Input Membership Research Articles

Adaptive PI controller to voltage regulation in power systems: STATCOM as a case study

Static synchronous compensator (STATCOM) provides the means to improve quality and reliability of a power system as it has the functional capability to handle dynamic disturbances, such as transient stability and power oscillation damping as well as to providing voltage regulation. In this paper, a robust adaptive PI-based optimal fuzzy control strategy is proposed to control a STATCOM used in distribution systems. The proposed intelligent strategy is based on a combination of a new General Type-II Fuzzy Logic (GT2FL) with a simple heuristic algorithm named Teaching Learning Based Optimization (TLBO) Algorithm. The proposed framework optimally tunes parameters of a Proportional-Integral (PI) controller which, similar to most of other researchers regarding control of STATCOM, are in charge of controlling the device. The proposed controller guaranties robustness and stability against uncertainties caused by external disturbances or ever-changing nature of the power systems. The TLBO optimizes the parameters of the controller as well as the input and output membership functions. To validate the efficiency of the proposed controller, the obtained simulation results are compared with those of the two most recent researches applied in this field, namely, conventional Proportional Integral (PI) controller and Optimal Fuzzy PI (OFPI) controller. Results demonstrate the successfulness and effectiveness of the proposed online-TLBO General Type-2 Fuzzy PI (OGT2FPI) controller and its superiority over conventional approaches.

An optimal general type-2 fuzzy controller for Urban Traffic Network

Urban traffic network model is illustrated by state-charts and object-diagram. However, they have limitations to show the behavioral perspective of the Traffic Information flow. Consequently, a state space model is used to calculate the half-value waiting time of vehicles. In this study, a combination of the general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters of input and output membership functions are optimized simultaneously by the novel heuristic algorithm MBSA. A comparison is made between the achieved results with those of optimal and conventional type-1 fuzzy logic controllers.

Weight on drill bit prediction models: Sugeno-type and Mamdani-type fuzzy inference systems compared

Drilling optimization aims to optimize controllable variables during drilling operations, such as weight on bit (WOB), in order to improve drilling rate of penetration and reduce well costs. Prediction models of Weight on Bit (WOB) are developed using two widely-applied fuzzy inference systems (FIS), Mamdani-type and Sugeno-type, for the Ahwaz oil field and Marun gas field formations; two large producing fields in Iran. The FIS are constructed based on field data involving multiple wells; six wells from Ahwaz field, and two wells from Marun field. The controllable input variable data for the FIS includes: rate of penetration, mud weight, formation type, pump pressure, pump flow rate and rotation rate. The key difference between these two FIS techniques is the manner in which they calculate their crisp output values. The Mamdani-type FIS requires defuzzification, whereas the Sugeno-type FIS applies a constant weighted-average technique avoiding defuzzification. The results for the two field cases evaluated convincingly demonstrate that the Sugeno-type FIS is superior to the Mamdani-type FIS for WOB prediction using the same input data and membership functions. There is scope for further refinement of FIS models for WOB prediction (e.g., by adding bit type information) and the Sugeno-type FIS methods should reduce the time and cost associated with the conventional rate of penetration methods for predicting WOB.

A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices

Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013–2014. Input membership functions were defined as “desirable”, “acceptable” and “unacceptable” based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy indices developed in this research are reliable and flexible when used in groundwater quality assessment for drinking purposes.

Maritime Weather Prediction using Fuzzy Logic in Ternate Waters North Maluku

Ternate waters of North Maluku is one of Indonesian eastern waters whose maritime weather is often unpredictable. Weathers prediction is important to avoid accidents in the waters. The aim of this research is to obtain a predictor model of wave’s height and current’s speed suitable for the Ternate waters using Takagi-Sugeno fuzzy logic. The data used is data from BMKG Maritime of Bitung which recorded per 6 hours during 5 years from July 2010 – June 2015. In order to reach accuracy of > 85%, 3 model predictor’s that used waves height and current speed are predictor Model A, Model B and Model C. Each model uses different input and total membership function. The result of this research shows that the Model C is the best model for Ternate waters. Model C uses 4 membership functions for 3 input variables. Inputs of waves height predictor consist of the actual wind speed (U(t)), actual waves height (H(t)) and waves height 6 hours ago (H(t-6)) and accuracy percentage of waves height 6 hours ahead (H(t+6)) is 91,99%; while inputs of current speed predictor consist of actual wind speed (U(t)), actual current speed (Cu(t)) and current speed 6 hours ago (Cu(t-6)) and accuracy percentage of current speed 6 hours ahead (Cu(t+6)) is 81,63%.

Localization of WSN Using Fuzzy Inference System with Optimized Membership Function by Bat Algorithm

Localization is one of the most important research topics in the wireless sensor network applications. To improve the indoor localization accuracy, the centroid localization algorithm based on Mamdani fuzzy system has been adopted to attain the weight between sensor node and anchor node. This paper proposes a novel optimized input membership function by bat algorithm in fuzzy inference system using the data of received signal strength in real indoor condition. The author has realized the algorithm on Zigbee platform and the experimental comparison on other different centroid localization algorithms indicates that Mamdani fuzzy inference adopting the membership function optimized by bat algorithm renders smaller mean localization errors.

A reinforcement learning algorithm for adjusting antecedent parameters and weights of fuzzy rules in a fuzzy classifier

This paper proposes a new fuzzy classifier based on reinforcement learning. A fuzzy rule based classification system is a special type of fuzzy modeling where its output is a discrete crisp value. The main challenging issue in designing fuzzy classifiers is constructing fuzzy rule base. Here, each fuzzy rule is considered as an agent who has to select the suitable class between candidate classes. It is considered a weight for each candidate class in each rule. These weights are adjusted using the proposed reinforcement learning algorithm. For each sample of training data, if the final result is true, the winner rule (agent) is rewarded and some other rules are punished based on the criteria which are defined in this paper. If the result is false, the winner rule is punished and the rules with high firing strength that have selected correct class are rewarded. Moreover, the input membership functions of rules are adjusted regarding the defined criteria which depend on punishment frequency of rules. The proposed approach is assessed on some UCI datasets. We compare our ideas in comparison with conventional reward and punishment scheme and multi-layer perceptron network. The experimental results show that our proposed approach outperforms both mentioned approaches in the terms of quality of classification and precision.

Designing an optimized model-free controller for improved motion tracking by rugged electrohydraulic system

Electrohydraulic actuation systems with proportional valves and industry-grade cylinders have widespread use due to their low cost and ease of maintenance. Achieving good tracking of output position of the piston in such a system by a proper controller design is the objective of this study. Due to various parameter uncertainties and complex nonlinearities like piston friction, valve deadband and flow characteristics, the variation of oil property with working temperature and air entrapment volume, the implementation of a model-based control becomes quite difficult for such systems. A notable contribution of this work is to couple a model-free fuzzy controller with a feedforward controller with all the parameters estimated by a real-coded genetic algorithm. Also, the optimizer has been used for identifying an appropriate fuzzy structure that is easy to implement in real time. A comprehensive experimental study has revealed the best fuzzy structure to be achieved through a Gaussian fuzzification of linear combination of the position and velocity errors as the input and singleton membership function for the output voltage actuating the proportional valve. Excellent responses with good disturbance rejection capability and energy efficiency have been achieved. These responses have emerged superior to those achieved in a similar system with different controller structures and in systems with much costlier components.

Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

Takagi-Sugeno Fuzzy System Accuracy Improvement with A Two Stage Tuning

In this paper a method for improving accuracy of a Takagi-Sugeno type fuzzy system used in Matlab Fuzzy Logic Toolbox as genfis3 (Sugeno) is proposed. For this fuzzy system, the input space is partitioned using Fuzzy C-means (FCM) clustering algorithm and the consequent parameters are optimized using least square. This improvement is done in a two stage tuning using particle swarm optimization (PSO).In the first stage, PSO is used to optimize input membership functions (mean and variance) and consequent parameters of Takagi-Sugeno fuzzy system. In the second stage, PSO is used to optimize a weighting factor for the rules and a scaling universe of discourse for inputs and outputs variables. To simplify the tuning process and performing it in one optimization stage, a one stage tuning is also discussed to tune same parameters optimized in the two stage tuning. Experimental results with real data applied in data classification problem shows a consistency of getting higher classification accuracy with the proposed tuning methods over the original system.

Personal-Statistics-Based Heart Rate Evaluation in Anytime Risk Calculation Model

Nowadays, the changes in people’s habits and the development of technology result in the wide spreading of the model-based health monitoring systems. In this paper, an improvement of the measurement evaluation method is introduced, which is used during the sport activity in real-time. The basis of the novel approach is our previously reported anytime hierarchical fuzzy risk calculation model, which is able to handle some uncertainties, imprecision, and subjectivity in the data and in the evaluation process and can cope with the dynamically changing environment, available time, and resources. In the new model the input membership functions, which are tuned according to the patient characteristics, are modified based on the data recorded during previous measurements under the same conditions. By this, the person-dependent characteristics and the unavoidable changing of the dynamic reactions of the human organism can also be considered and the risk level can be more reliably predicted.

A new intelligent online fuzzy tuning approach for multi-area load frequency control: Self Adaptive Modified Bat Algorithm

The primary aim of the Automatic Generation Control (AGC) is to maintain system frequency and tie-line interchanges in a predestine limits by regulating the power generation of electrical generators, in case of fluctuations in the system frequency and tie-line loadings. This paper proposes a new online intelligent strategy to realize the control of multi-area load frequency systems. The proposed intelligent strategy is based on a combination of a novel heuristic algorithm named Self-Adaptive Modified Bat Algorithm (SAMBA) and the Fuzzy Logic (FL) which is used to optimally tune parameters of Proportional–Integral (PI) controllers which are the most popular methods in this context. The proposed controller guaranties stability and robustness against uncertainties caused by external disturbances and impermanent dynamics that power systems face. To achieve an optimal performance, the SAMBA simultaneously optimizes the parameters of the proposed controller as well as the input and output membership functions. The control design methodology is applied on four-area interconnected power system, which represents a large-scale power system. To evaluate the efficiency of the proposed controller, the obtained results are compared with those of Proportional Integral Derivative (PID) controller and Optimal Fuzzy PID (OFPID) controller, which are the most recent researches applied to the present problem. Simulation results demonstrate the successfulness and effectiveness of the Online-SAMBA Fuzzy PI (MBFPI) controller and its superiority over conventional approaches.

Intelligent photovoltaic farms for robust frequency stabilization in multi-area interconnected power system based on PSO-based optimal Sugeno fuzzy logic control

Currently, the grid-connected large PV farms are extensively installed in power systems. Nevertheless, in addition to the load change, the intermittent power output of PV farms may lead to the serious problem of the system frequency fluctuation. To handle this problem, this paper proposes a new design of Sugeno fuzzy logic controller based on particle swarm optimization (PSO-SFLC) of intelligent PV farms for the frequency stabilization in a multi-area interconnected power system. To handle various scenarios, the frequency deviations and solar insolations are used as input signals of the PSO-SFLC. The output signal of the PSO-SFLC is a command signal for adjusting PV output power. The output power of PV is controlled by the PSO-SFLC to meet the load demand so that the system frequency fluctuation can be suppressed. Without the difficulty of trial and error, the optimal input and output membership functions, and control rules of PSO-SFLC are automatically achieved by PSO. Simulation study in a three-area loop interconnected power system with large PV farms elucidates that the frequency stabilizing performance and robustness of the PV equipped with the PSO-SFLC is much superior to that of the PV with the SFLC and the PV with the maximum power point tracking control in scenarios with various solar insolations and loading conditions.

A Genetic Algorithm Optimised Fuzzy Logic Controller for Automatic Generation Control for Single Area System

This paper presents a genetic algorithm (GA)-based design and optimization of fuzzy logic controller (FLC) for automatic generation control (AGC) for a single area. FLCs are characterized by a set of parameters, which are optimized using GA to improve their performance. The design of input and output membership functions (mfs) of an FLC is carried out by automatically tuning (off-line) the parameters of the membership functions. Tuning is based on maximization of a comprehensive fitness function constructed as inverse of a weighted average of three performance indices, i.e., integral square deviation (ISD), the integral of square of the frequency deviation and peak overshoot (Mp), and settling time (ts). The GA-optimized FLC (GAFLC) shows better performance as compared to a conventional proportional integral (PI) and a hand-designed fuzzy logic controller not only for a standard system (displaying frequency deviations) but also under parametric and load disturbances.

SC-IDT: SOFT COMPUTING BASED INTRUSION DETECTION TECHNOLOGY IN SMART HOME SECURITY SYSTEM

In this paper a theoretical model is introduced for the purpose of home security using mamdani system in environment. Paper defines the architecture for such a system, in which the technological combination of computing and soft computing form a home security model. This model is inspired by Intrusion Detection System (IDS) and context awareness (ubicom) system. IDS categorize the normal and intrusive (malwares) data from the network traffic data and allow the normal data to be pass to the system and block the intrusive data. Ubiquitous computing is latest trend where computing can occur using any device, in any location and in any format i.e. everywhere and anywhere. By combining ubiquitous computing, IDS, and fuzzy technologies together a theoretical model of smart home security is introduced. Model consists of input parameters, output parameters and membership functions.

Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems

This paper suggests a synergy of fuzzy logic and nature-inspired optimization in terms of the nature-inspired optimal tuning of the input membership functions of a class of Takagi-Sugeno-Kang (TSK) fuzzy models dedicated to Anti-lock Braking Systems (ABSs). A set of TSK fuzzy models is proposed by a novel fuzzy modeling approach for ABSs. The fuzzy modeling approach starts with the derivation of a set of local state-space models of the nonlinear ABS process by the linearization of the first-principle process model at ten operating points. The TSK fuzzy model structure and the initial TSK fuzzy models are obtained by the modal equivalence principle in terms of placing the local state-space models in the rule consequents of the TSK fuzzy models. An operating point selection algorithm to guide modeling is proposed, formulated on the basis of ranking the operating points according to their importance factors, and inserted in the third step of the fuzzy modeling approach. The optimization problems are defined such that to minimize the objective functions expressed as the average of squared modeling errors over the time horizon, and the variables of these functions are a part of the parameters of the input membership functions. Two representative nature-inspired algorithms, namely a Simulated Annealing (SA) algorithm and a Particle Swarm Optimization (PSO) algorithm, are implemented to solve the optimization problems and to obtain optimal TSK fuzzy models. The validation and the comparison of SA and PSO and of the new TSK fuzzy models are carried out for an ABS laboratory equipment. The real-time experimental results highlight that the optimized TSK fuzzy models are simple and consistent with both training data and validation data and that these models outperform the initial TSK fuzzy models.

An adaptive neuro-fuzzy identification model for the detection of meat spoilage

In food industry, safety and quality are considered important issues worldwide that are directly related to health and social progress. To address the rapid and non-destructive detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared (FTIR) spectroscopy with the aid of a neuro-fuzzy identification model has been considered in this research. FTIR spectra were obtained from the surface of beef samples, while microbiological analysis determined the total viable count for each sample. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from FTIR spectra. The proposed model utilises a prototype defuzzification scheme, whereas the number of input membership functions is directly associated to the number of rules, reducing thus, the curse of dimensionality problem. Results confirmed the advantage of the proposed scheme against the adaptive neuro-fuzzy inference system (ANFIS), in terms of prediction accuracy and structure simplicity. Subsequent comparison against multilayer perceptron (MLP) and partial least squares technique indicated that FTIR spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

An intelligent based decision support system for the detection of meat spoilage

In food industry, safety and quality are considered important issues worldwide that are directly related to health and social progress. Meat spoilage is the result of decomposition and the formation of metabolites, caused by the growth and enzymatic activity of microorganisms, and it presents not only a health hazard but an economic burden to the producer. In this research work, we explore the potential of Fourier transform infrared (FTIR) spectroscopy in combination of principal components analysis and neuro-fuzzy modelling, to determine beef spoilage microorganisms during aerobic storage at chill and abuse temperatures. FTIR spectra were obtained from the surface of beef samples, while culture microbiological analysis determined the total viable count (TVC) for each sample. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e., fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from FTIR spectra. The proposed neuro-fuzzy network model utilises a prototype defuzzification scheme, whereas the number of input membership functions is directly associated to the number of rules, reducing thus, the “curse of dimensionality” problem. Results confirmed the superiority of the adopted methodology compared to other schemes such as multilayer perceptron and the partial least squares techniques and indicated that FTIR spectral information in combination with an efficient choice of a learning-based modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

Modeling of TiN Coating Thickness Using ANFIS

In this paper, an approach in predicting thickness of Titanium Aluminum Nitrite (TiN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. The TiN coatings were coated on tungsten carbide (WC) using Physical Vapor Deposition (PVD) magnetron sputtering process. The N2 pressure, argon pressure and turntable speed were selected as the input parameters and the coating thickness as an output of the process. Response Surface Methodology (RSM) was used to design the matrix in collecting the experimental data. In the ANFIS structure, three bell shapes were used as input membership function (MFs). The collected experimental data was used to train the ANFIS model. Then, the ANFIS model was validated with confirmatory test data and compared with other prediction models in terms of the root mean square error (RMSE), residual error and prediction accuracy. The result indicated that the developed ANFIS model result was the lowest RMSE7 and average residual error, besides the highest in prediction accuracy compared to the other models. The result also indicated that the limited experimental data could be used in training the ANFIS model and resulting accurate predictive result.

A novel self-adaptive modified bat fuzzy sliding mode control of robot manipulator in presence of uncertainties in task space

SUMMARYIn this paper, an optimal fuzzy sliding mode controller has been designed for controlling the end-effector position in the task space. In the proposed control, feedback linearization method, sliding mode control, first-order fuzzy TSK system and optimization algorithm are utilized. In the proposed controller, a novel heuristic algorithm namely self-adaptive modified bat algorithm (SAMBA) is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input membership functions are optimized by SAMBA simultaneously. In this method, the bounds of structural and non-structural uncertainties are reduced by using feedback linearization method, and to overcome the remaining uncertainties, sliding mode control is employed. Mathematical proof demonstrates that the closed loop system with the proposed control has global asymptotic stability. The presence of sliding mode control gives rise to the adverse phenomenon of chattering in the end-effector position tracking in the task space. Subsequently, to prevent the occurrence of chattering in control input, a first-order TSK fuzzy approximator is utilized. Finally, to determine the fuzzy sliding mode controller coefficients, the optimization algorithm of Self-Adaptive Modified Bat is employed. To investigate the performance of the proposed control, a two-degree-of-freedom manipulator is used as a case study. The simulation results indicate the favorable performance of the proposed method.