summarize the limitations of inferences generated using non-natural model systems. PMID:19490728

Tripet, F

2009-12-01

356

Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system.  

PubMed

Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%. PMID:23367375

San, Phyo Phyo; Ling, Sai Ho; Nguyen, Hung T

2012-01-01

357

Electron Precipitation Parameters and Ionospheric Conductances Inferred from Auroral Images Acquired by the Visible Imaging Systems (VIS) on the Polar Spacecraft  

NASA Technical Reports Server (NTRS)

The Visible Imaging System (VIS) on the polar spacecraft provided time sequences of auroral images at multiple wavelengths that yield information of auroral dynamics on a global scale with a spatial resolution of - 20 km and temporal resolution of approx. 1 minute. Time sequences of VIS images in which the aurora was highly dynamic are used to infer global maps for the electron precipitation parameters, energy flux and characteristic energies, and ionospheric conductances. The maps are inferred from the corresponding VIS images using an auroral model (Lumerzheim et al., 1987). The temporal and spatial resolution of the VIS inferred patterns are unprecedented. The inferred patterns are highly structured and vary significantly on a time scale of less than 5 minutes. These patterns can be very beneficial for global physics-based numerical models for the high-latitude ionosphere which previously had to rely on statistical models for the electron precipitation and ionospheric conductance.

Sigwarth, John B.; Bekerat, Hamed A.

2008-01-01

358

Analysis of magnetic satellite data to infer the mantle electrical conductivity of telluric planets in the solar system  

NASA Astrophysics Data System (ADS)

Space missions launched to study the solar system planets generally involve a magnetometer in the scientific payload. The magnetic data may be used to infer the electrical conductivity of a planet's mantle using electromagnetic induction theory. The application of induction analysis on terrestrial bodies of the solar system other than the earth is challenging because of little information available about the external inducing sources. Here, we present a method to analyze magnetic data from these space missions that determines the geometry of the dominant external inducing magnetic field and deals with the inherent gaps in the satellite magnetic time series. We tested the approach on Earth synthetic satellite data generated to prepare the ESA magnetic mission Swarm and demonstrated the feasibility for recovering the 1-D conductivity part of the model used to generate these data. The analysis of real data from the Danish Ørsted magnetic mission provided satisfactory conductivity profiles of the Earth's mantle.

Civet, F.; Tarits, P.

2013-08-01

359

De novo inference of systems-level mechanistic models of development from live-imaging-based phenotype analysis.  

PubMed

Elucidation of complex phenotypes for mechanistic insights presents a significant challenge in systems biology. We report a strategy to automatically infer mechanistic models of cell fate differentiation based on live-imaging data. We use cell lineage tracing and combinations of tissue-specific marker expression to assay progenitor cell fate and detect fate changes upon genetic perturbation. Based on the cellular phenotypes, we further construct a model for how fate differentiation progresses in progenitor cells and predict cell-specific gene modules and cell-to-cell signaling events that regulate the series of fate choices. We validate our approach in C. elegans embryogenesis by perturbing 20 genes in over 300 embryos. The result not only recapitulates current knowledge but also provides insights into gene function and regulated fate choice, including an unexpected self-renewal. Our study provides a powerful approach for automated and quantitative interpretation of complex in vivo information. PMID:24439388

Du, Zhuo; Santella, Anthony; He, Fei; Tiongson, Michael; Bao, Zhirong

2014-01-16

360

STATISTICAL INFERENCE ON THE TRAFFIC INTENSITY FOR THE M\\/M\\/s QUEUEING SYSTEM  

Microsoft Academic Search

In the paper it is shown that, for the certain plans of observations on the M\\/M\\/s queueing system's performances, there exists a uniformly most powerful test of statistical hypothesis on the trac intensity. 1. Preliminaries. Let us consider the M=M=s queueing system. That means we have the system with poissonian input with parameter ; > 0 and exponential output of

Alexei Leahu

361

Inference and learning methodology of belief-rule-based expert system for pipeline leak detection  

Microsoft Academic Search

Belief rule based expert systems are an extension of traditional rule based systems and are capable of representing more complicated causal relationships using different types of information with uncertainties. This paper describes how the belief rule based expert systems can be trained and used for pipeline leak detection. Pipeline operations under different conditions are modelled by a belief rule base

Dong-ling Xu; Jun Liu; Jian-bo Yang; Guo-ping Liu; Jin Wang; Ian Jenkinson; Jun Ren

2007-01-01

362

Video-based cargo fire verification system with fuzzy inference engine for commercial aircraft  

Microsoft Academic Search

Conventional smoke detection systems currently installed onboard aircraft are often subject to high rates of false alarms. Under current procedures, whenever an alarm is issued the pilot is obliged to release fire extinguishers and to divert to the nearest airport. Aircraft diversions are costly and dangerous in some situations. A reliable detection system that minimizes false-alarm rate and allows continuous

Mokhtar Sadok; Radek Zakrzewski; Bob Zeliff

2005-01-01

363

Integrated system for maintenance and safety management through FMECA principles and fuzzy inference engine  

Microsoft Academic Search

Failure mode effects and criticality analysis (FMECA) is a widely used technique to improve products and processes safety and reliability in different contexts, such as automotive, aviation, computer science, etc. FMECA approach is based on a qualitative\\/quantitative analysis of a system (product or process) and its components in order to identify the most critical elements for system operability and safety

Matteo Mario Savino; Alessandro Brun; Carlo Riccio

2011-01-01

364

A general framework for modeling tumor-immune system competition and immunotherapy: Mathematical analysis and biomedical inferences  

NASA Astrophysics Data System (ADS)

In this work we propose and investigate a family of models, which admits as particular cases some well known mathematical models of tumor-immune system interaction, with the additional assumption that the influx of immune system cells may be a function of the number of cancer cells. Constant, periodic and impulsive therapies (as well as the non-perturbed system) are investigated both analytically for the general family and, by using the model by Kuznetsov et al. [V.A. Kuznetsov, I.A. Makalkin, M.A. Taylor, A.S. Perelson, Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis, Bull. Math. Biol. (1994) 56(2) 295-321), via numerical simulations. Simulations seem to show that the shape of the function modeling the therapy is a crucial factor only for very high values of the therapy period T, whereas for realistic values of T, the eradication of the cancer cells depends on the mean values of the therapy term. Finally, some medical inferences are proposed.

d'Onofrio, Alberto

2005-09-01

365

Hydrothermal system of Central Tenerife Volcanic Complex, Canary Islands (Spain), inferred from self-potential measurements  

NASA Astrophysics Data System (ADS)

An extensive self-potential survey was carried out in the central volcanic complex of Tenerife Island (Canary Islands, Spain). A total amount of ~ 237 km of profiles with 20 m spacing between measurements was completed, including radial profiles extending from the summits of Teide and Pico Viejo, and circular profiles inside and around Las Cañadas caldera and the northern slopes of Teide and Pico Viejo. One of the main results of this mapping is the detection of well-developed hydrothermal systems within the edifices of Teide and Pico Viejo, and also associated with the flank satellite M. Blanca and M. Rajada volcanoes. A strong structural control of the surface manifestation of these hydrothermal systems is deduced from the data, pointing to the subdivision of Teide and Pico Viejo hydrothermal systems in three zones: summit crater, upper and lower hydrothermal systems. Self-potential maxima related to hydrothermal activity are absent from the proximal parts of the NE and NW rift zones as well as from at least two of the mafic historical eruptions (Chinyero and Siete Fuentes), indicating that long-lived hydrothermal systems have developed exclusively over relatively shallow felsic magma reservoirs. Towards Las Cañadas caldera floor and walls, the influence of the central hydrothermal systems disappears and the self-potential signal is controlled by the topography, the distance to the water table of Las Cañadas aquifer and its geometry. Nevertheless, fossil or remanent hydrothermal activity at some points along the Caldera wall, especially around the Roques de García area, is also suggested by the data. Self-potential data indicate the existence of independent groundwater systems in the three calderas of Ucanca, Guajara and Diego Hernández, with a funnel shaped negative anomaly in the Diego Hernández caldera floor related to the subsurface topography of the caldera bottom. Two other important self-potential features are detected: positive values towards the northwestern Santiago rift, possibly due to the relatively high altitude of the water-table in this area; and a linear set of minima to the west of Pico Viejo, aligned with the northwestern rift and related to meteoric water infiltration along its fracture system.

Villasante-Marcos, Víctor; Finizola, Anthony; Abella, Rafael; Barde-Cabusson, Stéphanie; Blanco, María José; Brenes, Beatriz; Cabrera, Víctor; Casas, Benito; De Agustín, Pablo; Di Gangi, Fabio; Domínguez, Itahiza; García, Olaya; Gomis, Almudena; Guzmán, Juan; Iribarren, Ilazkiñe; Levieux, Guillaume; López, Carmen; Luengo-Oroz, Natividad; Martín, Isidoro; Moreno, Manuel; Meletlidis, Stavros; Morin, Julie; Moure, David; Pereda, Jorge; Ricci, Tullio; Romero, Enrique; Schütze, Claudia; Suski-Ricci, Barbara; Torres, Pedro; Trigo, Patricia

2014-02-01

366

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

PubMed

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

367

Inductively Inferring Valid Logical Models of Continuous-State Dynamical Systems  

Microsoft Academic Search

Hybrid control systems consist of a discrete event (DES) controller supervising a continuous-state (CSS) plant. A controller can be synthesized by obtaining a DES controller for an equivalent DES representation (DES plant) of the CSS plant. An important issue concerns the logical invariance (stability) of DES plant transitions to variations in the initial CSS plant state. This paper provides a

Michael D. Lemmon; Panos J. Antsaklis

1995-01-01

368

New Class of Intelligent Knowledge Based Systems with an Optimisation Based Inference Engine.  

National Technical Information Service (NTIS)

In the paper the authors describe a new class of Intelligent Knowledge Based Systems (I.K.B.S.) which can be used principally for managerial decision making applications. This class of applications often requires a framework for knowledge acquisition whic...

M. G. Singh R. Cook

1985-01-01

369

RULE-BASED INFERENCE SYSTEM FOR PREDICTING LINER/WASTE COMPATIBILITY  

EPA Science Inventory

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...

370

CAMERA PLACEMENT FOR NETWORK DESIGN IN VISION METROLOGY BASED ON FUZZY INFERENCE SYSTEM  

Microsoft Academic Search

For measuring complex industrial objects using vision metrology systems, automatic optimum network design is a real challenge. In the absence of given or simulated 3D CAD models of the objects and the workspace, the complexity of objects introduces several uncertainty factors into the camera placement decision making process. These uncertainty factors include the vision constraints such as visibility, accessibility and

M. Hahn

371

Pumping system fault detection and diagnosis utilizing pattern recognition and fuzzy inference techniques  

Microsoft Academic Search

An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and

R. M. Singer; K. C. Gross; K. E. Humenik

1991-01-01

372

Parameter estimation and model selection for a class of hysteretic systems using Bayesian inference  

NASA Astrophysics Data System (ADS)

The aim of this paper is to provide an overview of the possible advantages of adopting a Bayesian approach to nonlinear system identification in structural dynamics. In contrast to identification schemes which estimate maximum likelihood values (or other point estimates) for parameters, the Bayesian scheme discussed here provides information about the complete probability density functions of parameter estimates without adopting restrictive assumptions about their nature. Among other advantages of the Bayesian viewpoint are the abilities to make informed decisions about model selection and also to effectively make predictions over entire classes of models, with each individual model weighted according to its ability to explain the observed data. The approach is illustrated using data from simulated systems, first a Duffing oscillator and then a new application to hysteretic system of the Bouc-Wen type. The modelling and identification of the latter type of system has long presented problems due to the fact that commonly used model structures like the Bouc-Wen model are nonlinear in the parameters, or have unmeasured states, etc. These issues have been dealt with in the past by adopting an optimisation-based approach to the problem; in particular, the differential evolution algorithm has proved very effective. An objective of the current paper is to illustrate how the Bayesian approach provides the same information and more as the optimisation approach; it yields parameter estimates and their associated confidence intervals, but can also provide confidence bounds on model predictions and evidence measures which can be used to select the most appropriate model from a candidate set. A new model selection criterion in this context - the Deviance Information Criterion (DIC) - is presented here.

Worden, K.; Hensman, J. J.

2012-10-01

373

Detection and Classification of DDoS Attacks Using Fuzzy Inference System  

Microsoft Academic Search

\\u000a A DDoS attack saturates a network by overwhelming the network resources with an immense volume of traffic that prevent the\\u000a normal users from accessing the network resources. When Intrusion Detection Systems are used, a huge number of alerts will\\u000a be generated and these alerts consist of both False Positives and True Positives. Due to huge volume of attack traffic, there

T. Subbulakshmi; S. Mercy Shalinie; C. Suneel Reddy; A. Ramamoorthi

2010-01-01

374

Astrophysical Site of the Origin of the Solar System Inferred from Extinct Radionuclide Abundances  

Microsoft Academic Search

Extinct radionuclides in the solar abundance distribution (SAD) provide a basis with which to characterize the molecular cloud environment in which the solar system formed 4566±2 Ma ago. The low abundance of the longer-lived r-process radionuclide 129I(T½ = 16 Ma) indicates a long (˜ 102 Ma) isolation time from energetic interstellar medium (ISM) reservoirs containing most of the Galaxy's budget

Charles L. Harper Jr.

1996-01-01

375

Early history of Earth's crust-mantle system inferred from hafnium isotopes in chondrites  

Microsoft Academic Search

The 176Lu to 176Hf decay series has been widely used to understand the nature of Earth's early crust-mantle system. The interpretation, however, of Lu-Hf isotope data requires accurate knowledge of the radioactive decay constant of 176Lu (lambda176Lu), as well as bulk-Earth reference parameters. A recent calibration of the lambda176Lu value calls for the presence of highly unradiogenic hafnium in terrestrial

Martin Bizzarro; Joel A. Baker; Henning Haack; David Ulfbeck; Minik Rosing

2003-01-01

376

Velocity field in Asia inferred from Quaternary fault slip rates and Global Positioning System observations  

Microsoft Academic Search

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

377

Methane leakage from evolving petroleum systems: Masses, rates and inferences for climate feedback  

NASA Astrophysics Data System (ADS)

The immense mass of organic carbon contained in sedimentary systems, currently estimated at 1.56×1010 Tg (Des Marais et al., 1992), bears the potential of affecting global climate through the release of thermally or biologically generated methane to the atmosphere. Here we investigate the potential of naturally-occurring gas leakage, controlled by petroleum generation and degradation as a forcing mechanism for climate at geologic time scales. We addressed the potential methane contributions to the atmosphere during the evolution of petroleum systems in two different, petroliferous geological settings: the Western Canada Sedimentary Basin (WCSB) and the Central Graben area of the North Sea. Besides 3D numerical simulation, different types of mass balance and theoretical approaches were applied depending on the data available and the processes taking place in each basin. In the case of the WCSB, we estimate maximum thermogenic methane leakage rates in the order of 10-2-10-3 Tg/yr, and maximum biogenic methane generation rates of 10-2 Tg/yr. In the case of the Central Graben, maximum estimates for thermogenic methane leakage are in the order in 10-3 Tg/yr. Extrapolation of our results to a global scale suggests that, at least as a single process, thermal gas generation in hydrocarbon kitchen areas would not be able to influence climate, although it may contribute to a positive feedback. Conversely, only the sudden release of subsurface methane accumulations, formed over geologic timescales, can possibly allow for petroleum systems to exert an effect on climate.

Berbesi, L. A.; di Primio, R.; Anka, Z.; Horsfield, B.; Wilkes, H.

2014-02-01

378

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems  

PubMed Central

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.

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

2013-01-01

379

An expert system shell for inferring vegetation characteristics: Interface for the addition of techniques (Task H)  

NASA Technical Reports Server (NTRS)

All the NASA VEGetation Workbench (VEG) goals except the Learning System provide the scientist with several different techniques. When VEG is run, rules assist the scientist in selecting the best of the available techniques to apply to the sample of cover type data being studied. The techniques are stored in the VEG knowledge base. The design and implementation of an interface that allows the scientist to add new techniques to VEG without assistance from the developer were completed. A new interface that enables the scientist to add techniques to VEG without assistance from the developer was designed and implemented. This interface does not require the scientist to have a thorough knowledge of Knowledge Engineering Environment (KEE) by Intellicorp or a detailed knowledge of the structure of VEG. The interface prompts the scientist to enter the required information about the new technique. It prompts the scientist to enter the required Common Lisp functions for executing the technique and the left hand side of the rule that causes the technique to be selected. A template for each function and rule and detailed instructions about the arguments of the functions, the values they should return, and the format of the rule are displayed. Checks are made to ensure that the required data were entered, the functions compiled correctly, and the rule parsed correctly before the new technique is stored. The additional techniques are stored separately from the VEG knowledge base. When the VEG knowledge base is loaded, the additional techniques are not normally loaded. The interface allows the scientist the option of adding all the previously defined new techniques before running VEG. When the techniques are added, the required units to store the additional techniques are created automatically in the correct places in the VEG knowledge base. The methods file containing the functions required by the additional techniques is loaded. New rule units are created to store the new rules. The interface that allow the scientist to select which techniques to use is updated automatically to include the new techniques. Task H was completed. The interface that allows the scientist to add techniques to VEG was implemented and comprehensively tested. The Common Lisp code for the Add Techniques system is listed in Appendix A.

Harrison, P. Ann

1993-01-01

380

Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data.  

PubMed

Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming, expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb/RBC), we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and to predict serum iron level. Our results represent that the neural network analysis is superior to ANFIS and logistic regression models in diagnosing IDA. Moreover, the results show that the ANN is likely to provide an accurate test for predicting serum iron levels with high accuracy and acceptable precision. PMID:21503744

Azarkhish, Iman; Raoufy, Mohammad Reza; Gharibzadeh, Shahriar

2012-06-01

381

Real-time estimation and detection of non-linearity in bio-signals using wireless brain-computer interface.  

PubMed

In this paper, the work is mainly concentrated on removing non-linear parameters to make the physiological signals more linear and reducing the complexity of the signals. This paper discusses three different types of techniques that can be successfully utilised to remove non-linear parameters in EEG and ECG. (i) Transformation technique using Discrete Walsh-Hadamard Transform (DWHT); (ii) application of fuzzy logic control and (iii) building the Adaptive Neuro-Fuzzy Inference System (ANFIS) model for fuzzy. This work has been inspired by the need to arrive at an efficient, simple, accurate and quicker method for analysis of bio-signal. PMID:24589837

Ganesan, S; Victoire, T Aruldoss Albert; Vijayalakshmy, G

2014-01-01

382

QSPR prediction of flash point of esters by means of GFA and ANFIS.  

PubMed

A quantitative structure property relationship (QSPR) study was performed to develop a model for prediction of flash point of esters based on a diverse set of 95 components. The most five important descriptors were selected from a set of 1124 descriptors to build the QSPR model by means of a genetic function approximation (GFA). For considering the nonlinear behavior of these molecular descriptors, adaptive neuro-fuzzy inference system (ANFIS) method was used. The ANFIS and GFA squared correlation coefficient for testing set was 0.969 and 0.965, respectively. The results obtained showed the ability of developed GFA and ANFIS for prediction of flash point of esters. PMID:20381958

Khajeh, Aboozar; Modarress, Hamid

2010-07-15

383

Comparative assessment of rule-based and Bayes' theorem as inference engines in diagnosing symptoms for Islamic medication expert system  

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

384

Modeling Pb (II) adsorption from aqueous solution by ostrich bone ash using adaptive neural-based fuzzy inference system.  

PubMed

To evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) model in estimating the efficiency of Pb (II) ions removal from aqueous solution by ostrich bone ash, a batch experiment was conducted. Five operational parameters including adsorbent dosage (C(s)), initial concentration of Pb (II) ions (C(o)), initial pH, temperature (T) and contact time (t) were taken as the input data and the adsorption efficiency (AE) of bone ash as the output. Based on the 31 different structures, 5 ANFIS models were tested against the measured adsorption efficiency to assess the accuracy of each model. The results showed that ANFIS5, which used all input parameters, was the most accurate (RMSE = 2.65 and R(2) = 0.95) and ANFIS1, which used only the contact time input, was the worst (RMSE = 14.56 and R(2) = 0.46). In ranking the models, ANFIS4, ANFIS3 and ANFIS2 ranked second, third and fourth, respectively. The sensitivity analysis revealed that the estimated AE is more sensitive to the contact time, followed by pH, initial concentration of Pb (II) ions, adsorbent dosage, and temperature. The results showed that all ANFIS models overestimated the AE. In general, this study confirmed the capabilities of ANFIS model as an effective tool for estimation of AE. PMID:23383640

Amiri, Mohammad J; Abedi-Koupai, Jahangir; Eslamian, Sayed S; Mousavi, Sayed F; Hasheminejad, Hasti

2013-01-01

385

P72Inferring systems-level cardiac aging biomarkers through integromics network analysis.  

PubMed

From the perspective of Systems Medicine, cardiac aging is re-addressed through large scale diverse omics investigations and more importantly through their integration. Nowadays, the micronome revolutionized our comprehension of the underlying molecular mechanisms and established its role as a player of utmost importance in cardiac development, hypertrophy and longevity. A recent study [1] elucidated that the altered expression of miR-34a during aging is highly correlated with the cardiac function decline. Also the study of [2] identified a set of 65 age-dependent miRs and miRs* in the mouse model. Despite the significance of these discoveries, heart aging is a highly complex process that cannot be featured through changes on individual molecular components but rather through the changes on integromics sub-networks. In addition, despite the boom experienced in recent years in the study of gene regulation by the action of miRNAs, the analysis of genome-wide interaction networks among miRNAs and their targets has lagged behind. Motivated by the challenge to set a more realistic cardiac aging model, we examined the viewpoint that whole micronome-transcriptome-proteome interaction analysis is required to define age-related biomarkers and explore the potential consequences of miRNA (de)regulation as well as the cooperative/combinatorial targeting. To accomplish this, initially, we compiled a cohort of mRNA/miRNA cardiac tissue expression data from various mouse inbred strains, protein-protein and signaling pathway interactions, and miRNA-mRNA interactions. A multilevel network was constructed with two types of nodes (mRNA and miRNA) and three types of interactions (mRNA-mRNA, miRNA-mRNA and miRNA-miRNA), while the expression data served as means for weighting the final network so as to alleviate the identification of significantly altered modules (i.e. dense sub-networks with distinct functional role), via a module-detecting algorithm, due to aging factor. Our analysis revises recent discoveries and provides a signature set of integromics modules that will be valuable for future biomarker studies in humans. An indicative example module that offers novel hypotheses is the module constructed around miR-34a including HRAS1, EOMES, PIWIL2 and ZDHHC18 as interactors. Finally, our biomarkers pinpoint the involvement of miRs* in heart longevity as well as reveal many aspects of miRNA synergism. [1] Boon RA, et al. Nature. 2013;495:107-10. [2] Zhang X, et al. PLoS One. 2012;7:e34688. PMID:25020381

Dimitrakopoulou, K; Vrahatis, Ag; Bezerianos, A

2014-07-15

386

Type1 and Type2 Fuzzy Inference Systems as Integration Methods in Modular Neural Networks for Multimodal Biometry and Its Optimization with Genetic Algorithms  

Microsoft Academic Search

We describe in this paper a comparative study of Fuzzy Inference Systems as methods of integration in modular neural networks\\u000a (MNN’s) for multimodal biometry. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy\\u000a systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach\\u000a with

Denisse Hidalgo; Oscar Castillo; Patricia Melin

2008-01-01

387

Leuconostoc Mesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems  

PubMed Central

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.

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

2013-01-01

388

Analysis of adaptive-network-based fuzzy inference system (ANFIS) to estimate buoyancy-induced flow field in partially heated triangular enclosures  

Microsoft Academic Search

Adaptive-network-based Fuzzy Inference System (ANFIS) was used to predict temperature and flow field due to buoyancy-induced heat transfer in a partially heated right-angle triangular enclosure. Results are obtained for two-dimensional, steady and laminar flow. Data were generated from earlier studies which were obtained from a CFD-based computer code writing in Fortran. Equations were analyzed using finite difference method. Effective parameters

Yasin Varol; Ahmet Koca; Hakan F. Öztop; Engin Avci

2008-01-01

389

Physical limits of inference  

NASA Astrophysics Data System (ADS)

We show that physical devices that perform observation, prediction, or recollection share an underlying mathematical structure. We call devices with that structure “inference devices”. We present a set of existence and impossibility results concerning inference devices. These results hold independent of the precise physical laws governing our universe. In a limited sense, the impossibility results establish that Laplace was wrong to claim that even in a classical, non-chaotic universe the future can be unerringly predicted, given sufficient knowledge of the present. Alternatively, these impossibility results can be viewed as a non-quantum-mechanical “uncertainty principle”. The mathematics of inference devices has close connections to the mathematics of Turing Machines (TMs). In particular, the impossibility results for inference devices are similar to the Halting theorem for TMs. Furthermore, one can define an analog of Universal TMs (UTMs) for inference devices. We call those analogs “strong inference devices”. We use strong inference devices to define the “inference complexity” of an inference task, which is the analog of the Kolmogorov complexity of computing a string. A task-independent bound is derived on how much the inference complexity of an inference task can differ for two different inference devices. This is analogous to the “encoding” bound governing how much the Kolmogorov complexity of a string can differ between two UTMs used to compute that string. However no universe can contain more than one strong inference device. So whereas the Kolmogorov complexity of a string is arbitrary up to specification of the UTM, there is no such arbitrariness in the inference complexity of an inference task. We informally discuss the philosophical implications of these results, e.g., for whether the universe “is” a computer. We also derive some graph-theoretic properties governing any set of multiple inference devices. We also present an extension of the framework to address physical devices used for control. We end with an extension of the framework to address probabilistic inference.

Wolpert, David H.

2008-07-01

390

ASIAN: a website for network inference  

Microsoft Academic Search

Summary: We constructed a website for inferring a network by applying the graphical Gaussian model, from a large amount of data, including redundant information. The available tools on the website are based on a system, named ASIAN (Auto- matic System for Inferring A Network), in combination with the two methods in our previous papers, which were designed to analyze gene

Sachiyo Aburatani; Kousuke Goto; Shigeru Saito; Masaki Fumoto; Akira Imaizumi; Nobuyoshi Sugaya; Hiroo Murakami; Makihiko Sato; Hiroyuki Toh; Katsuhisa Horimoto

2004-01-01

391

Common-Sense Rule Inference  

NASA Astrophysics Data System (ADS)

In the paper we show how rule-based inference can be made more flexible by exploiting semantic information associated with the concepts involved in the rules. We introduce flexible forms of common sense reasoning in which whenever no rule applies to a given situation, the inference engine can fire rules that apply to more general or to similar situations. This can be obtained by defining new forms of match between rules and the facts in the working memory and new forms of conflict resolution. We claim that in this way we can overcome some of the brittleness problems that are common in rule-based systems.

Lombardi, Ilaria; Console, Luca

392

Active inference and agency.  

PubMed

Abstract I greatly enjoyed Seth's compelling synthesis of sensorimotor contingencies and active inference. I would also like to thank Jim Hopkins for sending me the quote (below)-which speaks directly to the embodied nature of perceptual inference that underlies the perspectives reconciled in Seth (this issue). These perspectives include perception as hypothesis testing, affordance, and sensorimotor contingencies. This commentary briefly rehearses the fundaments of active inference and offers a formal basis for Seth's key argument. PMID:24702520

Friston, Karl

2014-06-01

393

Inference in belief networks: A procedural guide  

Microsoft Academic Search

Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on inference algorithms to compute beliefs in the context of observed evidence. One established method for exact inference on belief networks is the probability propagation in trees of clusters (PPTC) algorithm, as developed by Lauritzen and Spiegelhalter and refined by Jensen et al. PPTC converts the

Cecil Huang; Adnan Darwiche

1996-01-01

394

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

395

Aggregation and Inference: Facts and Fallacies  

Microsoft Academic Search

The author examines inference and aggregation problems that can arise in multilevel relational database systems and points out some fallacies in current thinking about these problems that may hinder real progress from being made toward their solution. She distinguishes several different types of aggregation and inference problems and shows that the different types of problems are best addressed by different

Teresa F. Lunt

1989-01-01

396

Explaining Type Inference  

Microsoft Academic Search

Type inference is the compile-time process of reconstructi ng missing type information in a program based on the usage of its variables. ML and Haskell are two languages where this aspect of compilation has enjoyed some popularity, allowing type information to be omitted while static type checking is still performed. Type inference may be expected to have some application in

Dominic Duggan; Frederick Bent

1996-01-01

397

Supervised Graph Inference  

Microsoft Academic Search

We formulate the problem of graph inference where part of the graph is known as a supervised learning problem, and propose an algorithm to solve it. The method involves the learning of a mapping of the vertices to a Euclidean space where the graph is easy to infer, and can be formu- lated as an optimization problem in a reproducing

Jean-philippe Vert; Yoshihiro Yamanishi

2004-01-01

398

Statistical inference and Aristotle's Rhetoric.  

PubMed

Formal logic operates in a closed system where all the information relevant to any conclusion is present, whereas this is not the case when one reasons about events and states of the world. Pollard and Richardson drew attention to the fact that the reasoning behind statistical tests does not lead to logically justifiable conclusions. In this paper statistical inferences are defended not by logic but by the standards of everyday reasoning. Aristotle invented formal logic, but argued that people mostly get at the truth with the aid of enthymemes--incomplete syllogisms which include arguing from examples, analogies and signs. It is proposed that statistical tests work in the same way--in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely. Of existing theories of statistical inference only a weak version of Fisher's takes this into account. Aristotle anticipated Fisher by producing an argument of the form that there were too many cases in which an outcome went in a particular direction for that direction to be plausibly attributed to chance. We can therefore conclude that Aristotle would have approved of statistical inference and there is a good reason for calling this form of statistical inference classical. PMID:15511303

Macdonald, Ranald R

2004-11-01

399

Prediction of the Rock Mass Diggability Index by Using Fuzzy Clustering-Based, ANN and Multiple Regression Methods  

NASA Astrophysics Data System (ADS)

Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.

Saeidi, Omid; Torabi, Seyed Rahman; Ataei, Mohammad

2014-03-01

400

Understanding and Inductive Inference  

Microsoft Academic Search

\\u000a This talk will be about different kinds of understanding, including especially that provided by deductive and inductive inference.\\u000a Examples will be drawn from Galileo, Kepler, and Fermat. Applications will be given to the physicist’s problem of inferring\\u000a the laws of nature, and to the restricted case of inferring sequences in Sloane’s Encyclopedia of Integer Sequences.\\u000a \\u000a \\u000a While there is much theory

Manuel Blum

2010-01-01

401

Neuro-fuzzy Modeling and Fuzzy Rule Extraction Applied to Conflict Management  

Microsoft Academic Search

\\u000a This paper outlines all the computational methods which have been applied to the conflict management. A survey of all the\\u000a pertinent literature relating to conflict management is also presented. The paper then introduces the Takagi-Sugeno fuzzy\\u000a model for the analysis of interstate conflict. It is found that using interstate variables as inputs, the Takagi-Sugeno fuzzy\\u000a model is able to forecast

Thando Tettey; Tshilidzi Marwala

2006-01-01

402

Type 2 fuzzy sets and neuro-fuzzy clustering of radiographic tibia images  

Microsoft Academic Search

This paper is concerned with pre-processing of data for submission to neural networks. In particular the use of type 2 fuzzy sets to assist in this process is discussed and the results of using type 2 sets with FuzzyART is presented for clustering of radiographic tibia images. These results indicate that the approach out performs a type 1 approach, for

R. I. John; P. R. Innocent; M. R. Barnes

1998-01-01

403

Type 2 fuzzy sets and neuro-fuzzy clustering of radiographic tibia images  

Microsoft Academic Search

This paper is concerned with pre-processing of data for submission to neural networks. In particular the use of type 2 fuzzy sets to assist in this process is discussed and the results of using type 2 sets with FuzzyART is presented for clustering of radiographic tibia images. These results indicate that the approach outperforms a type 1 approach, for certain

R. I. John; P. R. Innocent; M. R. Barnes

1997-01-01

404

Neuro-Fuzzy Method for Automated Defect Detection in Aluminium Castings  

Microsoft Academic Search

The automated ?aw detection in aluminium castings consists of two steps: a) identiflcation of potential defects using image process- ing techniques, and b) classiflcation of potential defects into defects and regular structures (false alarms) using pattern recognition techniques. In the second step, since several features can be extracted from the po- tential defects, a feature selection must be performed. In

Sergio Hernández; Doris Saez; Domingo Mery

2004-01-01

405

Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy  

Microsoft Academic Search

Liquefaction of soils induced by the earthquake is one of the major complex problems for the geotechnical engineering. It is generally determined from in situ tests and laboratory test of which application is very difficult, expensive and time consuming. They also require extreme cautions and labor. Hence the development of new models for the prediction of liquefaction potential of soils

C. Kayadelen

2011-01-01

406

Identification of critical genes in microarray experiments by a Neuro-Fuzzy approach  

Microsoft Academic Search

Gene expression profiling by microarray technology is usually difficult to interpret into a simpler pattern. One approach to resolve the complexity of gene expression profiles is the application of artificial neural networks (ANNs). A potential difficulty in this strategy, however, is that the non-linear nature of ANN makes it essentially a ‘black-box’ computation process. Addition of a fuzzy logic approach

Chin-fu Chen; Xin Feng; Jack Szeto

2006-01-01

407

Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators  

Microsoft Academic Search

Obtaining the joint variables that result in a desired position of the robot end-effector called as inverse kinematics is one of the most important problems in robot kinematics and control. As the complexity of robot increases, obtaining the inverse kinematics solution requires the solution of non linear equations having tran- scendental functions are difficult and computationally expensive. In this paper,

Srinivasan Alavandar; M. J. Nigam

2008-01-01

408

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

Microsoft Academic Search

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

409

A novel power swing detection algorithm using adaptive neuro fuzzy technique  

Microsoft Academic Search

Any sudden change in the configuration or the loading of an electrical network causes power swing between the load concentrations of the network. Power swing can affect the distance relay performance by entering the impedance locus into protection zone of the relay causing unnecessary tripping. In this paper a novel technique to prevent the distance relay from tripping during power

Ahad Esmaeilian; Sajjad Astinfeshan

2011-01-01

410

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

411

Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach  

Microsoft Academic Search

Stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and

Leocundo Aguilar; Patricia Melin; Oscar Castillo

2003-01-01

412

Neuro-fuzzy network for the classification of buried pipe defects  

Microsoft Academic Search

Pipeline infrastructure is decaying at an accelerating rate due to reduced funding and insufficient quality control resulting in poor installation, little or no inspection and maintenance, and a general lack of uniformity and improvement in design, construction and operation practices. The current practice that is being followed to inspect the conditions of pipes is usually time consuming, tedious and expensive.

Sunil K. Sinha; Paul W. Fieguth

2006-01-01

413

Forward Chaining Parallel Inference.  

National Technical Information Service (NTIS)

Rule based inference has demonstrated its applicability for a wide variety of domains. As users have grown more comfortable with this technology, the scope of attempted projects has grown from small laboratory demonstrations into massive real world time-c...

J. Labhart M. C. Rowe S. Carrow S. Matney

1990-01-01

414

Layered Random Inference Networks  

NASA Astrophysics Data System (ADS)

Random Boolean Networks (RBN) have been used for decades to study the generic properties of genetic regulatory networks. This paper describes Random Inference Networks (RIN) where the aim is to study the generic properties of inference networks used in high-level information fusion. Previous work has discussed RIN with a linear topology, and this paper introduces RIN with a layered topology. RIN are related to RBN, and exhibit stable, critical and chaotic dynamical regimes. As with RBN, RIN have greatest information propagation in the critical regime. This raises the question as to whether there is a driver for real inference networks to be in the critical regime as has been postulated for genetic regulatory networks. Key Words: situation assessment, inference network, information propagation, criticality

Lingard, David M.

415

Estimation and optimization of thermal performance of evacuated tube solar collector system  

NASA Astrophysics Data System (ADS)

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy (ANFIS) in order to predict the thermal performance of evacuated tube solar collector system have been used. The experimental data for the training and testing of the networks were used. The results of ANN are compared with ANFIS in which the same data sets are used. The R2-value for the thermal performance values of collector is 0.811914 which can be considered as satisfactory. The results obtained when unknown data were presented to the networks are satisfactory and indicate that the proposed method can successfully be used for the prediction of the thermal performance of evacuated tube solar collectors. In addition, new formulations obtained from ANN are presented for the calculation of the thermal performance. The advantages of this approaches compared to the conventional methods are speed, simplicity, and the capacity of the network to learn from examples. In addition, genetic algorithm (GA) was used to maximize the thermal performance of the system. The optimum working conditions of the system were determined by the GA.

Dikmen, Erkan; Ayaz, Mahir; Ezen, H. Hüseyin; Küçüksille, Ecir U.; ?ahin, Arzu ?encan

2014-05-01

416

Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study  

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

417

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

418

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

419

Towards context sensitive information inference  

Microsoft Academic Search

Humans can make hasty, but generally robust judge- ments about what a text fragment is, or is not, about. Such judgements are termed information inference. This article furnishes an account of information inference from a psychologistic stance. By drawing on theories from nonclassical logic and applied cognition, an infor- mation inference mechanism is proposed that makes inferences via computations of

Dawei Song; Peter Bruza

2003-01-01

420

Intelligent data processing of an ultrasonic sensor system for pattern recognition improvements  

NASA Astrophysics Data System (ADS)

Though conventional time-of-flight ultrasonic sensor systems are popular due to the advantages of low cost and simplicity, the usage of the sensors is rather narrowly restricted within object detection and distance readings. There is a strong need to enlarge the amount of environmental information for mobile applications to provide intelligent autonomy. Wide sectors of such neighboring object recognition problems can be satisfactorily handled with coarse vision data such as sonar maps instead of accurate laser or optic measurements. For the usage of object pattern recognition, ultrasonic senors have inherent shortcomings of poor directionality and specularity which result in low spatial resolution and indistinctiveness of object patterns. To resolve these problems an array of increased number of sensor elements has been used for large objects. In this paper we propose a method of sensor array system with improved recognition capability using electronic circuits accompanying the sensor array and neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. Relying upon the known sensor characteristics, a set of different return signals from neighboring senors is manipulated to provide an enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

Na, Seung Y.; Park, Min S.; Hwang, Won-Gul; Kee, Chang-Doo

1999-05-01

421

OFMspert - Inference of operator intentions in supervisory control using a blackboard architecture. [operator function model expert system  

NASA Technical Reports Server (NTRS)

The authors proposes an architecture for an expert system that can function as an operator's associate in the supervisory control of a complex dynamic system. Called OFMspert (operator function model (OFM) expert system), the architecture uses the operator function modeling methodology as the basis for the design. The authors put emphasis on the understanding capabilities, i.e., the intent referencing property, of an operator's associate. The authors define the generic structure of OFMspert, particularly those features that support intent inferencing. They also describe the implementation and validation of OFMspert in GT-MSOCC (Georgia Tech-Multisatellite Operations Control Center), a laboratory domain designed to support research in human-computer interaction and decision aiding in complex, dynamic systems.

Jones, Patricia S.; Mitchell, Christine M.; Rubin, Kenneth S.

1988-01-01

422

Inference of Stellar Coronal Structure  

NASA Astrophysics Data System (ADS)

Unlike the solar corona, stellar coronae cannot be --- directly --- spatially resolved at X-ray wavelengths. Yet stellar coronae are likely to exhibit similar amounts of structure as the solar corona. Currently structural information from such spatially unresolved data can be inferred from rotational modulation of the X-ray emission for single stars and/or eclipses in the case of binary systems as well as from coronal density measurements, which can be obtained from suitably chosen density sensitive line ratios. The most powerful information on structure is contained in Doppler data, however, the spectral resolution of currently X-ray available instrumentation does not permit such measurements. I will discuss some of the observations obtained, and review the methods used to infer structure from these data. Particular emphasis will be placed on the ill-conditioned nature of the inversion problem, that makes it rather difficult to infer the possibly three-dimensional structure of stellar coronae. Finally I will address the prospects of obtaining structural information on other stars with the next generation of X-ray telescopes.

Schmitt, J. H. M. M.

423

Applying a belief rule-base inference methodology to a guideline-based clinical decision support system  

Microsoft Academic Search

A critical issue in the clinical decision support system (CDSS) research area is how to represent and reason with both uncertain medical domain knowledge and clinical symptoms to arrive at accurate conclusions. Although a number of methods and tools have been developed in the past two decades for modelling clinical guidelines, few of those modelling methods have capabilities of handling

Guilan Kong; Dong-Ling Xu; Xinbao Liu; Jian-Bo Yang

2009-01-01

424

Prediction of flow fields and temperature distributions due to natural convection in a triangular enclosure using Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN)  

Microsoft Academic Search

Artificial Neural Network (ANN) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) were used to predict the natural convection thermal and flow variables in a triangular enclosure which is heated from below and cooled from sloping wall while vertical wall is maintained adiabatic. Governing equations of natural convection were solved using finite difference technique by writing a FORTRAN code to generate database

Yasin Varol; Engin Avci; Ahmet Koca; Hakan F. Oztop

2007-01-01

425

Prediction on carbon dioxide emissions based on fuzzy rules  

NASA Astrophysics Data System (ADS)

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

426

Lessons about Inferring  

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

427

Efficient local type inference  

Microsoft Academic Search

Inference of static types for local variables in Java bytecode is the first step of any serious tool that manipulates bytecode, be it for decompilation, transformation or analysis. It is important, therefore, to perform that step as accurately and efficiently as possible. Previous work has sought to give solutions with good worst-case complexity. We present a novel algorithm, which is

Ben Bellamy; Pavel Avgustinov; Oege de Moor; Damien Sereni

2008-01-01

428

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

429

Stress-induced anisotropy of partially molten media inferred from experimental deformation of a simple binary system under acoustic monitoring  

NASA Astrophysics Data System (ADS)

Microstructural changes of partially molten media under deviatoric stress were investigated in a newly developed apparatus by deforming a large sample (a 70-mm cube) under a uniform pure shear stress. Borneol + melt system having a moderate dihedral angle and texturally equilibrated under hydrostatic stress was used as a partially molten rock analogue. The applied stress was small enough not to involve cataclastic-plastic deformation of the solid grains. Shear strain rate was about 10-8 s-1, and a stress exponent indicative of diffusion creep was obtained. During the deformation, sample microstructure was observed in situ by means of ultrasonic shear waves. The development of stress-induced anisotropy was successfully detected by shear wave splitting. The results obtained indicate that grain boundary contiguity in the direction of the least compressive stress (?3) was reduced with respect to the equilibrium texture and also that the relative values of liquid pressure and ?3 play an essential role for development of anisotropy. The developed anisotropy persisted as long as deviatoric stress was applied, but the initial isotropic structure was recovered by releasing this stress. Several interesting phenomena were involved in the structural change; these include shear creep-induced dilatancy, strong dependence of the timescale of structural recovery on the amount of deformation (memory effect), and relaxation creep after releasing stress. Scaling considerations using the Griffith theory shows that the structural changes observed in the present experimental system are expected to occur in the Earth as well.

Takei, Yasuko

2001-01-01

430

"Groundwater ages" of the Lake Chad multi-layer aquifers system inferred from 14C and 36Cl data  

NASA Astrophysics Data System (ADS)

Assessment of recharge, paleo-recharge and groundwater residence time of aquifer systems of the Sahel is pivotal for a sustainable management of this vulnerable resource. Due to its stratified aquifer system, the Lake Chad Basin (LCB) offers the opportunity to assess recharge processes over time and to link climate and hydrology in the Sahel. Located in north-central Africa at the fringe between the Sahel and the Sahara, the lake Chad basin (LCB) is an endorheic basin of 2,5.106 km2. With a monsoon climate, the majority of the rainfall occurs in the southern one third of the basin, the Chari/Logone River system transporting about 90% of the runoff generated within the drainage basin. A complex multi-layer aquifer system is located in the central part of the LCB. The Quaternary unconfined aquifer, covering 500 000 km2, is characterized by the occurrence of poorly understood piezometric depressions. Artesian groundwaters are found in the Plio-Pleistocene lacustrine and deltaic sedimentary aquifers (early Pliocene and Continental Terminal). The present-day lake is in hydraulic contact with the Quaternary Aquifer, but during past megalake phases, most of the Quaternary aquifer was submerged and may experience major recharge events. To identify active recharge area and assess groundwater dynamics, one hundred surface and groundwater samples of all layers have been collected over the southern part of the LCB. Major and trace elements have been analyzed. Measurements of 36Cl have been carried out at CEREGE, on the French 5 MV AMS National Facility ASTER and 14C activities have been analyzed for 17 samples on the French AMS ARTEMIS. Additionally, the stable isotopic composition was measured on the artesian aquifer samples. In the Quaternary aquifer, results show a large scatter with waters having very different isotopic and geochemical signature. In its southern part and in the vicinity of the surface waters, groundwaters are predominantly Ca-Mg-HCO3 type waters with very high 36Cl/Cl ratio (>1000.10-15 at/at) very likely linked to the bomb pulse. These high 36Cl/Cl ratios are in the same order than the 36Cl/Cl signature of surface waters active modern recharge in this area. In the other part of the Quaternary Aquifer, waters are Na-HCO3-SO4-Cl type and are characterized by lower 36Cl/Cl ratios (around 200.10-15 at/at), suggesting longer residence time of the groundwaters. The 14C contents of the unconfined aquifer waters are all above 50 pmc, suggesting recent or Holocene recharge of this system. In contrast, the confined aquifer has a more homogeneous geochemical signature. The 14C contents are below all 0.5 pmc and mainly below detection level. 36Cl/Cl ratios are

Bouchez, Camille; Deschamps, Pierre; Goncalves, Julio; Hamelin, Bruno; Seidel, Jean-Luc; Doumnang, Jean-Claude

2014-05-01

431

Spatial and temporal geochemical trends in the hydrothermal system of Yellowstone National Park: Inferences from river solute fluxes  

NASA Astrophysics Data System (ADS)

We present and analyze a chemical dataset that includes the concentrations and fluxes of HCO 3-, SO 42-, Cl -, and F - in the major rivers draining Yellowstone National Park (YNP) for the 2002-2004 water years (1 October 2001 - 30 September 2004). The total (molar) flux in all rivers decreases in the following order, HCO 3- > Cl - > SO 42- > F -, but each river is characterized by a distinct chemical composition, implying large-scale spatial heterogeneity in the inputs of the various solutes. The data also display non-uniform temporal trends; whereas solute concentrations and fluxes are nearly constant during base-flow conditions, concentrations decrease, solute fluxes increase, and HCO 3-/Cl -, and SO 42-/Cl - increase during the late-spring high-flow period. HCO 3-/SO 42- decreases with increasing discharge in the Madison and Falls Rivers, but increases with discharge in the Yellowstone and Snake Rivers. The non-linear relations between solute concentrations and river discharge and the change in anion ratios associated with spring runoff are explained by mixing between two components: (1) a component that is discharged during base-flow conditions and (2) a component associated with snow-melt runoff characterized by higher HCO 3-/Cl - and SO 42-/Cl -. The fraction of the second component is greater in the Yellowstone and Snake Rivers, which host lakes in their drainage basins and where a large fraction of the solute flux follows thaw of ice cover in the spring months. Although the total river HCO 3- flux is larger than the flux of other solutes (HCO 3-/Cl - ? 3), the CO 2 equivalent flux is only ˜ 1% of the estimated emission of magmatic CO 2 soil emissions from Yellowstone. No anomalous solute flux in response to perturbations in the hydrothermal system was observed, possibly because gage locations are too distant from areas of disturbance, or because of the relatively low sampling frequency. In order to detect changes in river hydrothermal solute fluxes, sampling at higher frequencies with better spatial coverage would be required. Our analysis also suggests that it might be more feasible to detect large-scale heating or cooling of the hydrothermal system by tracking changes in gas and steam flux than by tracking changes in river solute flux.

Hurwitz, Shaul; Lowenstern, Jacob B.; Heasler, Henry

2007-05-01

432

Oligocene Initiation of the Central Altyn Tagh Fault System Inferred From 40Ar/39Ar K-feldspar Thermochronology  

NASA Astrophysics Data System (ADS)

Determining the timing of Cenozoic deformation within and to the north of the Tibetan plateau is one of the major problems in understanding the evolution of the Indo-Asian collision. The NE-SW striking, left-slip Altyn Tagh fault system defines the northwestern margin of the Tibetan plateau and has played a key role in the collision by transferring deformation into the interior of Asia. Between 85° E and 92° E longitude the fault system is a 100 km wide shear zone that contains two main strands: the North Altyn fault in the north and the Altyn Tagh fault to the south. Structural mapping along the westernmost 120 km of the North Altyn fault indicates Tertiary motion was dominantly left-slip, with a small reverse component. To determine the ages of these two structures we have performed 40Ar/39Ar step heating experiments on fifteen basement K-feldspar samples collected between 85° 40'E and 86° 55'E longitude. Five of the samples are from tectonic slivers within 8 km of the active trace of the Altyn Tagh fault and show clear evidence of Oligocene to Early Miocene accelerated cooling. Eight samples from along the North Altyn fault show evidence of Late Triassic to Early Jurassic rapid cooling followed by a second phase of cooling in the middle Oligocene. We interpret the data from both the tectonic slivers along the Altyn Tagh fault and basement exposures along the North Altyn fault to indicate that slip along these structures had initiated by the Early Oligocene. Furthermore, we suggest that the left-slip North Altyn fault has reactivated a Mesozoic reverse fault. Mesozoic cooling of rocks along the North Altyn fault likely reflects denudation in the hanging wall of a NW-directed reverse fault since our structural mapping indicates Middle Jurassic fanglomerates in the footwall of the North Altyn fault experienced NW-directed contractional deformation prior to development of a Cretaceous(?) angular unconformity. Ongoing modeling of the 40Ar/39Ar data in the context of multidiffusion domain theory will allow us to place more precise constraints on the timing of Oligocene cooling, the age of the Altyn Tagh fault, and the magnitude of post-Jurassic vertical separation across the North Altyn fault.

Cowgill, E.; Yin, A.; Harrison, T. M.; Grove, M.; Wang, X.

2001-12-01

433

A comparison of modern and fossil ostracods from Frasassi Cave system (northeastern Apennines, Italy) to infer past environmental conditions  

NASA Astrophysics Data System (ADS)

Cave water and sediments from an extensive sulfidic, chemioautotrophic subterranean ecosystem in the hypogenic karst complex of Frasassi (northeastern Apennines of Italy) was analysed for modern and fossil ostracode assemblages. 22 extant and 16 extinct ostracode species make of this continental sulphidic ecosystem one of the richest worldwide. Both modern and fossil assemblages show the expected pattern of species diversity after the simulation procedure for taxonomic distinctness, which indicates no major extinction events since the Pleistocene. Extant species display patchy distribution according to habitat heterogeneity within the sulphidic environment. Fossil assemblages from a 3 m thick fluvial deposit trapped near the entrance of the Caverna del Carbone (CDC) at about 30 m above present river level, and a fine sand deposit resting at about the same elevation in Sala Duecento (SDS) within the Grotta Grande del Vento preliminarily dated with OSL at 111±17 ka are being investigated. The former deposit has yet to be dated but it represents probably a normal stratigraphic succession spanning a few tens of kyr, which was deposited when the cave entrance was at the reach of fluvial flooding, potentially recording the transition from the last interglacial Riss-Würm to the glacial Würm. Sediment samples from the SDS site yielded an ostracode assemblage represented by 12 species with a d18O signature of -5‰ and a well-diversified palinoflora assemblage indicating a transitional condition between steppe and temperate forest. The top sediment from the CDC site is characterized by a less diversified ostracode assemblage represented by 8 species, d18O of -3‰, and a poorly diversified palinoflora dominated by herbaceous plants and lesser pines, indicating a colder environment in the early stage of the last glacial. Additional information on the geometric morphometry approach of B-splines method applied to extant and fossil specimens of the hypogean Mixtacandona ostracode was used to identify microevolutionary patterns and environmentally cued variation. Analyses indicate the presence of one morphotype of a new species A of the group Mixtacandona riongessa, and three distinctive morphotypes of a species B of the group M. laisi-chappuisi occurring in stratigraphically distinct fluvial-cave sediments. Apparent difference in the disparity level between these species could be associated with their survival in different environmental conditions. Species A is found nowadays living exclusively in sulphidic cave waters, and was present in the system since at least the end of the last interglacial. The extraordinary high taxonomic and morphological diversity of ostracods reflects in situ evolutionary processes that have occurred under the cumulative effect of high environmental energy availability of subterranean sulphidic ecosystems, heterogeneous environmental conditions, and spatial and temporal isolation.

Iepure, S.; Namiotko, T.; Montanari, A.; Brugiapaglia, E.; Mainiero, M.; Mariani, S.; Fiebig, M.

2012-04-01

434

Evolution and connectivity in the world-wide migration system of the mallard: Inferences from mitochondrial DNA  

PubMed Central

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.

2011-01-01

435

Long Term Variability of the Canary Current Upwelling System inferred from Fine Scale Analysis of Satellite-derived SST  

NASA Astrophysics Data System (ADS)

Satellite-derived sea surface temperature (SST) trends are built at the pixel scale to investigate long term changes in oceanic patterns. We consider that the SST time-series already available is long enough to attempt the analysis at the decadal scale. The analysis extends from 1982 to 2009 and is applied to the eastern boundary of the North Atlantic, from 10 to 45 N extending until 30 W, covering the Canary Current Upwelling System. Monthly mean SST data from the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA series satellites, with a spatial resolution of 4x4 km, were provided by the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory. SST estimates are derived from the Pathfinder Version 5 algorithms. Whenever possible the time series are limited to the night-time passes to avoid any solar heating effect. Only high quality SST values with a flag assignment of 6 and 7 (Kilpatrick et al., 2001) are used. Using only the highest quality values creates a data processing problem that is posed by the voids left on the temperature grids on the node positions corresponding to the rejected values. The case is further complicated by the fact that the voids, originated by unfavourable weather conditions of strong cloud cover and coastal fogs, most intense during strong upwelling events, are located on variable positions on the grids depending on the month the grid refers to. This is particular evident on the winter months where large areas of the ocean did not have any reliable measure, even on a monthly average. We apply several procedures to fill these data gaps that guarantee that annual and seasonal averages are not biased towards summer temperatures. To investigate the spatial variability of the long term SST trend a robust linear fit was applied to each individual pixel, crossing along the time the same 4x4 km pixel in all the processed monthly mean AVHRR SST images from 1982 until 2009. Fields of SST trends were created upon the slopes of the linear fits applied to each pixel. They show a generalized warming of the entire region. However, alternate patches of significantly different warming rates are observed, ranging from large scale down to mesoscale sized features that corresponds to known oceanographic structures, like coastal upwelling features. Going deep into short spatial scales, the spatial heterogeneities of the ocean became evident, revealing the importance of the mesoscale in the response to the global warming. The detail in the warming variability obtained here results in a large extent from the fine scale analysis and the numerical processing carefully designed to avoid trend bias in the climatological studies. Kilpatrick, K., G. Podestá, and R. Evans (2001), Overview of the NOAA/NASA Pathfinder algorithm for sea surface temperature and associated match-up database, J. Geophys. Res., 106, 9179-919

Relvas, P.; Luis, J. M.; Silva, P. L.; Santos, A. M.

2011-12-01

436

Inference Rules and Inferential Distributions.  

National Technical Information Service (NTIS)

We introduce the concept of inferential distributions corresponding to inference rules. Fiducial and posterior distributions are special cases. Inferential distributions are essentially unique. They correspond to or represent inference rules and are defin...

E. E. M. van Berkum H. N. Linssen D. A. Overdijk

1994-01-01

437

Making Inferences Using Graphic Organizers  

NSDL National Science Digital Library

Students will use a graphic organizer to practice the skill of making inferences with the help of a picture book by Chris Van Allsburg. They will then continue to practice the skill with a making inferences worksheet.

2013-03-06

438

Identifying inferences in focus  

Microsoft Academic Search

Conventional approaches to 'the syntax-semantics in terface' concentrate on matching static syntactic structures directly to semantic forms. Th is fails to account for the possibility that underspecified and\\/or procedural meaning may be grammatically encoded, creating observed truth-conditional interpretations only via inferent ial processes. Here I argue that the Hungarian 'focus position' provides evidence for the latter k ind of analysis,

Daniel Wedgwood

439

INFERENCES FROM ROSSI TRACES  

SciTech Connect

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

440

Inferring shape evolution  

Microsoft Academic Search

Dynamic shapes, namely shapes that change with time, represent an important issue in several scientific and technological contexts. The current article presents a model-based mathematic-computational approach for inferring the processes governing some of the most representative types of shape evolution, with special attention given to biological shapes. The considered models include functional mappings, convolution-based evolution and normal wavefront propagation. The

L. Da Fountoura; A. G. Campos Bianchi

2001-01-01

441

A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: An example from Kangan and Dalan reservoirs in South Pars Gas Field, Iran  

NASA Astrophysics Data System (ADS)

Total organic carbon (TOC) content present in reservoir rocks is one of the important parameters, which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon-bearing units. In general, organic-rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher ?-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into 87 training sets to build the CMIS model and 37 testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC.

Kadkhodaie-Ilkhchi, Ali; Rahimpour-Bonab, Hossain; Rezaee, Mohammadreza

2009-03-01

442

Bayesian inference in physics  

NASA Astrophysics Data System (ADS)

Bayesian inference provides a consistent method for the extraction of information from physics experiments even in ill-conditioned circumstances. The approach provides a unified rationale for data analysis, which both justifies many of the commonly used analysis procedures and reveals some of the implicit underlying assumptions. This review summarizes the general ideas of the Bayesian probability theory with emphasis on the application to the evaluation of experimental data. As case studies for Bayesian parameter estimation techniques examples ranging from extra-solar planet detection to the deconvolution of the apparatus functions for improving the energy resolution and change point estimation in time series are discussed. Special attention is paid to the numerical techniques suited for Bayesian analysis, with a focus on recent developments of Markov chain Monte Carlo algorithms for high-dimensional integration problems. Bayesian model comparison, the quantitative ranking of models for the explanation of a given data set, is illustrated with examples collected from cosmology, mass spectroscopy, and surface physics, covering problems such as background subtraction and automated outlier detection. Additionally the Bayesian inference techniques for the design and optimization of future experiments are introduced. Experiments, instead of being merely passive recording devices, can now be designed to adapt to measured data and to change the measurement strategy on the fly to maximize the information of an experiment. The applied key concepts and necessary numerical tools which provide the means of designing such inference chains and the crucial aspects of data fusion are summarized and some of the expected implications are highlighted.

von Toussaint, Udo

2011-07-01

443

Multimodel inference and adaptive management  

USGS Publications Warehouse

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide