Genetic Fuzzy System Research Articles

Design of Voltage Reactive Optimizing Closed-Loop Control System in Local Power Grid

By means of computer and network communication techniques, this system analyzes and calculates the real-time data, which is collected by telemetry and telecommand at every node in the whole grid via SCADA system. On the premise of ensuring the safe operation of the power grid and equipments, under the constraint condition of qualified voltage at every node and power factor of province gate way, and in accordance with power flow calculation, improved genetic algorithm and fuzzy expert system, this system is able to carry out online voltage reactive optimizing closed-loop control from the angle of the whole power grid. Thus it makes the investment of reactive-load compensation equipments and the adjustment of main transformers tap-changer more reasonable. Therefore, the system achieves the goal of local balance in reactive power stratification.

A NOVEL APPROACH BASED ON GENETIC FUZZY CLUSTERING AND ADAPTIVE NEURAL NETWORKS FOR SALES FORECASTING

This article proposes a new hybrid sales forecastin g system based on genetic fuzzy clustering and BackPropagation (BP) Neural Networks with adaptive learning rate (GFCBPN).The proposed architecture consists of three stages: (1) utilizing Winter’s Exponential Sm oothing method and Fuzzy C-Means clustering, all normalized data records will be categorized into k clusters; ( 2) using an adapted Genetic Fuzzy System (MCGFS), the fuzzy rules of membership levels to each cluster will be extracted; (3) each cluster will be fed into parall el BP networks with a learning rate adapted as the level of cluste r membership of training data records. Compared to previous researches which use Hard clustering, this research uses the fuzzy clustering which capable to increas e the number of elements of each cluster and consequently improve the accuracy of the proposed forecasting s ystem. Printed Circuit Board (PCB) will be utilized as a c ase study to evaluate the precision of our proposed system. Experimental results show that the proposed model outperforms the previous and traditional approaches. Therefore, it is a very promising method for financ ial forecasting.

Hybrid fuzzy-genetic system for optimising cabled-truss structures

This paper demonstrates an application of a hybrid fuzzy-genetic system in the optimisation of lightweight cabled-truss structures. These structures are described as a system of cables and triangular bar formations jointed at their ends by hinged connections to form a rigid framework. The optimised lightweight structure is determined through a stochastic discrete topology and sizing optimisation procedure that uses ground structure approach, nonlinear finite element analysis, genetic algorithm, and fuzzy logic. The latter is used to include expertise into the evolutionary search with the aim of filtering individuals with low survival possibility, thereby decreasing the total number of evaluations. This is desired because cables, which are inherently nonlinear elements, demand the use of iterative procedures for computing the structural response. Such procedures are computationally costly since the stiffness matrix is evaluated in each iteration until the structure is in equilibrium. Initially, the proposed system is applied to truss benchmarks. Next, the use of cables is investigated and the system’s performance is compared against genetic algorithms. The results indicate that the hybrid system considerably decreased the number of evaluations over genetic algorithms. Also, cabled-trusses showed a significant improvement in structural mass minimisation when compared with trusses.

Developing an intelligent fiber migration simulator in spun yarns using a genetic fuzzy system

The mechanical and physical properties of spun yarns and fabrics depend not only on mechanical properties of the fibers making up the yarn, but also geometrical arrangement of fibers, known as fiber migration. The main aim of this research is to introduce a new approach to predict migratory behavior of spun yarns. Achieving the objectives of this research, general physical, mechanical and structural properties of spun yarns together with existing standards were thoroughly studied. A hybrid intelligent model was developed based on a Genetic Fuzzy System (GFS) to model the relationships between migration of fibers in spun yarns and some physical and mechanical properties of spun yarns. Results indicated that the developed fuzzy expert system can be used as an intelligent simulator to predict yarn migratory parameters.

Modelling trip distribution with fuzzy and genetic fuzzy systems

This paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.

The Scrutiny of Variation in the Number of Fuzzy Rules and Membership Functions in a New Genetic-Fuzzy System in Approximation and Prediction Problems

This paper shows a new fuzzy system was improved using genetic algorithm to handle fuzzy inference system as a function approximator and time series predictor. The system was developed generality that trained with genetic algorithms (GAs) corresponding to special problem and would be evaluated with different number of rules and membership functions. Then, compare the efficacy of variation of these two parameters in behavior of the system and show the method that achieves an efficient structure in both of them. Also, the proposed GA-Fuzzy inference system successfully predicts a benchmark problem and approximates an introduced function and results have been shown.

Improved GA-NFS-Based Decision Rule Acquisition in Route Selection for Public Transport Line

Aimed at the difficulty of the route selection of urban public transport line, a new method was proposed for the improved genetic algorithm BP neural fuzzy system model and algorithm. For the problem of route selection, it is required to acquire evolution rule of system status from the changes of multiple environment variable factors. The BP neural fuzzy system model based on Mamdani inference was given, in order to overcome the existing limitations of BP neural network, it puts forward the thought of algorithm improved by GA. The algorithm as the foundation is applied in route selection of urban road public transport line, and the method’s usability is proven through simulation, calculation and analytical investigation of practical problems on the main lines of bus rapid transit the route selection.

A Biologicaly Inspired Decision Model for Multivariable Genetic- Fuzzy-AHP System

This paper describes a hybridized intelligent algorithm as a tuning mechanism for one type of Genetic Fuzzy system termed the Genetic Fuzzimetric Technique (GFT). The proposed technique is based on the genetically inspired operations of crossover and mutation to achieve an optimized solution used to tune the fuzzy set shape (variables) within the rule-set. The GFT deals with knowledge representation in a modular form where each module -- termed a chromosome, in this article -- represents the defuzzified value of a rule-set inferring a specific output from a fuzzy input. A multivariable system, in this case, is the combination of all these chromosomes via a weighting factor termed the “Input Importance Factor”. This paper also explains the Analytic Hierarchy Process (AHP) technique which is proposed as a pairwise comparison methodology for the creation, selection and adaptation of the Input Importance Factor. The proposed GFT mechanism can be applied to any decision making problem within an uncertain environment. One example would be the determination of CRM performance measurement given a variety of inputs related to marketing, data mining tools, ordered materials and communication.

A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets

Lots of real world applications appear to be a matter of classification with imbalanced data-sets. This problem arises when the number of instances from one class is quite different to the number of instances from the other class. Traditionally, classification algorithms are unable to correctly deal with this issue as they are biased towards the majority class. Therefore, algorithms tend to misclassify the minority class which usually is the most interesting one for the application that is being sorted out.Among the available learning approaches, fuzzy rule-based classification systems have obtained a good behavior in the scenario of imbalanced data-sets. In this work, we focus on some modifications to further improve the performance of these systems considering the usage of information granulation. Specifically, a positive synergy between data sampling methods and algorithmic modifications is proposed, creating a genetic programming approach that uses linguistic variables in a hierarchical way. These linguistic variables are adapted to the context of the problem with a genetic process that combines rule selection with the adjustment of the lateral position of the labels based on the 2-tuples linguistic model.An experimental study is carried out over highly imbalanced and borderline imbalanced data-sets which is completed by a statistical comparative analysis. The results obtained show that the proposed model outperforms several fuzzy rule based classification systems, including a hierarchical approach and presents a better behavior than the C4.5 decision tree.

A NOVEL GENETIC FUZZY MARKUP LANGUAGE AND ITS APPLICATION TO HEALTHY DIET ASSESSMENT

In this paper, we present a novel Genetic Fuzzy Markup Language (GFML)-based genetic fuzzy system, including the genetic learning base, the knowledge base and rule base of FML, the fuzzy inference engine, and the genetic learning mechanism. The GFML is applied to the genetic fuzzy system for dealing with the knowledge base, the rule base, and the genetic learning base of the healthy diet domain, including the ingredients and the contained servings of six food categories of some common food in Taiwan. Moreover, the proposed novel system is able to infer the healthy status of human's daily eating. In the proposed system, the domain experts first define the nutrient facts of the common food to construct the fuzzy food ontology. Meanwhile, the involved Taiwanese students of National University of Tainan (NUTN) record their daily meals for a constant period of time. Then, based on the built fuzzy profile ontology, fuzzy food ontology, and fuzzy personal food ontology, a GFML-based genetic fuzzy system is carried out to infer the possibility of dietary healthy level for one-day meals. The experimental results show that the proposed GFML-based genetic fuzzy system gives good results for the healthy diet assessment.

Optimal Intelligent Control for Wind Turbulence Rejection in WECS Using ANNs and Genetic Fuzzy Approach

One of the disadvantages in Connection of wind energy conversion systems (WECSs) to transmission networks is plentiful turbulence of wind speed. Therefore effects of this problem must be controlled. �owadays, pitch-controlled WECSs are increasingly used for variable speed and pitch wind turbines. Megawatt class wind turbines generally turn at variable speed in wind farm. Thus turbine operation must be controlled in order to maximize the conversion efficiency below rated power and reduce loading on the drive-train. Due to random and non-linear nature of the wind turbulence and the ability of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificialeuraletworks (A��s) in the modeling and control of this turbulence, in this study, widespread changes of wind have been perused using MLP and RBF artificial ��s. In addition in this study, a new genetic fuzzy system has been successfully applied to identify disturbance wind in turbine input. Thus output power has been regulated in optimal and nominal range by pitch angle regulation. Consequently, our proposed approaches have regulated output aerodynamic power and torque in the nominal range.

Yarn Strength Modelling Using Genetic Fuzzy Expert System

This paper deals with the modelling of cotton yarn strength using genetic fuzzy expert system. Primarily a fuzzy expert system has been developed to model the cotton yarn strength from the constituent fibre parameters such as fibre strength, upper half mean length, fibre fineness and short fibre content. A binary coded genetic algorithm has been used to improve the prediction performance of the fuzzy expert system. The experimental validation confirms that the genetic fuzzy expert system has significantly better prediction accuracy and consistency than that of the fuzzy expert system.

INTEGRATION OF GENETIC ALGORITHMS AND FUZZY LOGIC FOR URBAN GROWTH MODELING

Abstract. Urban growth phenomenon as a spatio-temporal continuous process is subject to spatial uncertainty. This inherent uncertainty cannot be fully addressed by the conventional methods based on the Boolean algebra. Fuzzy logic can be employed to overcome this limitation. Fuzzy logic preserves the continuity of dynamic urban growth spatially by choosing fuzzy membership functions, fuzzy rules and the fuzzification-defuzzification process. Fuzzy membership functions and fuzzy rule sets as the heart of fuzzy logic are rather subjective and dependent on the expert. However, due to lack of a definite method for determining the membership function parameters, certain optimization is needed to tune the parameters and improve the performance of the model. This paper integrates genetic algorithms and fuzzy logic as a genetic fuzzy system (GFS) for modeling dynamic urban growth. The proposed approach is applied for modeling urban growth in Tehran Metropolitan Area in Iran. Historical land use/cover data of Tehran Metropolitan Area extracted from the 1988 and 1999 Landsat ETM+ images are employed in order to simulate the urban growth. The extracted land use classes of the year 1988 include urban areas, street, vegetation areas, slope and elevation used as urban growth physical driving forces. Relative Operating Characteristic (ROC) curve as an fitness function has been used to evaluate the performance of the GFS algorithm. The optimum membership function parameter is applied for generating a suitability map for the urban growth. Comparing the suitability map and real land use map of 1999 gives the threshold value for the best suitability map which can simulate the land use map of 1999. The simulation outcomes in terms of kappa of 89.13% and overall map accuracy of 95.58% demonstrated the efficiency and reliability of the proposed model.

Genetic fuzzy system for data-driven soft sensors design

This paper proposes a new method for soft sensors (SS) design for industrial applications based on a Takagi–Sugeno (T–S) fuzzy model. The learning of the T–S model is performed from input/output data to approximate unknown nonlinear processes by a coevolationary genetic algorithm (GA). The proposed method is an automatic tool for SS design since it does not require any prior knowledge concerning the structure (e.g. the number of rules) and the database (e.g. antecedent fuzzy sets) of the T–S fuzzy model, and concerning the selection of the adequate input variables and their respective time delays for the prediction setting. The GA approach is composed by five hierarchical levels and has the global goal of maximizing the prediction accuracy. The first level consists in the selection of the set of input variables and respective delays for the T–S fuzzy model. The second level considers the encoding of the membership functions. The individual rules are defined at the third level, the population of the set of rules is treated in fourth level, and a population of fuzzy systems is handled at the fifth level. To validate and demonstrate the performance and effectiveness of the proposed algorithm, it is applied on two prediction problems. The first is the Box–Jenkins benchmark problem, and the second is the estimation of the flour concentration in the effluent of a real-world wastewater treatment system. Simulation results are presented showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily with appropriate input variables and delay selection and a reasonable number of rules. The proposed methodology is able to design all the parts of the T–S fuzzy prediction model. Moreover, presented comparison results indicate that the proposed method outperforms other previously proposed methods for the design of prediction models, including methods previously proposed for the design of T–S models.

Human Gait Modeling Using a Genetic Fuzzy Finite State Machine

Human gait modeling consists of studying the biomechanics of this human movement. Its importance lies in the fact that its analysis can help in the diagnosis of walking and movement disorders or rehabilitation programs, among other medical situations. Fuzzy finite state machines can be used to model the temporal evolution of this type of phenomenon. Nevertheless, the definition of details of the model in each particular case is a complex task for experts. In this paper, we present an automatic method to learn the model parameters that are based on the hybridization of fuzzy finite state machines and genetic algorithms leading to genetic fuzzy finite state machines. This new genetic fuzzy system automatically learns the fuzzy rules and membership functions of the fuzzy finite state machine, while an expert defines the possible states and allowed transitions. Our final goal is to obtain a specific model for each person's gait in such a way that it can generalize well with different gaits of the same person. The obtained model must become an accurate and human friendly linguistic description of this phenomenon, with the capability to identify the relevant phases of the process. A complete experimentation is developed to test the performance of the new proposal when dealing with datasets of 20 different people, comprising a detailed analysis of results, which shows the advantages of our proposal in comparison with some other classical and computational intelligence techniques.

Application of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using Pittsburg approach

Genetic algorithm is well-known of its best heuristic search method. Fuzzy logic unveils the advantage of interpretability. Genetic fuzzy system exploits potential of optimization with ease of understanding that facilitates rules optimization. This paper presents the optimization of fourteen fuzzy rules for semi expert judgment automation of early activity based duration estimation in software project management. The goal of the optimization is to reduce linguistic terms complexity and improve estimation accuracy of the fuzzy rule set while at the same time maintaining a similar degree of interpretability. The optimized numbers of linguistic terms in fuzzy rules by 27.76% using simplistic binary encoding mechanism managed to improve accuracy by 14.29% and reduce optimization execution time by 6.95% without compromising on interpretability in addition to promote improvement of knowledge base in fuzzy rule based systems.

Optimal estimation and control of WECS via a Genetic Neuro Fuzzy Approach

Megawatt class wind turbines generally turn at variable speed in wind farm. Thus turbine operation must be controlled in order to maximize the conversion efficiency below rated power and reduce loading on the drive train. In addition, researchers particularly employ pitch control of the blades to manage the energy captured throughout operation above and below rated wind speed. In this study, fuzzy rules have been successfully extracted from Neural Network (NN) using a new Genetic Fuzzy System (GFS). Fuzzy Rule Extraction from Neural network using Genetic Algorithm (FRENGA) rejects wind disturbance in Wind Energy Conversion Systems (WECS) input with pitch angel control generation. Consequently, our proposed approach has regulated output aerodynamic power and torque in the nominal range. Results indicate that the new proposed genetic fuzzy rule extraction system outperforms one of the best and earliest methods in controlling the output during wind fluctuation.

Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

Abstract In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR) models and decision support (LR-FIR) models. The GFS is evaluated in an e-learning context.

A Genetic Based Neuro Fuzzy Technique for Process Grain Sized Scheduling of Parallel Jobs

Problem statement: In this study, we present the development of genetic algorithm based neuro fuzzy technique for process grain sized in scheduling of parallel jobs with the help of real lIfe workload data. Approach: The study uses the rule based scheduling strategy for the scheduling and classIfies all possible scheduling strategies. The rule bases are developed with the help of the neuro fuzzy system and with the genetic fuzzy system. From the comparison of the two classIfiers of the fuzzy systems, it is found that the neuro fuzzy system results higher error rate when compared to the genetic fuzzy system. Hence the study concentrates on reducing the error rate of the results of the neuro fuzzy system by using the genetic algorithm for improving the parameters to the neuro fuzzy system. Results: The study shows that improving parameter like weights in the layers of the neuro fuzzy system using genetic algorithm reduces the error rate and comparative results of the neuro fuzzy, genetic fuzzy and the genetic based neuro fuzzy technique are shown for the parallel job scheduling. Conclusion: The study confirmed that the Genetic Based Neuro Fuzzy Technique can be used as a better optimization tool for optimizing any scheduling algorithm and this optimization tool is used in this study for agile algorithm which is used for process grain scheduling of parallel jobs.

Fuzzy rules-based approach to estimate the availability of transportation system

The productivity of manufacturing process significantly depends on an availability of material handling systems, which in turn depends on reliability and maintainability of transportation devices. For this reason the scheduling of maintenance operations is very important to keep the high availability of transportation system. The attention in the paper is focused on a crane control problem and availability of automated material handling device, which refers to capability of a device to realise transportation operations with expected quality. An anti-sway crane control problem was solved in the paper by using indirect adaptive pole placement system with fuzzy estimator of parameters of a crane dynamic model. The paper presents the fuzzy model used to heuristic estimation of the system availability coefficient, and genetic fuzzy system that forecasts prognoses of operating time between failures. The models were implemented in the monitoring and diagnostic system tested on the laboratory stand, the overhead travelling crane.