Fuzzy Logic Genetic Algorithms Research Articles

Fault Diagnosis of IGBT Single-Phase Bridge Arm Based on Fuzzy Logic Genetic Algorithm

Fault Diagnosis of IGBT Single-Phase Bridge Arm Based on Fuzzy Logic Genetic Algorithm

Intelligent Adaptive Fuzzy Logic Genetic Algorithm Controller for Anti-Lock Braking System

Braking system plays a major safety feature in any vehicle; it is considered as one of the key points in safe motoring. The main objective of a good braking system is to provide safe stopping, in addition to the fact that it provides repeatable stopping. Anti-lock Braking System (ABS) is one of the recently developed safety feature used in a vehicle in order to prevent wheels from locking. This article proposes an intelligent adaptive fuzzy logic (FL) genetic algorithm (GA) controller for the Anti-Lock Braking System (ABS) to control the braking of a vehicle by controlling the slip ratio based on quarter car model. The main objective of the proposed controller is to prevent wheels from locking, which is a major safety issue in any braking system for different road conditions. The proposed controller consists of a Fuzzy Logic (FL) Genetic Algorithm (GA) based controller. The Genetic Algorithm is utilized in tuning the membership functions of the Fuzzy logic controller, which is developed to calculate the amount of reduction of the applied torque due to applying brake based on the error of the measured slip ratio signal compared with the desired one. Then a Genetic Algorithm (GA) is utilized in tuning the Fuzzy Logic parameters, which are the center of the Gaussian membership function, and the spread (б). The simulation results of the proposed fuzzy logic genetic algorithm controller are compared with both the results of the Fuzzy logic controller and the PD controller developed in previous work of the authors. The simulation results present many advantages of using Fuzzy Logic Genetic Algorithm based controller over both the Fuzzy Logic and the PD controllers, and the simulation is carried out at different road conditions, namely dry, wet and icy road surface.

A fuzzy logic-genetic algorithm approach to modelling public transport users’ risk-taking behaviour

ABSTRACTThis paper seeks to determine the effects of uncertainty in out-of-vehicle times on route choice. Data were collected at two key interchanges in Auckland, New Zealand. Previous work modelled the data using a manual approach to fuzzy logic. This study extends that work by automating the process through defining a black-box function to match the survey data, then employing a genetic algorithm to fine-tune the fuzzy logic model. Results show that automation and the genetic algorithm improve the model’s capability to more accurately predict ridership. The tuning of the membership functions is conducted twice, first using initial fuzzy rules and again after the fuzzy rules have been adjusted to reduce disparity between the output and survey data. The calibrated membership functions provided for operational (transfer waiting and walking time and delay) and physical attributes (safety and seat availability) can be used by practitioners to determine an estimated ridership.

Power Control in Mac Protocol for Wireless Networks using Hybrid Optimization Techniques

Objectives: To propose power control techniques used in MAC protocol for wireless networks. Methods/Statistical Analysis: Energy reductions techniques are essential for wireless networks, as battery powered wireless hosts have limited energy. Hence energy aware techniques with power saving mechanisms are restored to conserve energy of nodes in wireless networks. Power based connectivity is an ad-hoc implements power control mechanisms to enhance network life by improving throughput, cost effective routes and spatial reuse. This study proposes hybrid optimization for “Medium Access Control (MAC)” protocol to implement coordination functions and power control mechanisms. Hybrid optimization is based on “Genetic Algorithm (GA)” and “Gravitational Search Algorithm (GSA)”. Findings: Experiments with hybrid optimization are compared to GA fuzzy rules and Fuzzy methods. The result reveal that two hop power control with GA fuzzy logic method lowers route discovery time, increases cache replies, minimizes simulation time and end to end delay in contrast to DSR routing and two hop routing protocols. Application/Improvements: The new hybrid GA-GSA has a throughput of 8.89% and 1.62% and low end to end delay of 13.66% and 2% compared to Fuzzy and GA based Fuzzy respectively.

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.

Heart Disease Prediction Using Hybrid Genetic Fuzzy Model

The objective of the work is to diagnose heart disease using computing techniques like genetic algorithm and fuzzy logic. The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper a hybrid genetic-fuzzy heart disease diagnosis system is designed. The genetic algorithm is used for a stochastic search that provides the optimal solution to the feature selection problem. The relevant features selected from the dataset help the diagnosing system to develop a classification model using fuzzy inference system. The rules for the fuzzy system are generated from the sample data. Among the entire rule set the important and relevant subset of rules are selected using genetic algorithm. The proposed work uses the benefits of genetic algorithms and fuzzy inference system for effective prediction of heart disease in patients. The selected features are sex, serum cholesterol (chol), maximum heart rate achieved (thalach), Exercise induced angina (exang), ST depression induced by exercise relative to rest (oldpeak), number of major vessels coloured (ca) and thal value. Fuzzification using Fuzzy Gaussian membership function and defuzzification using centroid method improves the performance of the system. The work has been evaluated using the performance metrics like accuracy, specificity, sensitivity, confusion matrix that help in proving the efficiency of the work. The obtained classification accuracy is 86% using the stratified k fold technique with the values for specificity and sensitivity as .90 and .80 respectively. The number of attributes has been reduced from 13 to 7 from heart disease dataset available in the UCI Machine learning repository. When compared with the existing system the accuracy of the proposed work has been increased by 1.54%. The proposed model is named as GAFL model called Genetic Algorithm Fuzzy Logic model for effective heart disease prediction. It is easy to build the model thereby providing an easy option to be used in hospitals and medical centers for the aid of the physicians.

Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: Application of artificial intelligence

Abstract In this paper, a new approach based on artificial intelligence concept is evolved to monitor the permeability and porosity of petroleum reservoirs by means of petro-physical logs at various conditions. To address the referred issue, different artificial intelligence techniques including fuzzy logic (FL) and least square support vector machine (LSSVM) were carried out. Potential application of LSSVM and FL optimized by genetic algorithm (GA) is proposed to estimate the permeability and porosity of petroleum reservoirs. The developed intelligent approaches are examined by implementing extensive real field data from northern Persian Gulf oil fields. The results obtained from the developed intelligent approaches are compared with the corresponding real petro-physical data and gained outcomes of the other conventional models. The correlation coefficient between the model estimations and the relevant actual data is found to be greater than 0.96 for the GA–FL approach and 0.97 for GA–LSSVM. The results from this research indicate that implication of GA–LSSVM and GA–FL in prediction can lead to more reliable porosity/permeability predictions, which can lead to the design of more efficient reservoir simulation schemes.

Intelligent maximum power point trackers for photovoltaic applications using FPGA chip: A comparative study

In this paper, various intelligent methods (IMs) used in tracking the maximum power point and their possible implementation into a reconfigurable field programmable gate array (FPGA) platform are presented and compared. The investigated IMs are neural networks (NN), fuzzy logic (FL), genetic algorithm (GA) and hybrid systems (e.g. neuro-fuzzy or ANFIS and fuzzy logic optimized by genetic algorithm). Initially, a complete simulation of the photovoltaic system with intelligent MPP tracking controllers using MATLAB/Simulink environment is given. Secondly, the different steps to design and implement the controllers into the FPGA are presented, and the best controller is tested in real-time co-simulation using FPGA Virtex 5. Finally, a comparative study has been carried out to show the effectiveness of the developed IMs in terms of accuracy, quick response (rapidity), flexibility, power consumption and simplicity of implementation. Results confirm the good tracking efficiency and rapid response of the different IMs under variable air temperature and solar irradiance conditions; however, the FL–GA controller outperforms the other ones. Furthermore, the possibility of implementation of the designed controllers into FPGA is demonstrated.

The Application Research on Fuzzy Theory and Genetic Algorithm

Fuzzy theory is one of the newly adduced self-adaptive strategies,which is applied to dynamically adjust the parameters of genetic algorithms for the purpose of enhancing the performance.In this paper, the financial time series analysis and forecasting as the main case study to the theory of soft computing technology framework that focuses on the fuzzy logic genetic algorithms(FGA) as a method of integration. the financial time series forecasting model based on fuzzy theory and genetic algorithms was built. the ShangZheng index cards as an example. The experimental results show that FGA perform s much better than BP neural network，not only in the precision.but also in the searching speed.The hybrid algorithm has a strong feasibility and superiority.

A novel hybrid fuzzy logic-genetic algorithm-data envelopment approach for simulation optimisation of pressure vessel design problems

In this paper, we propose a hybrid fuzzy logic-genetic algorithm for optimising a non-linear problem related to pressure vessel design. The fuzzy non-linear program that we obtain is solved using a Genetic Algorithm (GA), and a simulation was used for generating the initial population. The efficiency of some of the design optimisation algorithms are compared with the proposed approach of this study, based on the Data Envelopment Analysis (DEA). The results of the DEA reveal that the proposed method is superior. This is the first study that integrates GA and Fuzzy logic for optimising the pressure vessel design problems.

Soft computing in medicine

Soft computing (SC) is not a new term; we have gotten used to reading and hearing about it daily. Nowadays, the term is used often in computer science and information technology. It is possible to define SC in different ways. Nonetheless, SC is a consortium of methodologies which works synergistically and provides, in one form or another, flexible information processing capability for handling real life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness and low-cost solutions. SC includes fuzzy logic (FL), neural networks (NNs), and genetic algorithm (GA) methodologies. SC combines these methodologies as FL and NN (FL–NN), NN and GA (NN–GA) and FL and GA (FL–GA). Recent years have witnessed the phenomenal growth of bio-informatics and medical informatics by using computational techniques for interpretation and analysis of biological and medical data. Among the large number of computational techniques used, SC, which incorporates neural networks, evolutionary computation, and fuzzy systems, provides unmatched utility because of its demonstrated strength in handling imprecise information and providing novel solutions to hard problems. The aim of this paper is to introduce briefly the various SC methodologies and to present various applications in medicine between the years 2000 and 2008. The scope is to demonstrate the possibilities of applying SC to medicine-related problems. The recent published knowledge about use of SC in medicine is researched in MEDLINE. This study detects which methodology or methodologies of SC are used frequently together to solve the special problems of medicine. According to MEDLINE database searches, the rates of preference of SC methodologies in medicine were found as 68% of FL–NN, 27% of NN–GA and 5% of FL–GA. So far, FL–NN methodology was significantly used in medicine. The rates of using FL–NN in clinical science, diagnostic science and basic science were found as %83, %71 and %48, respectively. On the other hand NN–GA and FL–GA methodologies were mostly preferred by basic science of medicine. Another message emerging from this survey is that the number of papers which used NN–GA methodology has continuously risen until today. Also search results put the case clearly that FL–GA methodology has not applied well enough to medicine yet. Undeniable interest in studying SC methodologies in genetics, physiology, radiology, cardiology, and neurology disciplines proves that studying SC is very fruitful in these disciplines and it is expected that future researches in medicine will use SC more than it is used today to solve more complex problems.

Genetic Algorithm Fuzzy Logic Energy Management Strategy for Fuel Cell Hybrid Vehicle

Abstract The use of fuel cell hybrid vehicles is a potential transport solution achieving zero emission once technical, economic, and social challenges are overcome. Energy management strategies on board remain an important research subject in a quest to find an efficient power splitting between the fuel cell system and the energy storage element of the hybrid powertrain. This paper proposes an online strategy based on a fuzzy decision system. Fine tuning of the fuzzy system parameters, mainly the membership functions, is made possible using a powerful optimisation tool which is a genetic algorithm. This optimisation procedure takes into consideration the minimisation of the hydrogen consumption while satisfying the requested power over a given driving cycle.

Non-identical parallel-machine scheduling using genetic algorithm and fuzzy logic approach

Manufacturing industries frequently face the problem of reducing earliness and tardiness penalties, owing to the emerging concept of the just-in-time production philosophy. The problem studied in this work is the Non-identical Parallel-machine?Earliness-Tardiness Non-common Due Date?Sequence-dependent Set-up Time Scheduling Problem (NPETNDDSP) for jobs with varying processing times, where the objective is to minimise the sum of the absolute deviations of job completion times from their corresponding due dates for the different weighted earliness and tardiness combinations. A Genetic Algorithm-Fuzzy Logic Approach (GA-Fuzzy) has been proposed to select the optimal weighted earliness-tardiness combinations in a non-identical parallel-machine environment. The performance of the combined objective function obtained by the proposed GA-Fuzzy technique has been compared with the solutions yielded by the Genetic Algorithm (GA) techniques available in literature, known as GA with Partially Mapped Crossover Operator (GA-PMX) and GA with Multi-Component Uniform Order-based Crossover Generator (GA-MCUOX). The comparison shows that the proposed GA-Fuzzy technique outperforms both the GA-PMX and GA-MCUOX.

A combined genetic algorithm-fuzzy logic controller (GA–FLC) in nonlinear programming

Abstract This paper presents a combined genetic algorithm-fuzzy logic controller (GA–FLC) technique for constrained nonlinear programming problems. In the standard Genetic algorithms, the upper and lower limits of the search regions should be given by the decision maker in advance to the optimization process. In general a needlessly large search region is used in fear of missing the global optimum outside the search region. Therefore, if the search region is able to adapt toward a promising area during the optimization process, the performance of GA will be enhanced greatly. Thus in this work we tried to investigate the influence of the bounding intervals on the final result. The proposed algorithm is made of classical GA coupled with FLC. This controller monitors the variation of the decision variables during process of the algorithm and modifies the boundary intervals to restart the next round of the algorithm. These characteristics make this approach well suited for finding optimal solutions to the highly NLP problems. Compared to previous works on NLP, our method proved to be more efficient in computation time and accuracy of the final solution.

Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of the optimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies.

A combined genetic algorithm–fuzzy logic method (GA–FL) in mechanisms synthesis

This work presents a combined genetic algorithm–fuzzy logic method to solve the problem of path generation in mechanism synthesis. The proposed method is made of a classical genetic algorithm coupled with a fuzzy logic controller (GA–FL). This controller monitors the variation of the design variables during the first run of the genetic algorithm and modifies the initial bounding intervals to restart a second round of the genetic algorithm. Compared to previous works on the same problem, our method proved to be more efficient in finding the optimal mechanism.

Vibration Control of Unknown Flexible Beam Structures

Two approaches, namely, state space-principal component analysis (SS-PCA) and fuzzy logic-genetic algorithms (FL-GAs) controllers are introduced for constraining the transient motion of flexible structures. These two techniques require no prior knowledge about the physical modeling of such structures. The SS-PCA has the capability, among all the other available methods, of providing an estimation of the characteristics of the system under consideration from measured displacement outputs. Excellent control was achieved with both proposed techniques, though quite better results were obtained when applying FL-GAs. However, in the online processing, the FL-GA was found to need more computational time when compared with the SS-PCA.