Fuzzy Integral Research Articles

Ensemble Technique of Deep Learning Model for Identifying Tomato Leaf Diseases Based on Choquet Fuzzy Integral

As tomato leaves are often attacked by various microorganisms, pests and bacterial diseases, the yield of tomato is seriously reduced. Accurate and timely identification of tomato leaf diseases is of great significance to reduce farmers’ economic losses. Ensemble learning as a combinatorial optimization method, which can improve the generalization ability and model stability, is widely used in the field of plant leaf disease identification. However, commonly used ensemble methods such as majority voting, weighted averaging, etc. do not consider the interaction between inputs when aggregating the inputs of multiple models, such that they do not produce representative outputs. To solve this problem, this paper adds fuzzy algorithms to the ensemble method, i.e., five pre-trained deep learning models, namely VGG16, VGG19, Xception and InceptionV3 and InceptionResnet V2, are integrated using Choquet integral fuzzy integrals for four classes of tomato disease identification and classification. The experimental results show that the proposed method achieves encouraging results, with the best single deep learning model achieving 98.63% accuracy on the PlantVillage dataset and the proposed fuzzy ensemble method achieving 99.80% accuracy. For the identification of tomato diseases in natural scenarios, the proposed method achieves 97% accuracy. The method can effectively identify tomato diseases.

Optimal design of adaptive model predictive control based on improved GWO for autonomous vehicle considering system vision uncertainty

The tuning issue of the parameters and the system uncertainty represent big challenges in most engineering applications. In this regard, a new tuning approach is developed for adaptive model predictive control (AMPC) of autonomous vehicles (AVs). The proposed control strategy tackles the uncertainty issue of the vision system due to the variation of the velocity, road curvature, time delay, and look ahead distance. The proposed AMPC is designed depending on a new improved grey wolf optimizer (IGWO) algorithm with different learning procedures and fitness-distance balance (FDB) approach to equilibrium the exploration and exploitation manners of the original GWO. This development of GWO based on FDB can increase the capability of the GWO for global search and avoid trapping in local states. Furthermore, the proposed IGWO does not require more adjustable parameters that can enhance the convergence to the best global solution rapidly. The new IGWO is compared with recent optimization algorithms such as the mayfly optimization algorithm (MA), chimp optimization algorithm (COA), dynamic arithmetic optimization algorithm (DAOA), whale optimization algorithm (WOA), jaya algorithm (JA), archimedes optimization algorithm (AOA), and equilibrium optimizer (EO) as well as the original GWO. Besides, the performance of the designed AMPC is compared with the fuzzy logic proportional integral (FLPI) controller and the traditional MPC. Various test scenarios are performed to assert the robustness and effectiveness of the designed AMPC against the variation of the velocity, road curvature, time delay, and look ahead distance. The results emphasize that the proposed AMPC can perform the best damping response with an overshoot of around 1.3 m and a settling time of around 1.12 s less than the FLPI controller and the traditional MPC.

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

FI-NPI: Exploring Optimal Control in Parallel Platform Systems

Typically, the current and speed loop closure of servo motor of the parallel platform is accomplished with incremental PI regulation. The control method has strong robustness, but the parameter tuning process is cumbersome, and it is difficult to achieve the optimal control state. In order to further optimize the performance, this paper proposes a double-loop control structure based on fuzzy integral and neuron proportional integral (FI-NPI). The structure makes full use of the control advantages of the fuzzy controller and integrator to improve the performance of speed closed-loop control. And through the feedforward branch, the speed error is used as the teacher signal for neuron supervised learning, which improves the effect of current closed-loop control. Through comparative simulation experiments, this paper verifies that the FI-NPI controller has a faster dynamic response speed than the traditional PI controller. Finally, in this paper, the FI-NPI controller is implemented in C language in the servo-driven lower computer, and the speed closed-loop test of the BLDC motor is carried out. The experimental results show that the FI-NPI double-loop controller is better than the traditional double-PI controller in performance indicators such as convergence rate and RMSE, which confirms that the FI-NPI double-loop controller is more suitable for BLDC servo control.

Micro-vibration control of spaceborne payload with smart parallel struts

Currently, the observation resolution of Earth observation satellites and space telescopes is progressively increasing. However, vibrations induced by solar panel vibrations, control torque gyroscopic reaction forces, and internal satellite noise significantly impede the observation accuracy of space-borne precision optical payloads. To mitigate the impact of vibrations on precision payload, it is necessary to incorporate isolation devices into the transmission path between the payload and spacecraft bus. This paper aims to design a six-degree-of-freedom parallel struts for active control of vibration in sensitive payloads. An optical payload isolation system is constructed using a Gough-Stewart parallel configuration with flexible hinges. The dynamic model of the system is established by the Newton-Euler method. The dynamic characteristics and response of the isolation system are analyzed. Then the active controller is designed based on the Fuzzy Auto-Disturbance Rejection Control strategy, and the effectiveness of the controller is simulated and compared with the fuzzy Proportion Integration Differentiation (PID) control algorithm. The results show that the active parallel struts exhibit excellent vibration isolation performance and possess strong robustness, which provides a theoretical reference for further space applications.

Optimizing IRB1410 industrial robot painting processes through Taguchi method and fuzzy logic integration with machine learning

Optimizing IRB1410 industrial robot painting processes through Taguchi method and fuzzy logic integration with machine learning

Research on Case Reasoning Multi-attribute Group Decision-making Method Based on Hesitant Fuzzy Set

In this study, a hesitant fuzzy set-based case-based reasoning integration method is proposed for the multi-attribute group decision-making problem with unknown attribute weights and mixed forms of attribute values. First, from two perspectives, traditional distance measure and information theory, a multi-objective optimization model is constructed using the distance similarity measure and information entropy of each type of attributes to determine the attribute weights. Secondly, considering the hybrid and nonlinear characteristics of case data, based on the principle of symmetric interaction entropy and TOPSIS method, a global similarity measure based on symmetric interaction entropy is proposed and a case inference algorithm suitable for hesitant fuzzy environment is designed. Finally, by analyzing the arithmetic cases of the target case in the case base, the most similar historical cases to the target case are retrieved to determine the decision-making scheme, and the practicality and feasibility of the decision-making method are verified. The results show that considering hesitant fuzzy theory for case-based reasoning research will help improve the accuracy and reliability of decision-making and provide more effective support for multi-attribute group decision management.

IoT and fuzzy logic integration for improved substrate environment management in mushroom cultivation

The current era witnesses the advent of the Internet of Things (IoT), a transformative force giving rise to various technological innovations, most notably connected objects. This paradigm shift catalyzes the widespread adoption of autonomous decision-making systems, particularly in sectors like agriculture, where the aim is to amplify productivity. As a result of the agricultural domain, mushrooms have concurrently emerged as a significant component of daily diets, offering additional vitamins and flavor. Despite their popularity, cultivating mushrooms in open environments poses challenges in maintaining optimal environmental conditions, prompting numerous research efforts. Recognizing the inconsistencies in existing approaches to safeguard vital parameters in mushroom farms, this paper introduces an innovative system utilizing intelligent sensors whose real-time records are managed based on the fuzzy sets concept. These sensors, encompassing the Capacitive Soil Moisture Sensor v1.2, DHT22 Sensor, Light Dependent Resistor (LDR sensor), and Passive Infrared Receiver (PIR sensor), collectively capture essential data for decision-making in mushroom farming. Employing fuzzy logic, the system addresses pivotal aspects such as substrate watering, environmental control, light management, and pest detection. Through experimental results, it becomes evident that the proposed system not only exemplifies the potential of IoT technologies in agriculture but also offers a comprehensive and efficient approach to real-time decision-making. By aggregating sensor data, the system proves instrumental in enhancing the quality and yield of mushroom crops, showcasing a promising trajectory for sustainable and technologically driven agricultural practices.

Intelligent control decision integrating fuzziness and randomness for automatic management of cash flow.

Automatic management of cash flow from the perspective of cybernetics decisions can improve work efficiency and accuracy of cash flow management. Disadvantage of traditional fuzzy control method is that it only expresses fuzziness and ignores randomness. The automatic management of cash flow involves variables representing the fuzziness and randomness of human cognition which need new calculation methods to solve. Based on fuzzy control this paper proposes a cloud set control decision method for cash flow management. Cloud set and its I operation and P operation are described. Methods are studied including observation variables and control variables, fuzziness of observation variables and control variables, description of rules, and cloud reasoning based on cloud set. The method is applied successfully in automatic management of cash flow in which control amount of expenditure intensity is -2.285. It is shown that this method can effectively obtain reasonable control quantities considering fuzzy and random properties by the comparison with fuzzy control method. The method for automatic management of cash flow proposed has greater objectivity and effectiveness for the integration of fuzzy and randomness representing human cognition and decision.

Dynamic-Deep-Ensemble-Learning Scheme for Probabilistic Voltage Stability Margin Estimation to Enhance Resilient Power Grid Monitoring

Modern power grids are characterized by significant penetration of renewable energy sources (RES), variable power demand, and aging transmission infrastructure, all of which contribute to a high degree of operational uncertainty. Such uncertainty complicates the assessment of the static voltage stability in power grids. In response to this challenge, this paper proposes a novel deep ensemble learning-based approach to assess the probabilistic voltage stability margin (PVSM) for strengthening the resilience of power grid monitoring. First, the estimation of the PVSM is formulated as a quantile regression problem. Then, an improved deep quantile regression (iDQR) is utilized to generate a set of quantiles under specific nominal proportions. Next, a dynamic deep ensemble learning (D2EL) scheme based on diverse iDQR models and an improved Choquet fuzzy integral (iCFI) algorithm is proposed to enhance the overall performance of predictive quantiles for the PVSM. The proposed D2EL-based PVSM estimation approach is capable of accommodating system changes in a timely manner, thus providing higher estimation accuracy and stronger adaptability than conventional approaches. A comprehensive numerical study of several test systems is carried out, taking into account uncertain RES and loads, as well as topology changes. The results reveal the impressive performance of the proposed approach in the PVSM assessment.

Bearing early fault identification and condition monitoring based on dynamic difference index

Bearing condition monitoring is essential for early fault detection and early warning of large equipment, and signal processing techniques are frequently used to analyse nonlinear and nonstationary sequences. Cross-correlation integral is implemented in bearing condition monitoring because it can analyse the non-stationarity of time series in dynamic systems. This paper proposes a dynamic difference index (DDI) due to the difficulty of determining the threshold in the cross-correlation integral and the roughness and operation caused by sequence similarity of 0 or 1. It is a measure of the similarity between the fuzzy autocorrelation integral of a portion of a time series and the cross-correlation between that portion and other portions of the same time series, and it is used to determine the stationarity of the time series. When bearings begin to degrade or develop structural defects, the DDI changes dramatically. The XJTU-SY dataset and IMS Bearing Data were utilised for algorithm validation. First, the algorithm’s efficacy was demonstrated by optimising the effects of various thresholds, distance measures, and time window sizes on DDI and computational efficiency. Secondly, the comparison with common methods and state of the art shows the superiority of the algorithm in detecting early bearing faults, and reveals its appropriate use in practical engineering applications.

Development of demolition project hazard index using the possibility approach

Hazard assessment is the most crucial criterion for demolition projects’ safety due to its dynamics and characteristics. This study presents a framework to assess demolition hazards by developing the demolition project hazard index (DPHI), which represents hazard level of the demolition project’s using the possibility approach. The three-level hierarchical framework consisting of eight components and 29 attributes was adopted to evaluate DPHI. The evaluation methodology and weight determination of the hazardous components and their attributes were carried out using fuzzy measures, fuzzy integrals, Analytic Hierarchy Process (AHP) with cosine maximization (CM), and Weighted Sum Method (WSM). This study was validated by establishing the correlation between DPHI and safety budget of several demolition projects. In addition, the sensitivity analysis was presented to examine the influence of variation in components’ weights over DPHI. The DPHI will help distinguish the demolition projects based on their hazard level, which allows the stakeholders to understand a more realistic view of the hazard levels and, accordingly, the necessary resources can be assigned to expand the safety management in demolition projects. Application of possibility approach, AHP with CM, and consideration of the three-level hierarchical framework are some of the key contributions and highlights of this study.

Observer-based preview control for T-S fuzzy systems

PurposeConsidering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator and preview controller, is considered to address the tracking control problem.Design/methodology/approachThe authors employ an augmentation technique to construct an augmented error system for uncertain T-S fuzzy discrete-time systems with time-varying uncertainties. Additionally, the authors obtain the corresponding linear matrix inequality (LMI) conditions for designing the preview controller.FindingsThis paper discusses the preview tracking problem for nonlinear systems. First, considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator, and preview controller, is considered to address the tracking control problem. Then, using the fuzzy Lyapunov functional with the linear matrix inequality (LMI) technique, new sufficient conditions for the asymptotic stability of the augmented system are derived by applying the LMI technique. The preview controller and fuzzy observer can be designed in one step. Finally, a numerical example is used to illustrate the effectiveness of the results.Originality/valueAn augmented error system is successfully constructed by the state augmentation approach. A novel preview controller is designed to address the tracking control problem. The preview controller and fuzzy observer can be designed in one step.

Risk Linkage Model for Cooking Oil Supply Chain

Cooking oil is one of nine elements of Indonesian staple cuisine, also known as basic food. In the middle of 2022, the public will experience cooking oil scarcity, with prices that are significantly higher than before. The price increase of CPO (Crude Palm Oil) raw materials, disruption of distribution channels, low supplies of domestic palm oil raw materials, and some domestic cooking oil producers exporting without permission from the government and violating the law are some of the causes of the rise in cooking oil prices. As a result, the supply that should be available to Indonesians has been curtailed, and there is a lack of cooking oil. One of the palm oil generating sectors involves supply chain activities that include palm oil suppliers, producers converting it into crude oil, and then sending the crude oil to domestic customers who utilize the crude oil as raw material to make the products they want, namely oil. fry. Unwanted risk occurrences will occur as a result of the supply chain activities that are formed. The goal of this study is to identify risk events, analyze the linkage between risks in the cooking oil supply chain using the DEMATEL-ISM-MICMAC fuzzy technique, and develop risk reduction strategies. DEMATEL fuzzy integration approach for developing structural models including complicated cause-and-effect interactions. Then, using graphs, ISM maps and visualizes risk correlations. The MICMAC approach is used to categorize existing elements based on their dependence power and driver power. The findings of this study found eleven risk events, with shipping uncertainty (R8) being the most influential risk event. To combat shipping unpredictability, the recommended mitigation action strategy is to supply alternative shipping partners.

A dynamic fuzzy group recommender system based on intuitionistic fuzzy choquet integral aggregation

A dynamic fuzzy group recommender system based on intuitionistic fuzzy choquet integral aggregation

Fuzzy integration into sustainability review

Fuzzy integration into sustainability review

Fuzzy integration into sustainability review

Fuzzy integration into sustainability review

Fusion of Explainable Deep Learning Features Using Fuzzy Integral in Computer Vision

Fusion of Explainable Deep Learning Features Using Fuzzy Integral in Computer Vision

Evaluation of ecosystem sustainability in Changchun City, China based on improved emergy analysis and fuzzy clustering integration model

Evaluation of ecosystem sustainability in Changchun City, China based on improved emergy analysis and fuzzy clustering integration model

Algoritmi za procenu kvalitativnih i kvantitativnih rizika kreditiranja malih i srednjih preduzeća zasnovani na fazi računu

Today, crisis conditions in the economy and finance require high-quality risk assessment. In the article, the authors propose two algorithms for assessing the projects lending risks (PLRs) to small and medium-sized businesses. To assess qualitative PLRs, we proposed to use a hierarchical system of criteria, in which the importance of the criteria is described using the Sugeno fuzzy measure, and the generalized estimate of the qualitative risk is calculated using the Sugeno fuzzy integral. To evaluate quantitative PLRs, we proposed to use the characteristics of fuzzy numbers that describe the project effectiveness criteria and have an arbitrary-form membership function. In addition, to describe quantitative risks, we proposed to use the risk-function of a fuzzy number, which reflects not only the size of possible losses, but also the possibility of their occurrence. This allows you to comprehensively and objectively assess the level of risks. We have demonstrated and discussed this algorithms on the example of preparing data for making a decision on lending to a project for the production of corn syrup in Ukraine.