Optimal Design of Adaptive Neuro-Fuzzy Inference System Using PSO And Ant Colony Optimization For Estimation of Uncertain Observed Values

This study employs a new method for regression model prediction in an uncertain environment and presents fuzzy parameter estimation of fuzzy regression models using triangular fuzzy numbers. These estimation methods are obtained by new learning algorithms in which linear programming is used. In this study, the new algorithm is a combination of a fuzzy rule-based system, on the basis of particle swarm optimization (PSO) and ant Colony Optimization AC\({O}_{R}\). In addition, a simulation and a practical example in the field of machining process are applied to indicate the performance of the proposed methods in dealing with problems where the observed variables have the nature of uncertainty and randomness. Finally, the results of the proposed algorithms are evaluated.

Trajectory-Tracking Control for Manipulators Based on Fuzzy Equivalence and a Terminal Sliding Mode

To suppress the chattering of manipulators under heavy-load operations, a control method called fuzzy equivalence & terminal sliding mode (FETSM) was applied to the trajectory tracking of motion curves for manipulators. Based on the switching term of the equivalent sliding mode (ESM), a fuzzy parameter matrix processed by the simple fuzzy rules was introduced, and the fuzzy switching term was obtained. By summing the fuzzy switching term and the equivalent term of the equivalence and a terminal sliding mode (ETSM), the control law of the FETSM for manipulators was obtained. On this basis, the stability of the system was analysed and the finite arrival time of it was deduced. On the premise of ensuring the stability of the system, the fuzzy rules and membership functions were designed for the fuzzy constants in the fuzzy switching term. Simulation tests show that the proposed FETSM can ensure sufficient trajectory-tracking precision, error convergence speed, and robustness. Compared with the ETSM, the proposed FETSM can reduce the chattering time by 94.75 % on average; compared with the proportion-integral-differential (PID) control method, the maximum chattering amplitude by the FETSM can be reduced by at least 99.21 %. Thus, the proposed FETSM is suitable for those manipulators under heavy-load operations.

Research on Inter-Provincial Transfer of CO2 Emissions from Transportation by Considering Fuzzy Parameter

CO2 reduction from transportation is exerting significant effects on global CO2 reduction. This industry contributes 23.96% of global CO2 emissions. In this research, an ecological network input–output interval fuzzy linear programming (EIFP) method is developed to clarify CO2 reduction responsibilities and depict transfer relationships of transportation. This method integrates input–output analysis (IOA), inexact rough interval fuzzy linear programming (IRFLP) and ecological network analysis (ENA) into a general framework. The proposed method is employed for calculating inter-provincial CO2 transfer under different situations in 30 provinces of China and further supporting the formulation of regional reduction policies. Results demonstrate that transportation energy demand of Beijing is dependent on imports, which indirectly increases CO2 reduction pressure in energy supply areas. Therefore, CO2 reduction responsibility should be traced to source and included in emission reduction plan of energy demand areas. In inter-provincial CO2 transfer relationships of natural gas, positive relationships account for a higher proportion; therefore, it is feasible to consider raising the proportion of natural gas in the future development direction of transportation. The achievements of this paper can provide scientific references for decision makers to formulate CO2 reduction policies in transportation.

APPLICATION OF AN EFFECTIVE METHOD ON THE SYSTEM OF NONLINEAR FUZZY INTEGRO-DIFFERENTIAL EQUATIONS

In real world physical applications purpose, it is complicated to acquire an exact solution of fuzzy differential equations due to complexities in fuzzy arithmetic and therefore creating the need for the use of reliable and efficient techniques in the solution of fuzzy differential equations. The purpose of this research paper is to utilize the reliable analytic approach of homotopy perturbation Sumudu transform method for better understanding of systems of non-linear fuzzy integro-differential equations, while using the concept of fuzzy parameter in certain dynamical problems to remove the hurdles faced in numerical approach. These mathematical models are of great interest in engineering and physics. Some numerical examples are also given to demonstrate the efficiency and superiority of the method, followed by graphical representation of the comparison of exact and approximated solution by using Maple 2017

Study of Fuzzified Boundary Value Problems for MHD Couette and Poiseuille Flow of a Differential Type Fluid

In this work, discuss the magneto-hydro-dynamics (MHD) flow in three fundamental flaws of the third-grade fluid between two parallel plates in a fuzzy environment by the fuzzy Adomian decomposition method (ADM). We extend the work of Kamran and Siddique [19], using fuzzy differential equations (FDEs) and explain our approach with the help of membership function of triangular fuzzy numbers (TFNs). In the end, the effect of the fuzzy parameter (\(\alpha \in [0,\,1]\)), and other engineering parameters on fuzzy velocity profiles are investigating in graphically and tabular representation.

A Proposed AODV Black hole Detection Model using the Fuzzy Inference Method in MANET Topology

Mobile Adhoc Network (MANET)as a network of mobile routers that self-configures has raised some security concerns which one of them has been the Black Hole Attack (BHA). Studies have shown various detection and mitigation techniques in line with the black hole attack of MANET technology. However, some of the proposed models in this direction have paid less attention to how fuzzy systems could be integrated in Adhoc On-Demand Distance Vector (AODV)protocol, to further guide its detection abilities against a possible black hole attack. Hence the study proposed a model that integrates the Fuzzy Inference Method (FIM)on the existing AODV security measures in order to strengthen further its detection metrics. Also,an Intrusion Detection System (IDS) was introduced for an early check against possible BHA along with the Fuzzy Parameter Extraction module which performs all necessary parameter checks before passing it on to the FIM. The parameters into consideration were adapted from the study of Narang (2013), with this study modifying further its proposed algorithm using the added concepts earlier mentioned. The proposed model presented tends to perform better than the existing models in this direction with evidence from the theoretical underpinnings of the functionality of the added modules in the proposed system

Devising a method for finding a family of membership functions to bifuzzy quantities

This paper has considered a task to expand the scope of application of fuzzy mathematics methods, which is important from a theoretical and practical point of view. A case was examined where the parameters of fuzzy numbers’ membership functions are also fuzzy numbers with their membership functions. The resulting bifuzziness does not make it possible to implement the standard procedure of building a membership function. At the same time, there are difficulties in performing arithmetic and other operations on fuzzy numbers of the second order, which practically excludes the possibility of solving many practical problems. A computational procedure for calculating the membership functions of such bifuzzy numbers has been proposed, based on the universal principle of generalization and rules for operating on fuzzy numbers. A particular case was tackled where the original fuzzy number’s membership function contains a single fuzzy parameter. It is this particular case that more often occurs in practice. It has been shown that the correct description of the original fuzzy number, in this case, involves a family of membership functions, rather than one. The simplicity of the proposed and reported analytical method for calculating a family of membership functions of a bifuzzy quantity significantly expands the range of adequate analytical description of the behavior of systems under the conditions of multi-level uncertainty. A procedure of constructing the membership functions of bifuzzy numbers with the finite and infinite carrier has been considered. The method is illustrated by solving the examples of using the developed method for fuzzy numbers with the finite and infinite carrier. It is clear from these examples that the complexity of analytic description of membership functions with hierarchical uncertainty is growing rapidly with the increasing number of parameters for the original fuzzy number’s membership function, which are also set in a fuzzy fashion. Possible approaches to overcoming emerging difficulties have been described.

VIRTUAL MODELING AND CONTROLLING OF AN ELECTRO-HYDRAULIC ACTUATOR

Instead of experiment, this paper builds a virtual model of the electro-hydraulic actuator (EHA) thought an Amesim software to evaluate the control response. The main feature of the EHA is to use the closed-loop circuit to reduce the size and oil volume as well as to eliminate the pressure loss caused by the orifice area of the valves. Firstly, the mathematical model of the EHA is established. Secondly, based on this model, an adaptive fuzzy sliding mode controller (AFSMC) is then designed to control the accurate position of the piston. In this control strategy, the system parameters are considered unknown, and they are lumped into two unknown time varying functions. An approximate technique is used to express one of the unknown functions as a finite combination of the basis function. In addition, a fuzzy logic inference mechanism is utilized for realizing a hitting control law to remove completely the chattering problem from the conventional sliding mode control. Then, the Lyapunov stability theorem is utilized to find the adaptive laws for updating the coefficients in the approximate series and turning the fuzzy parameter.