Fuzzy Logic Interface Research Articles

Fuzzy logic-based prediction and parametric optimizing using particle swarm optimization for performance improvement in pyramid solar still.

The primary objective of this study is to develop a robust model that employs a fuzzy logic interface (FL) and particle swarm optimization (PSO) to forecast the optimal parameters of a pyramid solar still (PSS). The model considers a range of environmental variables and varying levels of silver nanoparticles (Ag) mixed with paraffin wax, serving as a phase change material (PCM). The study focuses on three key factors: solar intensity ranging from 350 to 950 W/m2, water depth varying between 4 and 8 cm, and silver (Ag) nanoparticle concentration ranging from 0.5 to 1.5% and corresponding output responses are productivity (P), glass temperature (Tg), and basin water temperature (Tw). The experimental design is based on Taguchi's L9 orthogonal array. A technique for ordering preference by similarity to the ideal solution (TOPSIS) is utilized to optimize the process parameters of PSS. Incorporating a fuzzy inference system (FIS) aims to minimize the uncertainty within the system, and the particle swarm optimization algorithm is employed to fine-tune the optimal settings. These methodologies are employed to forecast the optimal conditions required to enhance the productivity of the PSS.

Enhancing Audio Accessory Selection through Multi-Criteria Decision Making using Fuzzy Logic and Machine Learning

This research paper aims to investigate the significance of electrical products, specifically earbuds and headphones, in the digital world. The processes of decision-making and purchasing of audio accessories are often characterized by a significant investment of time and effort, as well as a complex interplay of competing priorities. In addition, various methodologies are employed for the selection and procurement of audio equipment through the utilization of machine learning algorithms. This study aimed to gather responses from a diverse group of participants regarding their preferences for the latest functionalities and essential components in their gadgets. The data was collected through a questionnaire that provided multiple options about the specifications of the audio accessories for the participants to choose from. The study employed seven distinct input factors to elicit responses from participants. These factors included brand, type, design, fit, price, noise cancellation, and folding design. The quantification of each input parameter was executed through the utilization of a scaling function in the Fuzzy Logic Interface, which assigned the labels “Yes” or “No” to each parameter. In this study, the Mamdani approach, which is a widely used fuzzy reasoning tool, was employed to develop a fuzzy logic controller (FLC) consisting of seven input and one output processes. In this study, standard fuzzy algorithms were employed to enhance the accuracy of the process of selecting an audio accessory in accordance with the user's specific requirements on the basis of Fuzzy threshold where “Yes” signifies about the availability of such audio accessory and “No” refers to the non-availability and readjustment of the input parameters.

A novel hybrid approach with TLBO algorithm and fuzzy logic interface system for optimal design of SSSC controller

A novel hybrid approach with TLBO algorithm and fuzzy logic interface system for optimal design of SSSC controller

Design of a Fuzzy Logic Evaluation to Determine the Ergonomic Risk Level of Manual Material Handling Tasks.

In this work, we propose a fuzzy inference as a decision support system built in the MATLAB Fuzzy Logic Designer for evaluating manual material handling risk conditions. The input variables for the fuzzy decision were: (1) the total time duration of the manual material handling in one shift of 450 min, with 3 h considered the maximal exposition time; (2) 25 kg as a maximal mass reference which should never be exceeded; (3) the repetitiveness of the manual material handling task through the shift considering as the maximal frequency of four lifts per min. Results of 135 earlier direct ergonomic evaluations made using the method proposed by the ISO 11228-1 were used as validator results, and called “expected results”. The experimentation intended to simulate an ergonomic evaluation in different boundary conditions of work and verify if the fuzzy interface could correctly replicate the results of the ergonomic evaluations. As validation, the list with the 135 expected results was compared against the evaluation made by the fuzzy logic interface, called “Work_Conditions”. From the comparison, only three evaluations (0.02%) differed with respect to the expected results. Consequently, it is concluded that the fuzzy interface can be used as a tool for automating the determination of manual material handling ergonomic risk levels, with great precision.

Modeling uncertainty in declarative artifact-centric process models using fuzzy logic

In many business processes, knowledge workers collect information and make decisions about business entities. Artifact-centric process (ACP) models have been proposed to represent such knowledge-intensive processes. Declarative ACP models use precise business rules to define flexible process executions. However, in many business situations knowledge experts have to deal with uncertainty and vagueness. Currently, how to deal with such situations cannot be expressed in declarative ACP models. We propose to use the fuzzy logic framework to model uncertainty in such models. Using Guard-Stage-Milestone (GSM) schemas as declarative ACP notation, we show how GSM schemas can be adapted for modeling gradual progress of real-life knowledge-intensive processes via a fuzzy logic interface. We evaluate the proposed fuzzy GSM schemas in real-life scenarios of airport ground operations and healthcare services.

Dynamic modeling and control of five phase SVPWM inverter fed induction motor drive with intelligent speed controller

Multi-phase induction motor (IM) drive is an emerging field of electric drive applications. This enables us to use the multi-phase AC machine than a three-phase machine. Precision speed control is important for industrial applications. Hence, direct torque control (DTC) with PI or PID regulators used, and these regulators are not flexible because of a constant gain in all working conditions. Therefore, robust speed control is essential for the precision speed control in industrial applications. The major objective of this study is to investigate a novel speed control for DTC of 5-ϕ VSI with space vector PWM (SVPWM) technique fed IM drive using wavelet fuzzy logic interface system. In precise, wavelet-fuzzy controller is an alternative for the PID controller used for DTC IM drive. The speed error signal comprises many short and extraordinary-frequency components. The scaled gains are generated for a DTC IM drive by the decomposition of discrete wavelet transform and the fuzzy logic controller (FLC) from high and low-frequency signals. The investigation of 2 hp 5-ϕ VSI fed IMD is carried out using the simulation model developed in MATLAB2016a software and examined under various working conditions. The simulation results are shown in this paper to confirm the suitability for industrial applications.

The Design of a Portable Municipal Waste Incinerator With Fuzzy Logic Based Support for Emission Estimation

A fuzzy logic interface system to estimate oxygen requirement for complete combustion as well as the level of pollution from incinerator gas flue in order to manage solid waste from domestic, institutional, medical and industrial sources was designed. The designed incinerator is double chambered operating with a maximum temperature of 760 °C in the lower chamber and 1000°C in the upper chamber. The insulating wall is made up of a refractory brick of 55mm in thickness having a 2mm thickness low carbon steel as the outer wall. Hydrogen Chloride (HCl) and Nitrous oxides (NO)x are the gases was used to demonstrate the Fuzzy Inference System (FIS) model. The FIS was built with five input variables (Food, PVC, Polythene, Paper and Textile) and three input variables with two membership functions. The FIS was developed to estimation the degree of possibility distribution of pollution that should be expected when a certain composition of waste is incinerated. The plots of composition of waste high in food against oxygen require for combustion gives a possibility distribution of about 0.9 which is high according to the fuzzy set definition while the plot of waste composition high in PVC against HCL shows linearity.

Fuzzy Clustering Based Blind Adaptive OFDM System

<p>This paper present blind adaptive modulation for OFDM system. Automatic recognition of the modulation level of detected signal and blind estimation of SNR is a major task of an intelligent receiver presented here. In this paper, modulation classification is performed by fuzzy c-means clustering followed by new approach of modulation classification, fuzzy template matching considering the constellation of the received signal. In addition, SNR estimation and modulation order for the next transmission is carry out by Fuzzy Logic Interface based upon heuristics. The simulation that has been conducted shows high capability of recognition of modulation level, almost perfect estimation of SNR and adaptive with considerable BER in an Additive White Gaussian Noise channel. The performance of proposed system is simulated in computer simulation tool, MATLAB 7.0.</p><p><em>Journal of Advanced College of Engineering and Management, Vol.1, 2015</em>, pp. 51-58</p>

Applying of Fuzzy Logic Interface in Nylon Fiber Production

The nylon fiber by its high strength and modulus is becomes a potential option for many applications like general technical text ile, high performance uses, nanotechnology material as a reinforced co mposite formu lation and nanocrisper. The production of nylon fiber can be easily predicted and perform at an accurate level of energy saving with the help of using fuzzy logic interface. The circulation o f hot/cold oil flow which has normally used as the fuel to increase the temperature of the nylon fiber reactor can be precisely checked by fu zzy logic data. The pressure and temperature of the reactor can be also controlled by this process of adding fuzzy intelligence or simulat ion in the chamber of the nylon fiber production reactor.

Evaluating the expansion flexibility of flexible manufacturing systems in uncertain environments

Flexibility in manufacturing systems allows the management to actively react to some unexpected market events, allowing them to take advantage from these events, and simultaneously reduce their losses. Flexible systems have greater costs than dedicated ones. Thus, a correct evaluation of this kind of investment is needed to justify the additional costs. Nevertheless, traditional 'static' techniques often fail in this analysis. This paper uses an approach based on real options theory to evaluate the expansion flexibility of manufacturing systems, i.e., the possibility to expand the production capacity in response to deviations in product demand. A quantitative model based on the Monte Carlo technique was developed to assess this option. In order to obtain one of the required input parameters of the model, a fuzzy logic interface was implemented.

Hybrid control system design using a fuzzy logic interface

A hybrid control system is proposed for regulating an unknown nonlinear plant. The interface between the continuous-state plant and the discrete-event supervisor is designed using a fuzzy logic approach. The fuzzy logic interface partitions the continuous-state space into a finite number of regions. In each region, the original unknown nonlinear plant is approximated by a fuzzy logic-based linear model, then state-feedback controllers are designed for each linear model. A high-level supervisor coordinates (mode switching) the set of closed-loop systems in a stable and safe manner. The stability of the system is studied using nonsmooth Lyapunov functions. For illustration and verification purposes, this technique has been applied to the well-known inverted pendulum balancing problem.