Type-2 Fuzzy Inference System Research Articles

Network-based Type-2 Fuzzy System with Water Flow Like Algorithm for System Identification and Signal Processing

This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS) with a dynamic solution agent algorithm water flow like algorithm (WFA), for nonlinear system identification and blind source separation (BSS) problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA is inspired by the natural behavior of water flow. Splitting, moving, merging, evaporation, and precipitation have all been introduced for optimization. Some modifications, including new moving strategies, such as the application of tabu searching and gradient-descent techniques, are proposed to enhance the performance of the WFA in training the NT2FIS systems. Simulation and comparison results for nonlinear system identification and blind signal separation are presented to illustrate the performance and effectiveness of the proposed approach.

Assessment of Food Security Risk Level Using Type 2 Fuzzy System

In the paper, the assessment of food security using type-2 fuzzy system is presented. For this purpose, the input parameters cereal yield, cereal production, and economic growth are selected and the relationship between these parameters and risk level of food security is determined. The relationship is represented by using IF-Then rules. The values of the parameters in the rule base are described using fuzzy linguistic terms represented by the type-2 fuzzy sets. Based on the fuzzy rules the design of type-2 fuzzy inference system is performed. The system is tested using Turkey cereal data for the period from 1961 to 2012.The designed system is applied for assessing food security risk level and prediction periods of impending pressure on food supply.

Software fault prediction using Mamdani type fuzzy inference system

High quality software requires the occurrence of minimum number of failures while software runs. Software fault prediction is the determining whether software modules are prone to fault or not. Identification of the modules or code segments which need detailed testing, editing or, reorganising can be possible with the help of software fault prediction systems. In literature, many studies present models for software fault prediction using some soft computing methods which use training/testing phases. As a result, they require historical data to build models. In this study, to eliminate this drawback, Mamdani type fuzzy inference system (FIS) is applied for the software fault prediction problem. Several FIS models are produced and assessed with ROC-AUC as performance measure. The results achieved are ranging between 0.7138 and 0.7304; they are encouraging us to try FIS with the different software metrics and data to demonstrate general FIS performance on this problem.

An Evolving Interval Type-2 Neurofuzzy Inference System and Its Metacognitive Sequential Learning Algorithm

In this paper, we propose an evolving interval type-2 neurofuzzy inference system (IT2FIS) and its fully sequential learning algorithm. IT2FIS employs interval type-2 fuzzy sets in the antecedent part of each rule and the consequent realizes Takagi–Sugeno–Kang fuzzy inference mechanism. In order to render the inference fast and accurate, we propose a data-driven interval-reduction approach to convert interval type-1 fuzzy set in antecedent to type-1 fuzzy number in the consequent. During learning, the sequential algorithm learns a sample one-by-one and only once. The IT2FIS structure evolves automatically and adapts its network parameters using metacognitive learning mechanism concurrently. The metacognitive learning regulates the learning process by appropriate selection of learning strategies and helps the proposed IT2FIS to approximate the input–output relationship efficiently. An evolving IT2FIS employing a metacognitive learning algorithm is referred to as McTI2FIS. Performance of metacognitive interval type-2 neurofuzzy inference system (McIT2FIS) is evaluated using a set of benchmark time-series problems and is compared with existing type-2 and type-1 fuzzy inference systems. Finally, the performance of the proposed McIT2FIS has been evaluated using a practical stock price-tracking problem. The results clearly highlight that McIT2FIS performs better than other existing results in the literature.

Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO

In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms (GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy inference systems are used to estimate the type-2 fuzzy weights of backpropagation neural networks. Simulation results and a comparative study among neural networks with type-2 fuzzy weights without optimization of the type-2 fuzzy inference systems, neural networks with optimized type-2 fuzzy weights using genetic algorithms, and neural networks with optimized type-2 fuzzy weights using particle swarm optimization are presented to illustrate the advantages of the bio-inspired methods. The comparative study is based on a benchmark case of prediction, which is the Mackey-Glass time series (for τ=17) problem.

Modeling and control of an unstable system using probabilistic fuzzy inference system

Abstract A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

Generalized type-2 fuzzy weight adjustment for backpropagation neural networks in time series prediction

In this paper the comparison of a proposed neural network with generalized type-2 fuzzy weights (NNGT2FW) with respect to the monolithic neural network (NN) and the neural network with interval type-2 fuzzy weights (NNIT2FW) is presented. Generalized type-2 fuzzy inference systems are used to obtain the generalized type-2 fuzzy weights and are designed by a strategy of increasing and decreasing an epsilon variable for obtaining the different sizes of the footprint of uncertainty (FOU) for the generalized membership functions. The proposed method is based on recent approaches that handle weight adaptation using type-1 and type-2 fuzzy logic. The approach is applied to the prediction of the Mackey–Glass time series, and results are shown to outperform the results produced by other neural models. Gaussian noise was applied to the test data of the Mackey–Glass time series for finding out which of the presented methods in this paper shows better performance and tolerance to noise.

Optimization FOU of Interval Type-2 Fuzzy Inference System Using Big Bang – Big Crunch Algorithm for Short Term Load Forecasting on National Holiday Case Study: South and Central Kalimantan-Indonesia

The application interval type-2 fuzzy inference systems (IT2FIS) has been attention for a short-term load forecasting problems solution. This paper present optimization membership function of antecedent (X,Y) and consequent (Z) interval type-2 Fuzzy Logic System using Big Bang – Big Crunch Algorithm for application short term load forecasting on national holiday. This method has implemented on the historical peak load data during 14 national holiday case study in South and Central Kalimantan – Indonesia electrical power system on year 2008. The Big Bang–Big Crunch (BB–BC) algorithm will applied to optimization interval footprint of uncertainty (FOU) membership functions of interval type-2 fuzzy logic system. BB-BC Algorithm is a global optimization algorithm and has a low computational cost and a high convergence speed, so very efficient in this research. The test result showed main absolute percentage error (MAPE) less than interval type-2 fuzzy inference system (FIS) and type-1 fuzzy inference system (FIS). Finally, this paper defined main absolute percentage error (MAPE) 0.58045% for type-1 FIS, 0.53906% for interval type-2 FIS, and 0.52421% for optimization interval type-2 FIS-Big Bang Big Crunch Algorithm.

Network-based Type-2 Fuzzy System with Water Flow Like Algorithm for System Identification and Signal Processing

This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS) with a dynamic solution agent algorithm water flow like algorithm (WFA), for nonlinear system identification and blind source separation (BSS) problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA is inspired by the natural behavior of water flow. Splitting, moving, merging, evaporation, and precipitation have all been introduced for optimization. Some modifications, including new moving strategies, such as the application of tabu searching and gradient-descent techniques, are proposed to enhance the performance of the WFA in training the NT2FIS systems. Simulation and comparison results for nonlinear system identification and blind signal separation are presented to illustrate the performance and effectiveness of the proposed approach.

Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

Abstract This paper introduces JT2FISClustering, a data mining extension for JT2FIS. JT2FIS is a Java class library for building intelligent applications. This extension is used to extract information from a data set and transform it into an Interval Type-2 Fuzzy Inference System in Java applications. Mamdani and Takagi-Sugeno Fuzzy Inference Systems can be generated using fuzzy c-means or subtractive data mining methods. We compare the outputs and performance of Matlab R versus Java in order to validate the proposed extension.

Adaptive neuro-fuzzy inference-based modeling of a full-scale expanded granular sludge bed reactor treating corn processing wastewater

In this study, an adaptive neuro-fuzzy inference system ANFIS including five process variables, such as influent chemical oxygen demand, influent flow rate, influent total Kjeldahl nitrogen, effluent volatile fatty acids and effluent bicarbonate, was described to predict the effluent chemical oxygen demand load from a full-scale expanded granular sludge bed reactor EGSBR treating corn processing wastewater. The proposed ANFIS model was conducted by applying hybrid learning algorithm and the model performance was tested by the means of distinct test data set randomly selected from the experimental domain. The ANFIS-based predictions were also validated using various descriptive statistical indicators, such as root mean-square error, index of agreement, the factor of two, fractional variance, proportion of systematic error, etc. The lowest root mean square error RMSE = 0.03655 and the highest determination coefficient R2 = 0.958 were achieved with the subtractive clustering of a first-order Sugeno type fuzzy inference system. ANFIS predicted results were compared with the outputs of multiple nonlinear regression analysis-based models derived in the scope of the present work. Statistical performance indices computed for the testing data set proved that the developed ANFIS-based model exhibited a very good precision in predicting the effluent chemical oxygen demand, load for the EGSBR system. Due to high capability of the ANFIS model in capturing the dynamic behavior and non-linear interactions, it was demonstrated that a complex system, such as anaerobic digestion, could be easily modeled.

Fuzzified Expert System for Employability Assessment

Abstract Employability is somebody's prospective for gaining and maintaining employment. Basically employability depends on basic three parameters and these parameters are as education, understanding power and personal development. It is the capability to achieve the preliminary employment, to continue it and to obtain different one, if it is required. This paper introduced an innovative knowledgeable system for valuation of employability through some fuzzy rules. The purpose and scope of this concern research is to observe the optimal valuation for employability. This concern research considers three employability skills as an input namely Education, Understanding power and Personal development and find out a novel crisp value for employability which is basically characterize the ability of employee. This paper uses twenty seven fuzzy rules, by using Mamdani type fuzzy inference system in Mat-lab for catches solitary value of output named as employability.

Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic

This paper presents an edge-detection method that is based on the morphological gradient technique and generalized type-2 fuzzy logic. The theory of alpha planes is used to implement generalized type-2 fuzzy logic for edge detection. For the defuzzification process, the heights and approximation methods are used. Simulation results with a type-1 fuzzy inference system, an interval type-2 fuzzy inference system, and with a generalized type-2 fuzzy inference system for edge detection are presented. The proposed generalized type-2 fuzzy edge-detection method was tested with benchmark images and synthetic images. We used the merit of Pratt measure to illustrate the advantages of using generalized type-2 fuzzy logic.

A New Intelligent Motion Planning for Mobile Robot Navigation using Multiple Adaptive Neuro-Fuzzy Inference System

Nowadays intelligent tools such as fuzzy inference system (FIS), artific ial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable methods for modeling an engineering system. This paper presents a new hybrid technique based on the combination of fuzzy inference system and artificial neural network for addressing navigational problem of autonomous mobile robot. First we developed an adaptive fuzzy controller with four input parameters, two output parameters and three parameters each. Afterwards each adaptive fuzzy controller acts as a single takagi-sugeno type fuzzy inference system, where inputs are front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD) (from robot), heading angle (HA) (angle to target) and output corresponds to the wheel velocities ( Left wheel and right wheel) for the mobile robot. The effectiveness, feasibility and robustness of the prop osed navigational controller have been demonstrated by means of simulation experiments. The real time experimental results were verifie d with simulation experiments, showing that the proposed navigational algorithm consistently performs better results to navigate the mo bile robot safely in a completely or partially unknown environment.

Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators

This paper describes an optimization of interval type-2 and type-1 fuzzy integrators in ensembles of ANFIS models with genetic algorithms (GAs), this with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The Mackey-Glass time series was considered to validate the proposed ensemble approach. The methods used for the integration of the ensembles of ANFIS are: type-1 and interval type-2 Mamdani fuzzy inference systems (FIS). Genetic Algorithms are used for optimization of the membership function parameters of the FIS in each integrator. In the experiments we changed the type of the membership functions for each type-1 and interval type-2 FIS, thereby increasing the complexity of the training, The output (Forecast) generated by each integrator is calculated with the RMSE (root mean square error) to minimize the prediction error, therefore we compared the performance obtained by each FIS.

A comparative study of fuzzy logic systems approach for river discharge prediction

Summary In recent years, flood disasters resulting from extreme rainfall have been on the increase in many regions of the world. In developed countries, the usual practice of mitigating flood disasters is by structural means which can reduce infrastructural damages as well as casualties but are unaffordable in most developing countries. The alternative then is to look for non-structural means that involve, among other things, early warning systems which can reduce casualties. The basic technical components of an early warning system involves a measurable input data set that trigger floods, a measurable output data set that quantify the extent of flood and an appropriate mathematical model that transforms the input data set into a corresponding output data set. There are many types of mathematical models that can be used to transform the input data into corresponding output data. The crux of this paper is on one type of data driven mathematical models, namely the use of fuzzy logic approach. The reliability and robustness of the approach are demonstrated with daily and 6-hourly discharge predictions in 4 rivers in 3 countries having contrasting climatological, geographical and land use characteristics. The first application is for two tropical rivers in Sri Lanka using daily upstream rainfall and discharge data to predict downstream discharge with the minimum implication function type Mamdani fuzzy inference system. The second application is for another tropical river in Fiji using similar type of data with daily and 6-h time scales. Both Mamdani type fuzzy inference system with minimum and product implication functions as well as Larsen type inference systems were used. In the third application, daily upstream and tributary discharges were used to predict downstream discharges in a temperate-climate river in China using the TSK type fuzzy inference system with clustering. The methods are robust and the results obtained are within reasonable agreement with observations.

Grey Fuzzy Multiobjective Optimization of Process Parameters for CNC Turning of GFRP/Epoxy Composites

For obtaining close fits and tolerances, certain amount of machining has to be carried out on GFRP (Glass Fibre Reinforced Plastic) composites, produced by primary manufacturing processes. A number of cylindrical GFRP composite parts are finish machined by turning. These include axles, bearings, spindles, rolls and steering columns. There is always a tradeoff between quality and productivity during machining operations. Hence it becomes essential to evaluate the optimal cutting parameters setting in order to satisfy these opposing requirements. In this study, a hybrid multiobjective optimization algorithm involving grey and fuzzy coupled with Taguchi methodology is used. Four process parameters, each at three levels are selected for the study viz. cutting tool nose radius, cutting speed, feed rate and depth of cut. Surface roughness parameter Ra, tangential cutting force Fz and material removal rate MRR are the chosen output performance measures. The experimental plan is laid according to Taguchi's orthogonal array L27. Woven fabric based GFRP/ Epoxy tubes produced using hand layup process are finish turned using PCD cutting tool. Grey relational coefficients of the three performance measures are converted into a single multi performance characteristics index (MPCI) using Mamdani type fuzzy inference system. This MPCI is then optimized using Taguchi analysis. The parameter combination of A2B1C1D3, i.e. tool nose radius of 0.8mm, cutting speed of 120 m/min, feed rate of 0.05mm/rev and depth of cut of 1.6mm, is evaluated as the optimum combination. The confirmatory experiment at these settings gave maximum value of MPCI, validating the results.

A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm

A meta-cognitive interval type-2 neuro-fuzzy inference system (McIT2FIS) based classifier and its projection based learning algorithm is presented in this paper. McIT2FIS consists of two components, namely, a cognitive component and a meta-cognitive component. The cognitive component is an interval type-2 neuro-fuzzy inference system (IT2FIS) represented as a six layered adaptive network realizing Takagi-Sugeno-Kang type inference mechanism. A self-regulatory learning mechanism forms the meta-cognitive component. IT2FIS begins with zero rules, and rules are added and updated depending on the prediction error and relative knowledge contained the current sample. As each sample is presented to the network, the meta-cognitive component monitors the hinge-loss error and class-specific spherical potential of the current sample to decide what-to-learn, when-to-learn and how-to-learn them, efficiently. When a new rule is added or when an existing rule is updated, a projection based learning algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. It uses class specific criterion and sample overlap criterion to estimate the network parameters corresponding to the minimum energy point of the error function. Moreover, consistently under - performing rules are pruned from the network leading to a compact network. The performance of McIT2FIS is first evaluated on a set of benchmark classification problems from UCI machine learning repository. A tenfold cross validation based performance comparison with other state-of-the-art approaches indicates its improved performance. Next, its performance is evaluated on detection of attention deficiency hyperactivity disorder (ADHD) in children. The aim of this study is to classify a child as having typically developing controls or as an ADHD patient. Voxel based features extracted from amygdala region of the brain is employed in this study. The network is trained and tested on samples obtained from ADHD-200 consortium dataset consisting of 941 subjects. The performance comparison with standard support vector machine shows that McIT2FIS has superior classification ability than SVM in diagnosing ADHD.

Embedded Average of an Interval Type-2 Fuzzy Systems for Applications in FPGAs

This paper proposes the design of the embedded average of an interval type-2 fuzzy inference system (EAT2-FIS); four type-1 fuzzy inference systems are considered to model the footprint of uncertainty of inference process. The EAT2-FIS is designed in high level code, and this high level code is Very High Description Language (VHDL) for Field Programmable Gate Array (FPGA) applications. The goal is regulating the speed of a DC motor using Xilinx Spartan 3AN. The results obtained with the EAT2-FIS using different number bits are compared. The main contribution of the paper is the design and implementation of EAT2-FIS in VHDL, with the advantage of resolution change (bits number) of the EAT2-FIS for FPGA.

Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction

In this paper a new backpropagation learning method enhanced with type-2 fuzzy logic is presented. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. In this work, type-2 fuzzy inference systems are used to obtain the type-2 fuzzy weights by applying a different size of the footprint of uncertainty (FOU). The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a case of prediction for the Mackey-Glass time series (for τ=17). Noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.