Mamdani Fuzzy Rules Research Articles

Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm

In this paper, the accuracy of inertial sensor orientation relative to the level frame is improved through optimal tuning of a complementary filter by a genetic algorithm. While constant filter gains have been used elsewhere, these may introduce errors under dynamic motions when gyroscopes should be trusted more than accelerometers. Optimal gains are prescribed by a Mamdani fuzzy rule base whose membership functions are found using a genetic algorithm and experimental data. Furthermore, model fitness is not based directly on orientation but the error between estimated and ground truth velocities. This paper has three interrelated novel elements. The main novelty is the indirect tuning method, which is simple, low-cost and requires a single camera and inertial sensor. The method is shown to increase tracking accuracy compared with popular baseline filters. Secondary novel elements are the bespoke genetic algorithm and the time agnostic velocity error metric. The contributions from this work can help improve the localization accuracy of assets and human personnel. This research has a direct impact in command and control by improving situational awareness and the ability to direct assets to safe locations using safer routes. This results in increasing safety in applications such as firefighting and battlespace.

HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks

The uniaxial compressive strength (UCS) of the rock is one of the most important design parameters in various engineering applications. Therefore, the UCS requires to be either preciously measured through extensive field and laboratory studies or could be estimated by employing machine learning techniques and several other measured physical and mechanical explanatory rock parameters. This study is proposed to estimate the UCS of the evaporitic rocks by using a simple, measured point load index (PLI) and Schmidt Hammer (SHVRB) test rock blocks of evaporitic rocks. Finite mixture regression model (FMR), hybrid fuzzy inference systems model (HYFIS), multiple regression model (MLR), and locally weighted regression (LWR) are employed to predict the UCS. Different algorithms are implemented, including expectation–maximization (EM) algorithm, Mamdani fuzzy rule structures, Gradient descent-based learning algorithm with multilayer perceptron (MLP), and the least squares. Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and A20-index accuracy measures are used to compare the performances of the competing models. Based on all the above measures, LWR outperformed with the other models whereas the HYFIS model has a slight advantage over the other two models.

Trust aware clustering based secure routing techniques in wireless sensor network

Wireless Sensor Networks are becoming increasingly popular in everyday life since they offer a variety of network structures for developing cutting-edge real-time applications. Wireless sensor devices have high energy consumption limitations, so it is necessary to handle excessive energy consumption by malicious nodes properly to improve network performance. Even though numerous studies have been conducted to increase the dependability of routing in WSNs, the existing routing strategies do not meet the required security constraints by using intelligent methods to protect the sensor nodes from malicious attack. To overcome this challenge a novel Trust Aware Clustering based Secure Routing Techniques (TAC-SRT) has been proposed to minimize the overall energy consumption, improved security to nodes and to maximize the network lifetime. The proposed method is carried out in three phases. In the first phase, the cluster head is selected by using K mean clustering. In the second phase, the trust value of each node is evaluated by using Mamdani fuzzy inference rule. In the third phase, the Tversky similarity index is used to find the normal or malicious node and establishes the shortest route. The Fully Homomorphic Elliptic Curve Cryptography technique is then used to perform secure data transmission. The effectiveness of the proposed strategy is examined using several parameters, such as the lifetime of the network, data confidentiality, active nodes, and energy consumption. The proposed technique improves the network lifetime by 23.01%, 17.4%, and 13.2% better than MOSFA, SecDL, and CAR-MOSOA respectively. Finally, the proposed method demonstrated superior performance in terms of delay, throughput, encryption time, network lifetime, and packet delivery ratio compared with existing techniques.

Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm

The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use.

Prediction Of Closing Price Combined Stock Index (Ihsg) Using The Fuzzy Mamdani Method

This study proposes a method for predicting the closing IHSG stock price using the Mamdani fuzzy approach. This model uses historical closing stock price data as input, and generates closing stock price predictions using the Mamdani fuzzy rule. However, experimental results show that this model may not be suitable for predicting stock prices accurately and reliably. Therefore, this study does not recommend the use of the Mamdani fuzzy method for the purpose of predicting closing stock prices.

Student’s performance evaluation according to the cognitive domain: fuzzy logic approach

Teaching learning process is one of the important aspects in academic performance evaluation of students. This gives an idea of how much a student has gained the knowledge and skill in a particular subject during the entire season. In this paper, a multi criteria-based decision support system based on six levels of revised Bloom’s cognitive domain has been presented to measure the cognitive knowledge and skill acquired by a student. The fuzzy set theoretic approach has been adopted to overcome the vagueness/fuzziness present in the evaluation process and the inexact causal relationship between input and output parameters has been addressed using the Mamdani fuzzy IF-THEN rules. Finally, a comparative study has been depicted between the marks obtain by a group of students in the exam with their cognitive knowledge score.

Improving the Robustness of Object Detection Through a Multi-Camera-Based Fusion Algorithm Using Fuzzy Logic.

A single camera creates a bounding box (BB) for the detected object with certain accuracy through a convolutional neural network (CNN). However, a single RGB camera may not be able to capture the actual object within the BB even if the CNN detector accuracy is high for the object. In this research, we present a solution to this limitation through the usage of multiple cameras, projective transformation, and a fuzzy logic–based fusion. The proposed algorithm generates a “confidence score” for each frame to check the trustworthiness of the BB generated by the CNN detector. As a first step toward this solution, we created a two-camera setup to detect objects. Agricultural weed is used as objects to be detected. A CNN detector generates BB for each camera when weed is present. Then a projective transformation is used to project one camera’s image plane to another camera’s image plane. The intersect over union (IOU) overlap of the BB is computed when objects are detected correctly. Four different scenarios are generated based on how far the object is from the multi-camera setup, and IOU overlap is calculated for each scenario (ground truth). When objects are detected correctly and bounding boxes are at correct distance, the IOU overlap value should be close to the ground truth IOU overlap value. On the other hand, the IOU overlap value should differ if BBs are at incorrect positions. Mamdani fuzzy rules are generated using this reasoning, and three different confidence scores (“high,” “ok,” and “low”) are given to each frame based on accuracy and position of BBs. The proposed algorithm was then tested under different conditions to check its validity. The confidence score of the proposed fuzzy system for three different scenarios supports the hypothesis that the multi-camera–based fusion algorithm improved the overall robustness of the detection system.

Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.

Energy Efficient and High PDR Wireless Sensor Networks using DCHS and FIS

Wireless Sensor Network (WSN) is forthcoming technology that afford assorted applications in hearth care, military, industrial and object tracking. WSN is a wireless device consisting of dispersed autonomous apparatus using sensors to collectively adviser physical or environmental conditions. This paper is presenting WSN using dynamic cluster head selection (DCHS) and fuzzy inference system (FIS) technique. The DCHS methods for WSN are put leading in procedure to solve the problem of the arbitrary cluster head selection that may lead to the extending description and irregular energy consumption in the cluster communication. FIS is a scheme that uses fuzzy inclination theory to atlas inputs to outputs. FIS is depend on mamdani rules and Fuzzy rules are a compilation of linguistic statements that characterize how the FIS should make a judgement regarding classifying an input or regulating an output. The proposed technique is implemented MATLAB 2012R and calculated average energy consumption and success rate packet delivery ratio.

The Use of Fuzzy Linear Regression and ANFIS Methods to Predict the Compressive Strength of Cement

In this paper, the prediction of compressive cement strength using the fuzzy linear regression (FLR) and adaptive neuro-fuzzy inference system (ANFIS) methods was studied. Specifically, an accurate prediction method is needed as the modeling of cement strength is a difficult task, which is based on its composite nature. However, many approaches are widely implemented in strength-predicting problems, such as the artificial neural network (ANN), Mamdani fuzzy rules in MATLAB, FLR and ANFIS models. Applying these methods and comparing the results with the corresponding observed ones, we concluded that the ANFIS method successfully decreased the level of uncertainty in predicting cement strength, as the average percentage error level was extremely low. Although the FLR method had the highest average percentage error level compared with the other methods, it provides a standard equation to estimate the output values by using symmetric triangular fuzzy numbers and determines the most important factor in increasing compressive strength, in contrast to ANFIS and ANN, which are black box models, and to the fuzzy method, which uses rules without providing the specific way by which the results come out. Thus, ANFIS and FLR are appropriate methods for dealing with engineering mathematical models by using fuzzy logic.

Optimization of awj process using fuzzy taguchi method for improving surface characteristics of silicon wafer

This study investigates the slicing capabilities and surface characteristics of Silicon wafer by optimizing the operation parameters of the Abrasive water jet machining process. Slicing carried out on single crystal pure silicon ingot in AWJM by using an abrasive particle of size 80 mesh. To optimize the AWJ operation parameters L9 Taguchi orthogonal array is used and compare the superficial feature like surface roughness, topography, and slicing rate using different input parameters like SOD, water jet pressure, and flow rate of abrasives. To know the significant slicing parameter, which impact in the surface finish of silicon wafer by ANOVA analysis and the empirical model of the slicing process is find by using a regression model, Further Fuzzy logic analysis used to predict the abrasive water jet machining process parameters with Mamdani fuzzy rules and Triangular membership function. SEM analysis is portrayed by the surface morphology of different mesh size machined on pure Si wafer.

Fuzzy based image edge detection algorithm for blood vessel detection in retinal images

We developed a contour detection based image processing algorithm based on Mamdani (Type-2) fuzzy rules for detection of blood vessels in retinal fundus images. The method uses the green channel data from eye fundus images as input, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, and median filter for background exclusion. The Mamdani (Type-2) fuzzy rules applied on image gradient value are used for edge detection. The results of experiments on the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE) and CHASEdb datasets show the applicability of the proposed method as a flexible approach which can be adapted to numerous edge detection/contour based applications. We achieved an accuracy of 0.865 for STARE dataset, an accuracy of 0.939 for the DRIVE dataset, and the accuracy of 0.950 for the ChaseDB dataset. In relation to works of other authors, our method offered a similar performance, but it offers an improved dynamics and flexibility in formulation of the linguistic threshold criteria, which can be a leading factor in design of image processing systems with dynamic and flexible rules, such as Type-2 fuzzy rules would allow, offering an interesting alternative to currently widespread deep learning applications.

A Fuzzy Adaptive Dynamic NSGA-II With Fuzzy-Based Borda Ranking Method and its Application to Multimedia Data Analysis

In this article, a novel fuzzy-based dynamic multiobjective evolutionary algorithm is presented. In this article, giving a valid and true response to the change is an essential task to improve the diversity of solutions when an environmental change occurs. The basic idea is to randomly remove some solutions and replace by newly created solutions. However, the random selection detours the algorithm's trajectory and deteriorates the performance of the optimization algorithm. Recently, the Borda method has been deployed to find the best candidates to be removed from the solutions list. Although the Borda method outperforms the random strategy, it suffers from some drawbacks. In this article, we propose an improved Borda count method incorporated with fuzzy tuned parameters so that its parameters are adjusted by Mamdani fuzzy rules. Our new Borda method can distinguish the information before and after change with different fuzzy weights. In addition to the fuzzy-based Borda, we employ an improved evolutionary algorithm based on fuzzy logic. We propose a novel nondominated sorting genetic algorithm with its parameters tuned with fuzzy rules so that it is adapted to the new environment. Experiments are conducted on standard benchmarks and the results are compared with recent algorithms. Then, multimedia data analysis, such as segmentation of moving objects, is experimented as a dynamic multiobjective problem and solved by the proposed algorithm.

CONVENTIONAL FUZZY LOGIC CONTROLLER FOR BALANCING TWO-WHEEL INVERTED PENDULUM

This paper presents the design and the Real-Time implementation of self-balancing Two-Wheeled Inverted Pendulum (TWIP) using state-feedback controller and a conventional fuzzy logic controller (CFLC) on Real-Time. The state-feedback controller consists of two parts PD controller and PI controller. The state-feedback controller was designed first for the nonlinear model then it was updated for the Real-Time implementation. The CFLC was designed first based on the state-feedback controller to reach the point of using pure fuzzy controller for the TWIP. The CFLC was designed with Mamdani fuzzy inference architecture if-then rules. The TWIP is balanced with CFLC with a base of 7 rules only. Different controller’s types were tested on real-Time to perform a good balance for the TWIP. Fuzzy PI, Fuzzy PD, and combination of two independent CFLC were used to achieve the balancing of the TWIP on Real-Time.

New mixed-coding PSO algorithm for a self-adaptive and automatic learning of Mamdani fuzzy rules

Thanks to its algorithmic performances, PSO algorithm becomes a popular tune tool for fuzzy systems in literature. However, it still encounters many complications, especially when dealing with Mamdani fuzzy system type because of its nature. The Mamdani fuzzy system is known as a linguistic model where the semantic meaning of the fuzzy rules is an intrinsic characteristic that must be retained during the learning process, while seeking for high accuracy. Therefore, to tune the Mamdani fuzzy system, it is very crucial to well represent each rule in a way that preserves this characteristic firstly, and to look for a search mechanism to optimize them throughout this topic secondly. In this paper, we introduce a new and promising approach to optimize the Mamdani fuzzy systems without a need of any prior knowledge. To the best of our knowledge, this approach is the first to optimize simultaneously the membership functions, the scaling factor parameters and the fuzzy rule conclusions with a mixed-coding PSO algorithm by combining a special monitoring function and a self-adaptive threshold. The proposed approach is validated by a comparative study with other design strategies taken from Box–Jenkinsgas furnace system literature and two theoretical examples in addition to a real-time control of the inverted pendulum Feedback 33-200. The obtained results proved the potential and the effectiveness of the proposed approach.

Evolving Mamdani Fuzzy Rules Using Swarm Algorithms for Accurate Data Classification

This paper proposes and develops a fuzzy evolutionary system based on the Grey Wolf Optimizer (GWO) algorithm to evolve Mamdani fuzzy rules that give a knowledge base for accurate classification of data set. GWO takes inspiration from nature and is modeled after the hunting behavior of the grey wolves as they move around within a pack taking cues from the leader alpha, beta, and delta wolves until they find the best position to encircle and attack the prey. The algorithm is mapped onto the data specific rule base structure of the fuzzy systems. A grammar template in the form of fuzzy rules is designed, and then the GWO algorithm is used to evolve the fuzzy rules which classify the datasets. The algorithm will generate meaningful rules that make sense of data in easy to comprehend fuzzy rules. The algorithm was extensively tested on 15 datasets. GWO was compared with the standard Particle Swarm Optimizer (PSO) algorithm in generating a similar type of rules and comparing the accuracy of these two sets of rules in data classification. It was noted that the GWO algorithm converges in a lesser number of iterations and in a shorter time as compared to PSO and achieves the best accuracy.

A New Geospatial Model Integrating a Fuzzy Rule-Based System in a GIS Platform to Partition a Complex Urban System in Homogeneous Urban Contexts

Here, we present a new unsupervised method aimed at obtaining a partition of a complex urban system in homogenous urban areas, called urban contexts. Our model integrates spatial analysis processes and a fuzzy rule-based system applied to manage the knowledge of domain experts; it is implemented using a GIS platform. The area of study is initially partitioned in microzones, homogeneous portions of the urban system, which are the atomic reference elements for the census data. With the contribution of domain experts, we identify the physical, morphological, environmental, and socio-economic indicators needed to identify synthetic characteristics of urban contexts and create the fuzzy rule set necessary for determining the type of urban context. We implement the set of spatial analysis processes required to calculate the indicators for the microzones and apply a Mamdani fuzzy rule system to classify the microzones. Finally, the partition of the area of study in urban contexts is obtained by dissolving continuous microzones belonging to the same type of urban context. Tests are performed on the Municipality of Pozzuoli (Naples, Italy); the reliability of the out model is measured by comparing the results with the ones obtained through a detailed analysis.

Evolutionary fuzzification of RIPPER for regression: Case study of stock prediction

This paper presents a novel approach for extracting the knowledge base (KB) of a Mamdani fuzzy rule based system (FRBS) for stock market prediction. The KB, which is the most important component of the FRBS, has two main components: rule base (RB) and data base (DB). In the proposed model, the RB is learned using repeated incremental pruning to produce error reduction (RIPPER), which is state-of-the-art in classification rule induction. The RIPPER algorithm is applied to classification problems. In order to extend it for regression problems, unsupervised discretization of the target attribute and supervised discretization of continuous input attributes are used. Next, the classification rules obtained from RIPPER are fuzzified and the initial Mamdani FRBS is formed. The DB is tuned using a Genetic Algorithm (GA). The proposed model in this paper is the first model utilizing directly capabilities and benefits of rule-based classification systems in regression. The accuracy of the proposed model is tested in the context of stock market prediction, which is a complex and difficult area in regression problems. The proposed model is implemented using several indices from different stock markets such as the Taiwan Stock Exchange Index (TSE), Tehran Price Index (TEPIX). Other indices including the Industry Index, Top 50 Companies Index and Financial Group Index from Tehran Stock Exchange are also considered. Furthermore, the daily stock prices of multiple large companies such as Apple, DELL, IBM, British Airlines and Ryanair Airlines are incorporated. As shown by the mean absolute percentage error (MAPE) and non-parametric statistical tests, the proposed model offers superior performance compared to other models.

Fuzzy Sliding Mode Based Series Hybrid Active Power Filter for Power Quality Enhancement

This paper contributes an innovative gating signal generation technique based on fuzzy sliding mode pulse width modulation (FSMPWM) with a Series Hybrid Active Power Filter (SEHAPF) for reducing the total harmonic distortion (THD). Hybrid filters are used for compensating reactive power on the load side and mitigate harmonics for the growth of power quality under variable source and load conditions in the utility system. The objective of the paper is to eradicate the various power quality (PQ) problems with development in power factor and reducing distortion in transmission and distribution line due to harmonics. With the implementation of FSMPWM of the proposed filter, the gating pulses are generated by implementing a Mamdani fuzzy rule with sliding surfaces. For producing a fixed pulse the presented method reduces the chattering reaction by controlling the narrow boundary coating on the sliding surface. The results of the projected technique are analyzed and compared with the traditional hysteresis band current controller (HCC). The overall proficiency and results are examined with the help of MATLAB/SIMULINK environment.

Fuzzy adaptive cat swarm algorithm and Borda method for solving dynamic multi‐objective problems

AbstractThe main goal of this paper is to introduce a novel dynamic multi‐objective optimization algorithm. First, after detecting the environmental changes, Borda count ranking method is applied to population in order to assign the Borda score to each individual, and then the lowest score individuals are removed from population and replaced with new created solutions. Furthermore, fuzzy adaptive multi‐objective cat swarm optimization algorithm is used to estimate the Pareto‐optimal front in which its parameters are tuned to new environment by Mamdani fuzzy rules when a change occurs. Performance of the proposed algorithm is tested on dynamic multi‐objective benchmarks and is compared with recent achievements. The simulations show the quite satisfactory results and higher performance of the proposed method in comparison with traditional approaches.