Top PDF ECG Classification with an Adaptive Neuro-Fuzzy Inference System

ECG Classification with an Adaptive Neuro-Fuzzy Inference System

ECG Classification with an Adaptive Neuro-Fuzzy Inference System

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non- invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.
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Adaptive Neuro Fuzzy Inference System control of active suspension system with actuator dynamics

Adaptive Neuro Fuzzy Inference System control of active suspension system with actuator dynamics

The real time control of active suspension used in this paper is shown in Fig. 9(a). The low cost experimental setup consists of a HILINK microcontroller board manufactured by Zeltom Educational and Industrial Control System Company, a corresponding Simulink library for Matlab/Simulink, DC motor with encoder, and quarter car suspension test rig. A pneumatic system with compressor simulate the various road profile for suspension system. The relative distance between sprung and unsprung mass can be measured by rack and pinion arrangement to control the suspension travel of active suspension system.
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Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)

Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)

Abstract: Epilepsy is a disease that attacks the brain and results in seizures due to neurological disorders. The electrical activity of the brain recorded by the EEG signal test, because EEG test can be used to diagnose brain and mental diseases such as epilepsy. This study aims to identify whether a person has epilepsy or not along with the result of accurate, sensitivity, and precision rate using Fast Fourier Transform (FFT) and Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The FFT is used to transform EEG signals from time-based into frequency-based and continued with feature extraction to take characteristics from each filtering signal using the median, mean, and standard deviations of each EEG signal. The results of the feature extraction used for input on the category process based on characteristics data (classification) using ANFIS. EEG signal data is obtained from epilepsy center online database of Bonn University, German. The results of the EEG signal classification system using ANFIS with two classes (Normal-Epilepsy) states accuracy, sensitivity, and precision of 100%. The classification systems with three class division (Normal-Not Seizure Epilepsy-Epilepsy) resulted in an accuracy of 89.33% sensitivity of 89.37% and precision of 89.33%.
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Analysis of Liver Cancer using Adaptive Neuro Fuzzy Inference System (ANFIS)

Analysis of Liver Cancer using Adaptive Neuro Fuzzy Inference System (ANFIS)

ABSTRACT: In the fast developing world, cancer is one of the deadly diseases. Although there is much medical advancement, curing of cancer disease is not achievable. Among the cancer disease, Liver cancer is the second leading cause of cancer death. Liver cancer occurs when the liver cancer cells develops and changes in the DNA sequence. When these cells may begin to grow out of control and form a tumor in the liver. In 2012, 746,000 deaths are occurred due to liver cancer. Hence, new metrologies for early Liver Cancer are needed to determine the detection of the Liver Cancer. The aim of this paper is to study the Adaptive Neuro Fuzzy Inference System (ANFIS) technique in determining the liver cancer in human.
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A framework of modified adaptive neuro-fuzzy inference engine

A framework of modified adaptive neuro-fuzzy inference engine

METHODOLOGY 3.1 Overview 3.2 Design of a Modified Adaptive Fuzzy Inference Engine MAFIE 3.2.1 Fuzzy Inference System FIS 3.2.2 Hybrid Fuzzy clustering algorithm for Automatic Generation [r]

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Survey of fuzzy inference model and impact on QOS parameters using 
		adaptive neuro fuzzy inference system in MANET

Survey of fuzzy inference model and impact on QOS parameters using adaptive neuro fuzzy inference system in MANET

MANET is a mobile adhoc network where every node joins and leaves a network. The proposed method ensures scalability of the adhoc network and provides Optimized QoS parameters. By using fuzzy Inference system, the network attains better performance. The proposed scheme showed significant improvements in terms of packet delivery ratio, packet delay and various overheads. This work can be extended by including more number of QoS input parameters and fuzzy rule sets for better routing process.

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Electricity Consumption Forecasting Using Adaptive Neuro Fuzzy Inference System (ANFIS)

Electricity Consumption Forecasting Using Adaptive Neuro Fuzzy Inference System (ANFIS)

Abstract Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993. Therefore, it is crucial to have accurate future electricity consumption forecasting for its future energy management and saving. Even though there are previous works of electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS), but most of their data are multivariate data. In this study, we have only univariate data of UTHM electricity consumption from January 2009 to December 2018 and wish to forecast 2019 consumption. The univariate data was converted to multivariate and ANFIS was chosen as it carries both advantages of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS). ANFIS yields the MAPE between actual and predicted electricity consumption of 0.4002% which is relatively low if compared to previous works of UTHM electricity forecasting using time series model (11.14%), and first-order fuzzy time series (5.74%), and multiple linear regression (10.62%).
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Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract — The aim of the present study is to explore applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood for the case study, Dharoi Dam on the Sabarmati river near village Dharoi in Kheralu Taluka of Mehsana District in Gujarat State, India. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of Determination (R 2 ) and Discrepancy Ratio (D) are used to evaluate performance of the ANFIS models in forecasting flood. This objective is accomplished by evaluating the model by comparing ANFIS model to Statistical method like Log Pearson type-III method to forecasting flood. This comparison shows that ANFIS model can accurately and reliably be used to forecast flood in this study.
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Adaptive Neuro Fuzzy Inference System based Optical Character Recognition

Adaptive Neuro Fuzzy Inference System based Optical Character Recognition

612 | P a g e detect a character at the first see, but a computer or digital system can not do it unless it is familiar or trained with previous records. There are many algorithms, ways and methods of doing such job. Artificial intelligence[2] has a field of pattern recognition for recognising patterns such as character,face, image,voice etc. Artificial neural network[3], support vector machine[4] and various data mining techniques are being used for character recognition. Adaptive neuro fuzzy inference system (ANFIS)[5] is now a advance field of artificial intelligence. Various types of problems are being solved by ANFIS. Being a current important issue, there are a lot of research works on optical character recognition.
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Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System

Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System

Predicting the rainfall is a complex process due to the incidence of rain is non linear and dynamic. Many studies have tried to modeling rainfall forecasting to predict accurately. Such as statistical model approach like Multiple Linear Regression and artificial intelligence approaches like Fuzzy Inference System [8], Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Comparison of these approaches show ANFIS and GA predict more accurate than other methods. And ANFIS that use hybrid training method give better results [9]. Another study shows that ANFIS is better than ANN in forecasting rainfall monthly [10]. Since the popularity of ANFIS in predicting rainfall, further investigation is needed in forecasting rainfall using ANFIS.
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Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

The models that have been study in this paper are using adaptive neuro-fuzzy inference system with weather radar data to prove accuracy of prediction. It used object-based approach using the fuzzy logic, as well as segmentation technique and a feature extraction procedure has been developed [22]. For this reason, rainfall data and water discharge data are important to prediction of flood in Pahang. ANFIS is the most popular with other techniques because it has efficient function to get the output of experiment in study cases.

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Reasoning of the student’s performance based on adaptive neuro-fuzzy inference system

Reasoning of the student’s performance based on adaptive neuro-fuzzy inference system

2.7.3 Case Study 3: A Neuro Fuzzy Inference System for Student Modeling In Web Based Intelligent Tutoring Systems 2.7.4 Summarization of Related Researches of ANFIS

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Optimum Setting Strategy for WTGS by Using an Adaptive Neuro Fuzzy Inference System

Optimum Setting Strategy for WTGS by Using an Adaptive Neuro Fuzzy Inference System

For the WTGS control, usually, we just concern the con- trol method more. However, in the industrial process, the correct setting values also matter a lot. As to realize the maximum energy capture in the operation process of WTGS, we need to provide the optimum setting values of rotor speed ω r and mechanical power P m . In gener- al case, we use the measured wind speed values to esti- mate the optimum P m . And the optimum C P is used to set the optimum ω r . However, in the practice, the effi- ciency of the wind energy conversion process may be changed and the optimum C P may have some drift with time and varied environment. Thus, the setting values determined by the initial status of WTGS need to be up- dated according to the current operating status of WTGS. Based on the SCADA system, we can establish the profile mapping between ω r meas , P m meas and V . Then, using the estimated wind speed V ˆ , we can search out the primarily optimum output power P m opt and rotor speed ω r opt corresponding to the current status, ω r meas and P m meas .
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Modelling Unconfined Groundwater Recharge Using Adaptive Neuro-Fuzzy Inference System

Modelling Unconfined Groundwater Recharge Using Adaptive Neuro-Fuzzy Inference System

Abstract: Estimating groundwater recharge using mathematical models such as water budget or soil water balance method has been proved to be very difficult due to the complex, uncertain multidimensional nature of the process, despite the simplicity of the concept. Artificial Intelligence (AI) techniques have been proposed to deal with this complexity and uncertainty in a similar way to human thinking and reasoning. This study proposed the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model unconfined groundwater recharge using a set of data records from Kaharoa monitoring site in the North Island of New Zealand. Fifty-three data points, comprising a set of input parameters such as rainfall, temperature, sunshine hours, and radiation, for a period of approximately four and a half years, have been used to estimate ground water recharge. The results suggest that the ANFIS model is overall a reliable estimator for groundwater recharge, the correlation coefficient of the model reached 93% using independent data set. The method is easy, flexible and reliable; hence, it is recommended to be used for similar applications.
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Adaptive Neuro Fuzzy Inference System (ANFIS) for Generation of Joint Angle Trajectory

Adaptive Neuro Fuzzy Inference System (ANFIS) for Generation of Joint Angle Trajectory

ANFIS is the blend of a neural network and fuzzy inference system. Using a given input/output data set, the ANFIS constructs a fuzzy inference system (FIS) whose membership function parameters are adjusted using either a back propagation algorithm alone or in combination with a least squares type of method. This adjustment allows fuzzy systems to learn from the data used for modeling. Therefore, the data used for training this system plays an important role in demonstrating the effectiveness of the system. The joint space of the robot can be considered as an inverse image of the Cartesian space and vice versa. Similarly, the forward kinematics can be assumed to be an inverse image of inverse kinematics and vice versa. Based on this, it is decided to employ forward kinematics relations for determining the pose of the end-effector, i.e. P={X, Y, Z, Roll, Pitch, Yaw} corresponding to Q={ 1 ,  2 ,  3 ,  4 ,  5 }. Hence, the
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An Adaptive Neuro-Fuzzy Inference System for the Qualitative Study of Perceptual Prominence in Linguistics

An Adaptive Neuro-Fuzzy Inference System for the Qualitative Study of Perceptual Prominence in Linguistics

The use of fuzzy systems, and in particular of the ANFIS inference system, has interesting applications in the domain of phonetic research. While most of the machine learning approaches used to investigate perceptual phenomena are designed with the specific goal of automatic annotation, ad- vanced statistical modelling providing an interpretable descrip- tion of the decision process estimated from training data may represent a powerful tool to investigate complex relationships among acoustic features. In the case of syllabic prominence, we have shown that ANFIS control surfaces can be interpreted from a phonetic perspective to deepen the understanding re- searchers have about how this is conveyed by human speakers. While the dataset considered in this work is limited, results coherent with the literature and interesting new perspectives have been reported. Future work will consist of applying this tool to larger datasets, also taking into account more complex features, like pitch movements inside the syllable nucleus.
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Computation of Magnetic Field Distribution by Using an Adaptive Neuro-Fuzzy Inference System

Computation of Magnetic Field Distribution by Using an Adaptive Neuro-Fuzzy Inference System

It is supposed that there is three input linguistic variables x,y,z which describes height above ground level, magnetic field, relative values of magnetic field and each variable has five fuzzy sets. Fig. 5 shows a Sugeno fuzzy system with three inputs, one output and 125 rules, [16].Takagi-Sugeno- type fuzzy if-then rule is could be set up as:

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An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction

An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction

Figure 5.31: Dataset represents multiple hysteresis events modelled (two-input modelling approach) by 10 membership functions of gaussmf trained for 10,000 epochs ...130.. Figure 6.1: R[r]

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Optimization of adaptive neuro fuzzy inference system based urban growth model

Optimization of adaptive neuro fuzzy inference system based urban growth model

Asselt’s categories for futures studies that more strongly emphasizes cognitive uncertainty is foresight which deals with multiple possible and plausible future (Veenman 2013). Foresight draws conclusions for the present and is therefore a broad range policy instrument that can serve various objectives (Cuhls 2000). In fact, Foresight is pre- sented in a scenario study as a rich detailed portrait of a plausible future world, or as future states of a system (Ber- rogi 1997). A scenario is not a forecast but a plausible description of what might occur (Enserink et al. 2010). In foresight studies, future images are given with two or more scenarios (Schwartz 1991; Goodwin and Wright 2010). It is uncertain which trends develop, continue or stop, and which unexpected events might happen, since multiple, alternative futures are possible in foresight analysis (Veen- man 2013). Normative, is the third category of future stud- ies of Asselt et  al. (2010). In contrast to forecasting and foresight studies, normative futures studies favor norma- tiveness instead of trying to be ‘neutral’ (Veenman 2013). The normative studies include two branches: backcast- ing and critical futures studies. Backcasting is concerned with how desirable futures can be created, rather than what futures are likely to occur. In backcasting, one envi- sions a desired future endpoint, and then works backward to determine what policy measures would be required to achieve such a future. Critical future studies emphasizes that images of possible futures are not neutral but repre- sent particular desires, values, cultural assumptions and world views (Asselt et al. 2010). Such future studies sketch a future that is considered ideal, for example, a situation of peace and tolerance, or a situation where the environ- mental burden is minimised. These types of future studies do not attempt to imagine one or more possible images of the future or one or more possible images of development without a statement being made about the desirability of it. According to Asselt’s category, this paper implemented the first category, forecasting.
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Medium Term Load Forecasting using Adaptive Neuro Fuzzy Inference System

Medium Term Load Forecasting using Adaptive Neuro Fuzzy Inference System

Medium term load forecasting (MTLF) [1] covers the horizon from one month up to a few years ahead. Medium-term and long-term load forecasting plays an important role in power system planning, and short-term load forecasting is critical for reliable and efficient operation of power systems. While medium-term and long-term load forecasting is mainly based on the prediction of the future economy status, growth rate of the population. Load forecasts allow utilities to plan their operations such as unit commitment and generator maintenance beforehand, and thus, serve their customers with more reliable and more economically efficient electric power [2]. The geographical location, population, social factors, and weather factors have different effects on the systems and therefore these systems have different types of load patterns. The financial consequences for forecast errors are so significant that even a small fraction reduction in the forecast error can cause major financial benefits for the utility [3]. Accurate forecasts lead to substantial savings in operating and maintenance costs, increased reliability of power supply and delivery system, and correct decisions for future development. The load dispatcher at main dispatch center must anticipate the load pattern well in advance so as to have sufficient generation to meet the load
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