Output Of Fuzzy Inference System Research Articles

Corporate social responsibility's relationship with marketing and financial performance of agricultural companies: a case study in East Java, Indonesia


 
 
 
 
 
 
 
 
 
 Agricultural companies that produce utilitarian products must prioritize corporate social responsibility (CSR) efforts to uphold their reputation (Rep) and ensure long-term sustainability through superior performance. This study aims to examine the relationship between corporate responsibility and corporate financial and marketing performance, with differentiation strategy (Diff), firm size (FS), internationalization (Int), perceived external pressure (PEP), and altruism (Alt) acting as drivers of CSR initiatives. Employing a quantitative research approach, this study utilized the fuzzy inference system (FIS) to identify the predictive relationships between variables. Purposive sampling was employed to select five legally incorporated agricultural companies in East Java. The findings indicated that, at specific value thresholds, Diff, FS, Int, PEP, or Alt could serve as drivers of CSR efforts and were associated with corporate financial performance (CFP) and corporate marketing performance (CMP) from the perspectives of CSR, Rep, and PEP. The FIS output models, aligned with the proposed hypotheses, demonstrated that certain values of these input variables positively influence the direction of the output variables. Therefore, company policymakers should carefully consider the value levels of these variables and strive to maintain them to enhance CSR efforts and improve financial and marketing performance effectively. These factors were vital for the sustainability of agricultural companies in East Java.
 
 
 
 
 
 
 
 
 

Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic

In this paper, an adaptive path construction approach for Mobile Sink (MS) in wireless sensor networks (WSNs) for data gathering has been proposed. The path is constructed based on selecting Rendezvous Points (RPs) in the sensing field where the MS stops in order to collect the data. Compared with the most existing RP-based schemes, which rely on fixed RPs to construct the path where these points will stay fixed during the whole network lifetime, we propose an adaptive path construction where the locations of the RPs are dynamically updated using a Fuzzy Inference System (FIS). The proposed FIS, which is named Fuzzy_RPs, has three inputs and one output. The inputs are: the remaining energy of the sensor nodes, the transmission distance between the RPs and the sensor nodes, and the number of surrounding neighbors of each node. The output of FIS is a weight value for each sensor node generated based on the previous three parameters and, thus, each RP is updated to its new location accordingly. Simulation results have shown that the proposed approach extends the network lifetime compared with another existing approach that uses fixed RPs. For example, in terms of using the first dead node as a metric for the network lifetime, when the number of deployed sensor nodes changes from 150 to 300, an improvement that ranges from 48.3% to 83.76% has been achieved compared with another related approach that uses fixed RPs.

Using Fuzzy Inference System in Gas Turbine to Overcome a High Exhaust Temperature Problem

The turbine units are at the forefront of equipment in the process of pumping crude oil and exporting it to the oil ports, where there are many types of turbines used in different sites of Iraqi stations whether pumping oil stations or electrical power production stations. One of the most important types of turbines is the gas turbine, which is frequently used in oil depots. One of the remarkable depots in Iraq is Zubair-1 / Basra that exports around 850,000 barrels per day (B/D). Therefore, Zubair-1 should continue pumping the crude oil 24/7, which has five gas turbines (three Rolls-Royce AVON MK 1533 and two Siemens SGT 400). However, the three Rolls-Royce gas turbines in Zubair-1 have not worked in the summer season since 2016, when the ambient temperature goes high around 11:00 am to 3:30 pm. This paper proposes solution to solve a high exhaust temperature (EGT) shutdown signal (a preventing running turbine signal) without effect on the sequence of turbine running stages. The proposal is adding a fuzzy inference system (FIS) that controls the gas turbine in the first two running stages that demonstrates and controls of speed the turbine from 800 RPM to 3000 RPM. The inputs of FIS are the average temperature of eight combustion chambers (exhaust temperatures) and the speed of the gas turbine, while the output of FIS is the control signal to the flow control valve (FCV) with an amplifier to gain the signal.
 The FIS proposal has been applied in all three Rolls-Royce jet pumping turbines since April 7, 2021, and they work regularly at all times of the day. The FIS minimizes the maximum average of combustion chambers temperature at midafternoon in June 9, 2021 (48 ˚C ambience temperature) from 689 ˚C to 610 ˚C that means the improvement is around 45%.

Fuzzy Inference System Model from Non-Fuzzy Clustering Output

Fuzzy Inference System (FIS) is a process of mapping input into the desired output using fuzzy logic theory where decisions can be made or pattern are dscerned. Ths study aims to discuss on how non-fuzzy clustering output can be used to constmct a model of FIS. Here, the proposed idea is to show the efficient use of the FIS as a predction model for the data classification. In this study, employment income, self-employment income, property and tramfer received are taken into account for clustering the household income data. Then, the FIS prediction model is built using the center values of clusters formed and the output of FIS is compared to the original cluster in whch the best fit predction model to the data is determined. In conclusion, the best predction model in identify~ngin come class is dscovered based on the Root Mean Square Error (RMSE) value computed.

Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA

Organizations are continuously endeavoring to provide a healthy work environment without any incident, by Health, Safety, and Environment (HSE) management. As most of the activities and processes in the organizations have risk-taking nature, identification and evaluation of risks can be useful to decrease their negative effects on the system. Although Failure Mode and Effect Analysis (FMEA) technique is used widely for risk assessment, the traditional Risk Priority Number (RPN) score has shortcomings like do not considering different weights and the inherent uncertainty of risk factors as well as do not regarding all viewpoints of the experts in decision making. The aim of this study is presenting a hybrid approach based on the Linguistic FMEA, Fuzzy Inference System (FIS) and Fuzzy Data Envelopment Analysis (DEA) model to calculate a novel score for covering some RPN shortcomings and the prioritization of HSE risks. First, after identifying potential risks and assigning values to the RPN determinant factors by linguistic FMEA team members due to the differentiation of these values, FIS is used to reach a consensus opinion about these factors. Then, the outputs of FIS are used by the fuzzy DEA and its supper efficiency model to risk prioritization which can contribute to full prioritization. In addition to considering uncertainty and decreasing dependence on the team’s opinions, in this phase weights of triple factors are calculated based on mathematical programming. To show the ability of the proposed approach in terms of HSE risks prioritization, it has been implemented in an active company in the chemical industry. After identifying risks having high priority based on the proposed score, preventive/corrective actions are presented in accordance with the case study, and for more analysis of results, the self-organizing map has been applied in this study.

Integrating Principal Component Analysis, Fuzzy Linguistic Reasoning and Taguchi Philosophy for Quality-Productivity Optimization

The present work demonstrates a multi-response optimization problem for selection of optimal cutting parameters (optimal process environment) for machining (turning) of nylon 6; by using Principal Component Analysis (PCA) followed by fuzzy linguistic reasoning in combination with Taguchi’s robust design technique. In this study, three controllable process parameters: cutting speed, feed, and depth of cut have been considered for obtaining desired Material Removal Rate (MRR) of the process and favorable multiple surface roughness features for the machined product; based on L9 orthogonal array experimental design. The study has been aimed to search an appropriate process environment for simultaneous optimization of quality-productivity favorably. Various surface roughness parameters of statistical importance (of the machined product) have been considered as product quality characteristics whereas; MRR has been treated as productivity measure for the said machining process. To avoid assumptions, limitations, uncertainties and imprecision in application of existing multi-response optimization techniques; Principal Component Analysis (PCA) has been proposed to convert correlated responses into uncorrelated quality indices (called individual principal components); next, a fuzzy inference system (FIS) has been proposed for meaningful and feasible aggregation of individual principal components into an equivalent single quality index, thereby, converting such a multi-objective optimization problem into an equivalent single objective optimization situation. A Multi-Performance Characteristic Index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method. The study exhibits application feasibility of the proposed approach with satisfactory result of confirmatory test.

Intelligence-guided beam angle optimization in treatment planning of intensity-modulated radiation therapy

An intelligence guided approach based on fuzzy inference system (FIS) was proposed to automate beam angle optimization in treatment planning of intensity-modulated radiation therapy (IMRT). The model of FIS is built on inference rules in describing the relationship between dose quality of IMRT plan and irradiated region of anatomical structure. Dose quality of IMRT plan is quantified by the difference between calculated and constraint doses of the anatomical structures in an IMRT plan. Irradiated region of anatomical structure is characterized by the metric, covered region of interest, which is the region of an anatomical structure under radiation field while beam’s eye-view is conform to target volume. Initially, an IMRT plan is created with a single beam. The dose difference is calculated for the input of FIS and the output of FIS is obtained with processing of fuzzy inference. Later, a set of candidate beams is generated for replacing the current beam. This process continues until no candidate beams is found. Then the next beam is added to the IMRT plan and optimized in the same way as the previous beam. The new beam keeps adding to the IMRT plan until the allowed beam number is reached. Two spinal cases were investigated in this study. The preliminary results show that dose quality of IMRT plans achieved by this approach is better than those achieved by the default approach with equally spaced beam setting. It is effective to find the optimal beam combination of IMRT plan with the intelligence-guided approach.

Taguchi approach followed by fuzzy linguistic reasoning for quality‐productivity optimization in machining operation

PurposeThe study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.Design/methodology/approachIn this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L9 orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.FindingsThe study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.Originality/valueIn the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.

Well Placement Optimization Using Hybrid Optimization Technique Combined with Fuzzy Inference System

Decision on the location of new wells through infill drilling projects is a complex problem that depends on the reservoir rock and fluid properties, well and surface facilities specifications, and economic measures. Conventional approach to address this is a direct optimization that uses the numerical flow simulation. However, this is computationally very extensive. In this study the authors use a hybrid genetic algorithm (HGA) optimization technique based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the neural network. This hybridization introduces hill-climbing into the stochastic search and makes use of proxies created and calibrated iteratively throughout the run. It is emphasized that the numerical models are constructed based on scarce data, hence the simulation forecasts are uncertain and consequently the deterministic global solution is not achievable. To resolve this the authors used a Fuzzy Inference System (FIS). For economic evaluation they use net present value (NPV). Therefore, the FIS output is incorporated into the NPV, and a new objective function called corrected NPV (CNPV) is constructed. The authors validate the method by optimizing the placement of water injection wells in a section of an oil reservoir located in the west of Iran by maximizing the CNPV. It was observed that the number of simulations required to find the optimal well configurations were reduced significantly by using HGA.

Dynamic Transmission Capability Calculation Using Integrated Analysis Tools and Intelligent Systems

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a methodology to calculate the transmission capability between two areas in a power system, considering both static and dynamic security assessment. An optimal power flow (OPF) algorithm is used to compute the transmission capability from the static point of view. The dynamic security assessment of the operating point obtained from the OPF is then evaluated. Transient, voltage, and small-signal stability assessment are considered. If any dynamic security criterion is violated, additional constraints are added to the original OPF formulation, and a new and secure transmission limit is calculated. These new constraints can be very simple, such as to limit the transmitted power, or more elaborated, based on some rules obtained from an intelligent system. The intelligent system adopted is a fuzzy inference system (FIS) that drives the redispatch of the active power generation. The FIS output is used to formulate the new constraints to the OPF. In addition to the OPF, the proposed methodology makes use of other power systems analysis tools, such as full time-domain simulation, fast simulation, and modal analysis. The methodology was implemented in a computational platform that integrates all these tools in a single framework. The results for two test systems have shown the potential benefits of the proposed methodology and the importance of considering different dynamic aspects in transmission capability calculation. </para>

An adaptive recurrent fuzzy system for nonlinear identification

This paper describes the architecture and training procedure of a recurrent fuzzy system (RFS). The RFS is composed of a fuzzy inference system (FIS) and a delayed feedback connection. The recurrent property comes from feeding the FIS output back to the FIS input via an adjustable feedback parameter. Both the on-line and off-line training procedures based on the backpropagation-through-time (BPTT) algorithm have been investigated. The adjoint model of the RFS is obtained and used to compute the gradients. It is shown that the off-line training is insufficient to adapt to changes in system dynamics. So, an on-line training procedure is derived. In this procedure, a first in first out stack is used to store a certain history of the input-output data to perform a truncated BPTT algorithm. A quasi-Newton optimization method with a line search algorithm is used to adjust the RFS parameters. The performance of the developed RFS is demonstrated by applying to the identification of nonlinear dynamic systems. The simulation studies show that the proposed identification model has the ability to learn dynamics of highly nonlinear systems and compensate system uncertainties. The results are promising for the further application in the area of control and modeling.