Search

Abstract: In this study, we discuss a design of an optimized cascade fuzzy controller for the rotary inverted pendulum system and ball & beam system by using an optimization vehicle of differential evolution (DE). The structure of the differential evolution optimization environment is simple and a convergence to optimal values realized here is very good in comparison to the convergence reported for other optimization algorithms. DE is easy to use given its mathematical operators. It also requires a limited computing overhead. The rotary inverted pendulum system and ball & beam system are nonlinear systems, which exhibit unstable motion. The performance of the proposed fuzzy controller is evaluated from the viewpoint of several performance criteria such as overshoot, steady-state error, and settling time. Their values are obtained through simulation studies and practical, real-world experiments. We evaluate and analyze the performance of the proposed optimal fuzzy controller optimized by Genetic Algorithm (GA), and DE. In this setting, we show the superiority of DE versus other methods being used here as well as highlight the characteristics of this optimization tool. [Copyright &y& Elsevier]

Abstract: In this study, we introduce the design methodology of an optimized fuzzy controller with the aid of particle swarm optimization (PSO) for ball and beam system. The ball and beam system is a well-known control engineering experimental setup which consists of servo motor, beam and ball. This system exhibits a number of interesting and challenging properties when being considered from the control perspective. The ball and beam system determines the position of ball through the control of a servo motor. The displacement change of the position of ball leads to the change of the angle of the beam which determines the position angle of a servo motor. The fixed membership function design of type-1 based fuzzy logic controller (FLC) leads to the difficulty of rule-based control design when representing linguistic nature of knowledge. In type-2 FLC as the expanded type of type-1 FL, we can effectively improve the control characteristic by using the footprint of uncertainty (FOU) of the membership functions. Type-2 FLC exhibits some robustness when compared with type-1 FLC. Through computer simulation as well as real-world experiment, we apply optimized type-2 fuzzy cascade controllers based on PSO to ball and beam system. To evaluate performance of each controller, we consider controller characteristic parameters such as maximum overshoot, delay time, rise time, settling time, and a steady-state error. In the sequel, the optimized fuzzy cascade controller is realized and also experimented with through running two detailed comparative studies including type-1/type-2 fuzzy controller and genetic algorithms/particle swarm optimization. [Copyright &y& Elsevier]

Abstract: In this study, we present a design of an optimized fuzzy cascade controller based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) for a rotary inverted pendulum system. In this system, one controls the movement of a pendulum through the adjustment of a rotating arm. The objective is to control the position of the rotating arm and to make the pendulum maintain the unstable equilibrium point at vertical position. To control the system, we design a fuzzy cascade controller scheme which consists of two fuzzy controllers arrange in a cascaded topology. The parameters of the controller are optimized by means of the HFCGA algorithm. The fuzzy cascade scheme comprises two controllers located in two loops. An inner loop controller governs the position of the rotating arm while an outer controller modifies a set point of the inner controller implied by the changes of the angle of pendulum. The HFCGA being a computationally effective scheme of the Parallel Genetic Algorithm (PGA) has been developed to eliminate an effect of premature convergence encountered in Serial Genetic Algorithms (SGA). It has emerged as an effective optimization vehicle to deal with very large search spaces. A comparative analysis involving computing simulations and practical experiment demonstrates that the proposed HFCGA based fuzzy cascade controller comes with superb performance in comparison with the conventional Linear Quadratic Regulator (LQR) controller as well as HFCGA-based PD cascade controller. [Copyright &y& Elsevier]

Abstract: The paper is concerned with a hybrid optimization of fuzzy inference systems based on hierarchical fair competition-based parallel genetic algorithms (HFCGA) and information granulation. The process of information granulation is realized with the aid of the C-Means clustering. HFCGA being a multi-population based parallel genetic algorithms (PGA) is exploited here to realize structure optimization and carry out parameter estimation of the fuzzy models. The HFCGA becomes helpful in the context of fuzzy models as it restricts a premature convergence encountered quite often in optimization problems. It concerns a set of parameters of the model including among others the number of input variables to be used, a specific subset of input variables, and the number of membership functions. In the hybrid optimization process, two general optimization mechanisms are explored. The structural development of the fuzzy model is realized via the HFCGA optimization and C-Means, whereas to deal with the parametric optimization we proceed with a standard least square method and the use of the HFCGA technique. A suite of comparative studies demonstrates that the proposed algorithm leads to the models whose performance is superior in comparison with some other constructs commonly used in fuzzy modeling. [Copyright &y& Elsevier]

Abstract: In this study, we introduce a design methodology for an optimized fuzzy cascade controller for ball and beam system by exploiting the use of hierarchical fair competition-based genetic algorithm (HFCGA). The ball and beam system is a well-known control engineering experimental setup which consists of servo motor, beam and ball and exhibits a number of interesting and challenging properties when considered from the control perspective. The position of ball is determined through the control of a servo motor. The displacement change of the position of ball requires the change of the angle of the beam which determines the position angle of a servo motor. Consequently, the variation of the position of the moving ball and the ensuing change of the angle of the beam results in the change of the position angle of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer (1st) controller and the inner (2nd) controller in a cascaded architecture. Auto-tuning of the parameters of the controller (viz. scaling factors) of each fuzzy controller is realized with the use of the HFCGA. The set-point value of the inner controller (the 2nd controller) corresponds to the position angle of a servo motor, and is given as a reference value which enters into the inner controller as the 2nd controller of the two cascaded controllers. HFCGA is a kind of a parallel genetic algorithm (PGA), which helps alleviate an effect of premature convergence being a potential shortcoming present in conventional genetic algorithms (GAs). A detailed comparative analysis carried out from the viewpoint of the performance and the design methodology, is provided for the fuzzy cascade controller and the conventional PD cascade controller whose design relied on the use of the serial genetic algorithms. [Copyright &y& Elsevier]

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the condition part of the rule-based structure of the gHFNN. The conclusion part of the gHFNN is designed using PNNs. We distinguish between two types of the simplified fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the conclusion part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, we experimented with three representative numerical examples. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when compared with other neurofuzzy models. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we introduce a concept of self-organizing neurofuzzy networks (SONFN), a hybrid modeling architecture combining relation-based neurofuzzy networks (NFN) and self-organizing polynomial neural networks (PNN). For such networks we develop a comprehensive design methodology and carry out a series of numeric experiments using data coming from the area of software engineering. The construction of SONFNs exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the SONFN results from a synergistic usage of NFN and PNN. NFN contributes to the formation of the premise part of the rule-based structure of the SONFN. The consequence part of the SONFN is designed using PNNs. We discuss two types of SONFN architectures with the taxonomy based on the NFN scheme being applied to the premise part of SONFN and propose a comprehensive learning algorithm. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SONFN are not predetermined (as this is usually the case for a popular topology of a multilayer perceptron). The experimental results deal with well-known software data such as the NASA dataset concerning software cost estimation and the one describing software modules of the medical imaging system (MIS). In comparison with the previously discussed approaches, the self-organizing networks are more accurate and exhibit superb generalization capabilities. [Copyright &y& Elsevier]

This paper proposes an identification method for nonlinear models realized in the form of implicit rule-based fuzzy-neural networks (FNN). The design of the model dwells on the technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithm. The FNN modeling and identification environment realizes parameter estimation through a synergistic usage of clustering techniques, genetic optimization and a complex search method. An HCM (Hard C-Means) clustering algorithm helps determine an initial location (parameters) of the membership functions of the information granules to be used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the optimization algorithm of a GA hybrid scheme. The proposed GA hybrid scheme combines GA with the improved complex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) is used in the model design in order to achieve a sound balance between its approximation and generalization abilities. The proposed type of the model is experimented with several time series data (gas furnace, sewage treatment process, and NOx emission process data of gas turbine power plant). [Copyright &y& Elsevier]

A Soft Computing technique is one of the essential problems solving techniques for the researchers present in the world. Compared to other issues solving techniques, the Soft Computing domain gives outstanding performance, so the researchers are concentrating the Soft Computing domain for solving problems. Soft Computing domain categorizes into many sub-domains, like Neural Networks, Machine Learning, and Genetic Algorithm. Through this paper, we considered to study the impact produced by the Institutions as well as authors in the domain of Soft Computing from the year 1999 to 2019 for PubMed database. From the overall study, we found that China has made more number of publications, author productivity, and influential authors. Some countries, like Russia, Saudi Arabia, and Turkey, with minimum author productivity and minimum publications from the Asian continent in the Soft Computing-related domains. From the research, we determined that china dominates in terms of Institutions wise and Author productivity in the field of Soft Computing domain. [ABSTRACT FROM AUTHOR]