Capability Of Fuzzy Logic Research Articles

Forecasting the success of a new tourism service by a neuro-fuzzy technique

This paper presents a novel approach to forecasting the success of a newly launched service in tourism by using a hybrid intelligence system called the Adaptive Neuro Fuzzy Inference System (ANFIS). Recent studies have addressed the problem of modeling the success of a newly launched service by using different methods including artificial intelligence and model-based approaches. The ANFIS combines both the learning capabilities of a neural network and the reasoning capabilities of fuzzy logic to give enhanced forecasting capabilities, as compared to using a single methodology alone. Data collected through a questionnaire that concerns the variables of developing a new service in tourism have been used as inputs to the model. A new technique that is achieved by using a method that cycles through all the inputs and builds ANFIS models has been used for input reduction and input selection. The final model has been trained by leaving out a part of the data. The model was then evaluated by the data that were left out. The forecasting accuracy of the ANFIS model is evaluated by calculating well-known performance measures. The results have shown that ANFIS provides a prudent way to capture uncertainty in relationships among input variables and output variables to forecast the successful launch of a new tourism service. A comparative analysis with other methodologies confirms the superiority of the proposed approach.

Wide-Area-Based Adaptive Neuro-Fuzzy SVC Controller for Damping Interarea Oscillations

Low-frequency interarea oscillation is a major problem in interconnected power systems with weak tie-lines that causes several stability problems if not damped. Fuzzy logic controller can generate human knowledge-based control rules to solve complex nonlinear problems. Unlike a neural network, fuzzy systems cannot learn from data, and it takes a long time to modify the membership functions. The adaptive neuro-fuzzy inference system (ANFIS) is a robust and intelligent system that integrates the capabilities of fuzzy logic and neural networks with several advantages such as adaptability, robustness, rapidity, and flexibility. In this paper, an ANFIS-based controller is proposed for controlling the reactive power provided by static var compensator to damp interarea oscillations. The controller input is a remote signal provided by a wide-area measurement system, and it is calculated as the center-of-inertia difference of generator rotor speed deviations. Moreover, a proportional-plus-derivative time-delay compensator with adaptive parameters is added to the controller to reduce the influence of time delay. A two-area four-machine test system is used and simulated with a Simulink-based package developed for the work of this paper. The time-domain simulations and frequency response analysis demonstrate the capability of the proposed controller to effectively damp interarea oscillations, under a small- and large-scale disturbances and against a wide range of time delays and load uncertainty.

Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique

Fault identification and its diagnosis is an important issue in present scenario of power system, as huge amount of electric power is utilized. Random types of faults occur in substation, which leads to irregular and discontinue supply of power from generating to consumer point. Fault detection is an important concept of power system which is to be studied and new method has to develop for fault detection and removal of it. This paper proposed on-line fault detection and identification of fault-type by using Neuro-Fuzzy method in substation. Combination of Artificial Neural Network (ANN) and Fuzzy Logic (FL), results in gaining learning capabilities of fuzzy logic. Variation of current according to fault is used for identification. Fuzzy controller display output condition in form of (0,1).Here, single line-to ground (LG) fault, line-to-line (LL) fault, double line-to ground (LLG)/ LLL fault are considered.

Predicting CO2 permeability of bituminous coal using statistical and adaptive neuro-fuzzy analysis

The permeability of coal is considered as one of the critical parameters for coal bed methane (CBM) extraction and for evaluating the ability of coal seams to transport and store anthropogenic carbon dioxide. The heterogeneity in coal from basin to basin, inter and intra seams makes the estimation of permeability more critical and complex. In this work, permeability of Indian coal was evaluated at varied injection pressure and effective stresses using adaptive neuro-fuzzy inference system (ANFIS) technique and the outcomes were compared with the traditional statistical method of multiple regression analysis (MRA). The uniqueness of this technique lies in the fact that it integrates the learning capacity of neural networks and reasoning capabilities of fuzzy logic. For the development of prediction model, 48 datasets measured from laboratory analysis were used in such a way that the first set of 38 datasets were corresponding to the input parameters to train the models and 10 datasets were used to test the accuracy of the developed models. The performance capacity of the predictive models was evaluated based on coefficient of determination (R2), the mean absolute percentage error (MAPE) and the root mean square error (RMSE). The ANFIS predictive model had the R2, MAPE and RMSE equal to 0.9989, 1.8535% and 0.0002, respectively, supersede the performance of the MRA. Thus, the results from the present study are very encouraging and can be used to predict the permeability of coal.

Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

Minimal Undefinedness for Fuzzy Answer Sets

Fuzzy Answer Set Programming (FASP) combines the non-monotonic reasoning typical of Answer Set Programming with the capability of Fuzzy Logic to deal with imprecise information and paraconsistent reasoning. In the context of paraconsistent reasoning, the fundamental principle of minimal undefinedness states that truth degrees close to 0 and 1 should be preferred to those close to 0.5, to minimize the ambiguity of the scenario. The aim of this paper is to enforce such a principle in FASP through the minimization of a measure of undefinedness. Algorithms that minimize undefinedness of fuzzy answer sets are presented, and implemented.

AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR FORECASTING AUSTRALIA’S DOMESTIC LOW COST CARRIER PASSENGER DEMAND

This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model‘s training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

Oswestry Disability Analysis of Fuzzy Control Multi-cup Electric Cupping System

A multi-cup electric cupping system (MECS) was proposed, based on the ancient cupping method. MECS consisted of several cups that could be used simultaneously to treat 85 lumbago patients. Each cup was equipped with its own pump and pressure-monitoring system. The vacuum pressure of the cups was controlled using fuzzy logic. Through automated control of the vacuum pressure, long-term relief of muscle tightness was achieved. To develop a scientific foundation for this alternative treatment, we compared the Oswestry Disability Index (ODI) scores from conventional basic cupping to the ODI scores for our proposed MECS. The ODI scores using MECS decreased from 11.71 ± 1.61 before treatment to 4.81 ± 1.48 and 1.87 ± 1.61 after three and five treatments, respectively. The improvement rate in the ODI scores using MECS after three treatments was higher than that achieved by basic cupping. These results, combined with the convenience offered by enhanced information technology and fuzzy logic capabilities, should increase the efficiency of this device, and facilitate the opportunity to further explore the potential of Oriental medical practices.

An adaptive neuro-fuzzy inference system for modelling Australia's regional airline passenger demand

This study has proposed and empirically tested for the first time two adaptive neuro-fuzzy inference system (ANFIS) models for forecasting Australia's regional airline passenger demand, as measured by enplaned passengers (PAX model) and revenue passenger kilometres performed (RPKs model). In ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalised in order to increase the model's training performance. The results found that the mean absolute percentage error (MAPE) for the out of sample testing dataset of the PAX and RPKs models was 5.40% and 6.91%, respectively.

Adaptive fuzzy backstepping controller design for uncertain underactuated robotic systems

In this paper, the control problem of underactuated systems with mismatched and matched uncertainties is addressed. Adaptive fuzzy backstepping controller is proposed to solve the problem, ensuring the robustness against uncertainties and disturbances. Taking a general class of underactuated robotic systems into account, the nonlinear dynamical equations are first transformed to the so-called cascade form and then, an adaptive-based controller is constructed using the capability of fuzzy logic to tackle the perturbations. From the analytic point of view, the closed loop stability is ensured using the Lyapunov stability theorem. To demonstrate the effectiveness of the method, the proposed controller has been applied to a two-wheeled self-balancing robot with three degrees of freedom, and also to a pendubot with two degrees of freedom. In order to highlight the superiority of the proposed algorithm, the performance is compared with that of an existing robust strategy.

New technology product demand forecasting using a fuzzy inference system

This work presents a fuzzy inference system for forecasting the product demand of a new technology. Recent studies have addressed the problems of new technology product demand forecasting using different methods including artificial neural networks and model-based approaches. In this study, we propose to use a hybrid intelligent system called adaptive neuro-fuzzy inference system (ANFIS) for forecasting computer demand. In ANFIS, both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic are combined in order to provide enhanced forecasting capabilities, compared to using a single methodology alone. After training ANFIS and checking for forecasting, it was found that the root-mean-square error and other common error measures can be reduced in comparison with two other conventional models (autoregressive and autoregressive moving average).

A Fuzzy Logic Model for Predicting Dipole Shear Sonic Imager Parameters From Conventional Well Logs

Sonic wave velocities, including compressional, shear, and Stoneley, are functional and practical parameters for various branches of reservoir characterization. In the last few years, several tools such as the dipole shear sonic imager (DSI) were introduced for measuring sonic wave velocities. Most of these instruments are expensive and drilling companies do not run them in all wells of a field because of economical restrictions. In this study, an accurate, intelligent, and indirect method was presented for prediction of sonic wave velocities, which are directly obtained from dipole shear sonic log, utilizing conventional well log data and fuzzy logic technique. Furthermore, the proposed methodology was applied on a carbonate reservoir of Iran. The results of the case study demonstrated the capabilities of fuzzy logic for estimation of sonic wave velocities, where DSI may not be run. The MSEs of the predicted Vp, Vs , and Vst in the test data calculated 1.47 (US/F), 4.96 (US/F), and 2.219 (US/F), respectively, which correspond to the R 2 values of 91.8%, 89.3%, and 90.4%, respectively.

Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks

AbstractHigh-strength concretes (HSC) were prepared with five different binder contents, each of which had several silica fume (SF) ratios (0–15%). The compressive strength was determined at 3, 7, and 28 days, resulting in a total of 60 sets of data. In a fuzzy logic (FL) algorithm, three input variables (SF content, binder content, and age) and the output variable (compressive strength) were fuzzified using triangular membership functions. A total of 24 fuzzy rules were inferred from 60% of the data. Moreover, the FL model was tested against an artificial neural networks (ANNs) model. The results show that FL can successfully be applied to predict the compressive strength of HSC. Three input variables were sufficient to obtain accurate results. The operators used in constructing the FL model were found to be appropriate for compressive strength prediction. The performance of FL was comparable to that of ANN. The extrapolation capability of FL and ANNs were found to be satisfactory.

<b>Designing an intelligent system to predict drill wear by using of motor current and fuzzy logic method</b> - doi: 10.4025/actascitechnol.v35i4.15647

In automation flexible manufacturing systems, tool wear detection during the cutting process is one of the most important considerations. This study presents an intelligent system for online tool condition monitoring in drilling process. In this paper, analytical and empirical models have been used to predict the thrust and cutting forces on the lip and chisel edges of a new drill. Also an empirical model is used to estimate tool wear rate and force values on the edges of the worn drill .By using the block diagram of machine tool drives, the changes in the feed and spindle motor currents are simulated, as wear rate increases. To predict tool wear rate, fuzzy logic capabilities have been used to develop an intelligent system. The simulation results presented with MATLAB software show the effectiveness of proposed system for on-line drill wear monitoring. This is confirmed by comparing the measured and estimated values with each other in which the value of R2 was obtained 0.9367 in the regression graph.

Fuzzy Neural Network Hybrid Learning Control on AUV

A novel controller based on the fuzzy B-spline neural network is presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as fuzzy functions. A hybrid learning algorithm of the controller is proposed as well. The results show that it is feasible to design the fuzzy neural network control of autonomous underwater vehicle by the hybrid learning algorithm.

English

  Determination of the status of water quality of a river or any other water source is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to show the type of water treatment that would be used to meet different demands. By exploring the behavior and limitations of conventional methods for quality evaluation, a better overall index for water quality in Iran and its application in Karoon River is proposed. Six variables are employed for the quality assessment. Numerical scales relating to the degree of quality are established for each variable to assess variations in quality and to convey findings in a comprehensive manner. The unit operates in a fuzzy logic mode including a fuzzification engine receiving a plurality of input variables on its input and being adapted to compute membership function parameters. A processor engine connected downstream of the fuzzification unit will produce fuzzy set, based on fuzzy variable namely dissolved oxygen (DO), total dissolved solids (TDS), turbidity, nitrate, fecal coliform and pH. It has a defuzzification unit which operates to translate the inference results into a discrete crisp value of water quality index. The development of the fuzzy model with one river system is explained in this paper. Water quality index in most countries is only referring to physico-chemical parameters due to great efforts needed to quantify the biological parameters. This study ensures a better method to include special parameters into water quality index due to superior capabilities of fuzzy logic in dealing with non-linear, complex and uncertain systems.   Key words: Water quality index, fuzzification, monitoring, inference system.

Fuzzy modeling and hybrid genetic algorithm optimization of virus removal from water using microfiltration membrane

Membranes are finding increasing applications in disinfection processes including virus removal from water for municipal effluent reuse. The capability of virus removal from water by microfiltration membranes has previously been demonstrated. In this study, the capability of fuzzy logic for modeling and simulation of dead-end microfiltration process for removal of IBR and FMD viruses from water was elucidated. The main parameters indicating membrane performance i.e. flux and rejection were experimentally obtained under different conditions and compared with theoretically calculated flux and rejection using fuzzy inference system. The genetic algorithm which is an efficient and systematic method was employed in the design of fuzzy model for optimization of the poorly understood, irregular and complex membership function with improved performance. Hybrid genetic algorithm was used for optimizing the parameters that are located at the Gaussian membership functions in the premise and consequent of each rule. The results indicated that fuzzy inference system predicts the key parameters i.e. flux and rejection for different operating conditions with an acceptable error. In other words FIS is able to apply for modeling the microfiltration membrane which is mathematically difficult or in many cases an unpredictable process.

Impacts of Fuzzy Logic Modeling for Constraints Optimization

Today’s US naval vessels are required to perform an ever more complicated array of missions, thus increasing the number of constraints imposed on the ship design. As designers we struggle to find a means to effectively express the relationships of these competing, interdependent, and often subjective constraints. The majority of current optimization schemes require designers to define crispvalued constraints even when in the presence of subjective ‘‘soft’’ constraints, thereby ignoring the uncertainty that is inherent during the definition of early-stage design constraints. One method capable of handling these subjective constraints is Fuzzy Logic (FL). FL is a rule-based method for soft computing that can be used to model the uncertainty of crisp-valued objective data, as well as for the modeling of subjective linguistic variables. A Fuzzy Logic System (FLS) is unique in its ability to concurrently consider multiple input design constraints of different types, such as objective and subjective, and of different units, such as length and area. A key component of FL modeling is the fuzzification of crisp input design constraints. Fuzzification itself is the act of mapping a crisp input constraint to a membership function (MF), also called a fuzzy utility curve. The MFs are used by the FLS to model uncertainty of the input data. Taken the current state of optimization techniques, it is believed that further improvement in optimization may be possible by introducing the modeling capabilities of FL. In this paper the authors investigate the impacts of using FL to quantify and model the subjectivity of design constraints associated with a general ship arrangements optimization process. A brief overview of FLSs is given, followed by a discussion of the methodology used to design a FL constraint MF. The University of Michigan Intelligent Ship Arrangements (ISA) system, a program designed to assist in the development of optimized ship arrangements, is used for the platform of this study. In one example the ISA program is run using crisp constraint inputs, the program output is then compared to the output of the same run when using fuzzy inputs. This process serves to highlight the significance of utilizing FL for the modeling of design constraints during optimization. Another ISA run, showing an example of the arrangements process, is used to help the reader understand the critical process of choosing the correct fuzzifier for modeling of an input constraint.

Fuzzy Logic Water Quality Index and Importance of Water Quality Parameters

Determination of status of water quality of a river or any other water sources is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to advice for type of water treatment for meeting different demands. One such model (UNIQ2007) is developed as an application software in water quality engineering. The unit operates in a fuzzy logic mode including a fuzzification engine receiving a plurality of input variables on its input and being adapted to compute membership function parameters. A processor engine connected downstream of the fuzzification unit will produce fuzzy set, based on fuzzy variable viz. DO, BOD, COD, AN, SS and pH. It has a defuzzification unit operative to translate the inference results into a discrete crisp value of WQI. The UNIQ2007 contains a first memory device connected to the fuzzification unit and containing the set of membership functions, a secondary memory device connected to the defuzzification unit and containing the set of crisp value which appear in the THEN part of the fuzzy rules and an additional memory device connected to the defuzzification unit. More advantageously, UINQ2007 is constructed with control elements having dynamic fuzzy logic properties wherein target non-linearity can be input to result in a perfect evaluation of water quality. The development of the fuzzy model with one river system is explained in this paper. Further the model has been evaluated with the data from few rivers in Malaysia, India and Thailand. This water quality assessor probe can provide better quality index or identify the status of river with 90% perfection. Presently, WQI in most of the countries is referring to physic-chemical parameters only due to great efforts needed to quantify the biological parameters. This study ensures a better method to include pathogens into WQI due to superior capabilities of fuzzy logic in dealing with non-linear, complex and uncertain systems.

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

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 target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.