Fuzzy Modeling Method Research Articles

A self-evolving system for robotic disassembly sequence planning under uncertain interference conditions

• A fuzzification of disassembly sequence planning (FDSP) method is proposed. • FDSP offers the capability for DSP to adapt to failures and re-planing online. • The dual-loop self-evolving framework can cope with uncertain interference conditions. Robotic disassembly sequence planning (DSP) is a research area that looks at the sequence of actions in the disassembly intending to achieve autonomous disassembly with high efficiency and low cost in remanufacturing and recycling applications. A piece of key input information being factored in DSP is the interference condition of a product, i.e., a mathematical representation of the spatial location of components in an assembly, usually in the form of a matrix. An observed challenge in the area is that the interference condition can be uncertain due to variations in the end-of-life conditions, and there is a lack of tools available in DSP under uncertain interference. To address this challenge, this paper proposes a new DSP method that can cope with uncertain interference conditions enabled by the fuzzification of DSP (FDSP) . This new approach in the core is a fuzzy and dynamic modeling method in combination with an iterative re-planning strategy, and FDSP offers the capability for DSP to adapt to failures and self-evolve online. Three products are given to demonstrate FDSP.

Water Quality Monitoring System Based on Fuzzy Algorithm

Water is one of the essential things in our life, especially for daily needs such as cooking, bathing, drinking, etc. Therefore, water quality is vital to keep humans stay healthy. The water fountain is one of the sources of water. This research designs a water monitoring device to determine the feasibility level of water produced in a water fountain using two sensors. The device measures water acidity using a pH sensor and water turbidity using a turbidity sensor. Both data measurements, water acidity, and turbidity, are processed on the Arduino nano microcontroller based on a fuzzy algorithm. The proposed fuzzy algorithm uses the Tsukamoto Fuzzy model method. The fuzzy inference system is implemented for each sensor to control the valve. There are three membership functions for the fuzzy set of pH and turbidity sensors. The model determines nine fuzzy rules that affect the solenoid valve. The solenoid valve will open if the water condition is suitable for drinking and close if the water condition is not suitable for drinking. The experimental setup is conducted for each pH and turbidity sensor. The turbidity sensor experiment used three water conditions, i.e., clean water, slightly cloud water, and dirty water. Differently, pH sensor considers three water conditions, i.e., acid, neutral, and alkaline water. Based on the result, the fuzzy inference system is determined and generates nine rules for solenoid valves as a defuzzification process. The result shows that implementing a fuzzy algorithm can be used as a filter to detect water conditions.

Fuzzy event-triggered sliding mode depth control of unmanned underwater vehicles

This paper focuses on the problem of depth controller design of unmanned underwater vehicles (UUVs) subject to ocean currents and communication delays via a Takagi–Sugeno (T–S) fuzzy modeling method and an event-triggered integral sliding mode control strategy. An event-triggered scheme is developed to avoid unnecessary redundancy in terms of communication resources and an integral sliding mode control strategy is proposed to strength anti-interference ability of the vehicle in complex ocean currents. Sufficient conditions for the existence of a practical sliding mode and the stability of the sliding motion are established in the form of linear matrix inequalities (LMIs), under which the state trajectory is compelled to a bounded region near the sliding surface and moves along the surface toward the equilibrium point after finite time. Whether inter-execution time intervals have a positive lower bound is also analyzed and the absence of the Zeno effect in the designed event-triggered scheme is corroborated. Finally, simulation results and discussions are provided to verify the effectiveness of the new design techniques.

Fuzzy Logic Modeling of Land Degradation in a Loess Plateau Watershed, China

Various land degradation processes have led to land productivity reduction, food insecurity and ecosystem destruction. The Loess Plateau (LP) suffered from severe land degradation, such as vegetation degradation, soil erosion and desertification. This study assessed land degradation changes by considering different land degradation types including vegetation degradation, soil erosion, aridity, loss of soil organic carbon and desertification in the Huangfuchuan watershed of the northern LP. A comprehensive land degradation index (LDI) was developed by combining different degradation processes using the fuzzy logic modeling method. Our results showed significant land use transitions from bare land and sandy area to grass land and forest land from 1990 to 2018, which were consistent with an obvious increase in vegetation cover from 31.24% to 40.72%. The soil erosion rate predicted by the RUSLE model decreased by 51.95% during 1990–2018. The basin-average LDI decreased from 0.68 in 1990 to 0.51 in 2018, suggesting the great success of land degradation prevention in a fragile ecological environment region on the LP during the past decades. This study proposed an integrated framework for land degradation assessment in the high erodible area. The results can provide good references for the improvement of ecological environment in the future.

Distribution of Irrigated and Rainfed Agricultural Land in a Semi-Arid Sandy Area

Under water resource and terrain constraints, a certain scale threshold of irrigated and rainfed agricultural areas exists in semi-arid sandy areas. If this threshold is exceeded, water and soil resources will be unbalanced, and the ecological environment will deteriorate. Accurate assessment of the suitable scale of cultivated land in semi-arid sandy areas is of great significance for sustainable utilization of cultivated land resources and regional ecological security. Most existing research methods are based on water resource constraints and rarely consider terrain factors. Therefore, based on the principle of water balance and with the Horqin Left Wing Rear Banner as the research area, this study adopted a multi-objective fuzzy optimization model and relative terrain index analysis method to explore the appropriate spatial ratio of irrigation and rainfed agriculture. The results show that the area of irrigated agriculture in the study area is 77,700 hm2, and the appropriate scale is 91,700 hm2. The current area of dry farming is 184,600 hm2, and the suitable scale is 117,100 hm2. The results also show that the utilization efficiency of water and soil resources in irrigated agriculture was not optimal, rainfed agriculture exceeded its suitable scale, and water and soil resources were seriously unbalanced. However, the region of cultivated land that exceeds the appropriate scale is mostly located in an area with poor terrain, less precipitation, and other unsuitable conditions for cultivation, which is prone to abandonment, resulting in deterioration of the ecological environment. Therefore, the spatial layout of agricultural land use in the study area should be adapted to local conditions, and the water-saving structure of irrigated agriculture should be optimized to achieve the maximum comprehensive benefits. Dry farming should be controlled on a reasonable scale, and the part exceeding the appropriate scale should be returned to grassland to ensure sustainable development.

An Approach Towards the Design of Interval Type-3 T–S Fuzzy System

This article providesa systematic approach for the design of an interval type-3 (IT3) Takagi–Sugeno (T–S) fuzzy logic system (FLS) using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha $</tex-math></inline-formula> - plane representation. An IT3 FLS is designed with the baseline of the general type-2 (GT2) FLS in a similar manner as an IT2 FLS was designed from the baseline of type-1 FLS. Hence, IT3 FLS evolved as a successor of GT2 FLS, where secondary membership function is an interval type-2 fuzzy set (FS), and values of tertiary membership are unity over the footprint of uncertainty of secondary membership. This extra degree of freedom in IT3 FLS provides better modeling capability as compared to GT2 FLS in the presence of a high degree of uncertainty and vagueness. The proposed system will be more appealing while dealing with uncertain information or data, which is supposed to be generated from uncertain sources; i.e., there exist uncertainties even in the presence of uncertainty. The computations needed for the design of IT3 FLS are derived using IT2 FS and GT2 FS mathematics. The design algorithms adopted for the baseline IT2 T–S fuzzy system are as per the modified interval type-2 fuzzy c-regression model clustering algorithm and hyper-plane-shaped membership function. The proposed methodology is applied to several benchmark examples and obtained results are compared with recently developed fuzzy modeling methods having a comparable number of rule bases. The proposed IT3 T–S FLS shows good performance in terms of accuracy when data is corrupted by noise and uncertainties related to missing or unvarying data exist. The computational cost is linear with design parameters and by optimum choice of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha $</tex-math></inline-formula> -planes, it is still bearable considering advantages and nature of applications.

Forecasting cryptocurrencies prices using data driven level set fuzzy models

The paper develops fuzzy models to forecast cryptocurrencies prices using a data-driven fuzzy modeling procedure based on level set. Data-driven level set is a novel fuzzy modeling method that differs from linguistic and functional fuzzy modeling in how the fuzzy rules are built and processed. The level set-based model outputs the weighted average of output functions of active fuzzy rules. Output functions map the activation levels of the fuzzy rules directly in model outputs. Computational experiments are done to evaluate the level set method in one-step-ahead forecasting of the closing prices of cryptocurrencies. Comparisons are made with the autoregressive integrated moving average, multi layer neural network, and the naïve random walk as a benchmark for Cardano, Binance Coin, Bitcoin, Ethereum, Chainlink, Litecoin, Tron, Stellar, Monero and Ripple. The results suggest that the random walk outperforms most methods addressed in this paper, confirming the Meese–Rogoff puzzle for the case of digital coins, i.e. the difficulty to surpass the naïve random walk in predicting exchange rates. However, when performance is measured by the direction of price change, the level set-based fuzzy modeling performs best amongst the remaining methods.

Fuzzy logic-based modeling method for regional multi-ship collision risk assessment considering impacts of ship crossing angle and navigational environment

The ship crossing angle and navigational environment have significant impact on the precisely modeling of regional ship collision risk, but the existed studies seldom consider this problem. In order to realize the perception of regional multi-ship collision risk, this paper proposes a fuzzy logic-based modelling method for regional multi-ship collision risk assessment considering impact of ship crossing angle and navigational environment. The method incorporates the impact factors of DCPA, TCPA, ship crossing angle and navigational environment in multi-ship encounter situation by using fuzzy logic theory. We use Analytic Hierarchy Process (AHP) to obtain the risk weights of different ships versus single ships, and then obtain the value of multi-ship collision risk. In order to verify the validity of the model, data from the Taiwan Strait is used for verification, and the results show that this method can realize the early warning of regional multi-ship collision risk. The method can provide an important basis for maritime collision risk monitoring and ship navigation risk assessment for maritime administration departments and shore-based center of maritime autonomous ships.

Fully distributed consensus for fuzzy multiagent systems with jointly connected topology

AbstractThis paper investigates the problem of fully distributed consensus for polynomial fuzzy multiagent systems (MASs) under jointly connected topologies. First, a polynomial fuzzy modeling method is presented to characterize the error dynamics that is constructed by one leader and multiple followers. Then, using the relative state information and the agents' dynamics, a distributed adaptive protocol is designed to guarantee that MASs under jointly connected topologies can achieve consensus in a fully distributed fashion. Utilizing the Lyapunov technique, a relaxed sufficient criterion is proposed to ensure consensus for fuzzy MASs under jointly connected topologies. Moreover, the adaptive coupling weights between neighboring agents can converge to certain values. The derived condition is transformed into a sum‐of‐squares form, which can be solved numerically. We provide an example to illustrate the proposed distributed adaptive consensus technique's validity.

Fuzzy identification of nonlinear dynamic system based on selection of important input variables

Input variables selection (IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure. Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno (T-S) fuzzy modeling.

Technology investigation on time series classification and prediction.

Time series appear in many scientific fields and are an important type of data. The use of time series analysis techniques is an essential means of discovering the knowledge hidden in this type of data. In recent years, many scholars have achieved fruitful results in the study of time series. A statistical analysis of 120,000 literatures published between 2017 and 2021 reveals that the topical research about time series is mostly focused on their classification and prediction. Therefore, in this study, we focus on analyzing the technical development routes of time series classification and prediction algorithms. 87 literatures with high relevance and high citation are selected for analysis, aiming to provide a more comprehensive reference base for interested researchers. For time series classification, it is divided into supervised methods, semi-supervised methods, and early classification of time series, which are key extensions of time series classification tasks. For time series prediction, from classical statistical methods, to neural network methods, and then to fuzzy modeling and transfer learning methods, the performance and applications of these different methods are discussed. We hope this article can help aid the understanding of the current development status and discover possible future research directions, such as exploring interpretability of time series analysis and online learning modeling.

The Construction of Virtual Experiment Platform Based on the Fuzzy System

Virtual experiment platform is an open network virtual experiment system, and it is constructed by virtual reality technology. It virtualizes and digitizes experimental resources in the current virtual experiment platform. Virtual experimental platform is similar to real experimental platform. Users can adjust the virtual instrument to complete the experiment process. In this paper, a virtual experiment platform model of the fuzzy system is built. Based on the acquired correlative variables of the fuzzy system, a virtual experiment platform model of the fuzzy system based on the recursive fuzzy network is established. And the model is trained and modified by using the running data of the actual fuzzy algorithm, and the prediction of the fuzzy system in the process of virtual experiment platform is realized. The experimental results show that the virtual experiment platform model of the fuzzy system based on the recursive fuzzy neural network can obtain higher prediction accuracy. Compared with standard fuzzy neural network and other modeling methods, the model of virtual experiment platform of the fuzzy system proposed in this paper can obtain better prediction effect. So, the virtual experiment platform based on the fuzzy algorithm is feasible. The virtual experiment simulation platform of the fuzzy system is built, and the experiment design and research are carried out through the virtual experiment simulation platform.

Generalized model of hierarchical management of the state of the system for production maintenance of road vehicles

This article discusses the application of the fuzzy modeling method in the study of the system for ensuring the production maintenance of road vehicles in specific conditions of their use. Keywords: road vehicle, fuzzy modeling method, production maintenance

Stability analysis for nonlinear switched singular systems via T-S fuzzy modeling

The stability for discrete nonlinear switched singular systems with unstable subsystems is investigated. First, by constructing an appropriate multiple discontinuous Lyapunov function, and utilizing the characteristics of mode-dependent average dwell time switching signals, new stability results for nonlinear switched singular system are established. Then, we adopt the T-S fuzzy modeling method to approximate the nonlinear switched singular systems and get general stability conditions in forms of linear matrix inequalities. Compared to the current results, our technique is more flexible and we also get tighter dwell time boundaries. Furthermore, a numerical example demonstrates the effectiveness of the proposed method.

Exploring relationships of urban seismic resilience assessment indicators with a fuzzy total interpretive structural model method

PurposeThe purpose of this paper is to investigate relationships of urban seismic resilience assessment indicators.Design/methodology/approachTo achieve this aim, construction of the urban seismic resilience assessment indicators system was conducted and 20 indicators covering five dimensions, namely building and lifeline infrastructure, environment, society, economy and institution were identified. Following this, this study used evidence fusion theory and intuitionistic fuzzy sets to process the information from experts then developed the fuzzy total interpretive structure model.FindingsA total of 20 urban seismic resilience assessment indicators are reconstructed into a hierarchical and visual system structure including five levels. Indicators in the bottom level including debris flow risk, landslide risk, earthquake experience and demographic characteristics are fundamental indicators that significantly impact other indicators. Indicators in the top level including open space, gas system and public security are direct indicators influenced more by other indicators. Other indicators are in middle levels. Results of MICMAC analysis visually categorize these indicators into independent indicators, linkage indicators, autonomous indicators and dependent indicators according to driving power and dependence.Originality/valueThis paper attempts to explore relationships of urban seismic resilience assessment indicators with the interpretive structural model method. Additionally, Fuzzy total interpretive structure model is developed combined with evidence fusion theory and intuitionistic fuzzy sets, which is the extension of total interpretive structure model. Research results can assist the analytic network process method in assessing urban seismic resilience in future research.

Повышение эффективности функционирования малоинтенсивных линий: постановка задачи и математические модели

Purpose: The article proposes a solution of the problem of functioning of low-intensity lines. The provisions of the strategies for the development of railway transport for various planning horizons are considered in the context of improving the efficiency of the operation of low-intensity lines. The mechanism of assessment on the set of indicators and stages of life cycle of the lines has been defined. The scenarios of sustainable development of low-intensity lines depending on life cycle phase are formed. The mechanism for the evaluation and current stimulation of low-intensity lines has been developed to maintain optimal values of key parameters. The features of the functioning of low-intensity lines on Russian railways are determined in terms of high social significance for population and strategic importance for the country and the correlation of the size of traffic in the regions with the country economic development. Mathematical formulation of the problem of effective functioning of low-intensity lines based on its decomposition is proposed to determine the stages of the life cycle. The model of managing the interaction of intense and low-intensity lines has been improved by means of fuzzy cognitive modeling methods. An enlarged fuzzy cognitive map for the control of intense and low-intensity lines is presented. Control concepts that have a significant impact on the control object are defined. Methods: Methods of analysis and synthesis, system theory, mathematical modeling are applied. Results: A solution for the problem of increasing the efficiency of the operation of low-intensity lines in modern conditions from the standpoint of loosely-structured systems is proposed. Practical significance: Mathematical formulation of the problem of low-intensity line functioning is presented which is solved by determining the stages of the life cycle and the model for managing the interaction between intense and low-intensity lines based on fuzzy cognitive modeling for practical application to existing low-intensity lines.

Economic security of regions

At the present stage of development, one of the major problems of regions is ensuring their economic security. This requires the development of effective methodological approaches to assessing economic security at the regional level by governing bodies, and the development of directions for regulating economic security at the local and state levels. The purpose of the article is to form methodological tools for assessing the economic security of regions based on the method of fuzzy modeling, to develop approaches to managing the economic security of a region by public authorities and local governments. The article has proposed and analyzed indicators for assessing the economic security of regions. The expediency of using fuzzy logic tools to assess economic security at the meso-level has been substantiated. The effectiveness of the proposed approach has been tested on the example of Dnipropetrovsk, Odessa and Chernivtsi regions in Ukraine. The integral index of economic security has been modelled, together with its components: investment, innovation, financial, foreign economic, demographic, social security and security of economic activity. It has been empirically proven that the economic security of regions is determined not only by the resources available in the region, but also by the conditions created by the state and local governments for economic and innovation activities and for maintaining the level and quality of life, as well as institutional conditions. The article has proposed directions for economic and legal regulation of economic security in Ukraine, which are aimed at increasing the level of economic security in the depressed regions in Ukraine.

Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators

The stability of the economy of any country is primarily determined by the totality of macroeconomic indicators that describe the current economic state. This article provides a multi-dimensional analysis of the macroeconomic situation in Europe according to the data of 2020. The purpose of the article is to give a clear idea of the relative position of the economies of European countries, their proximity or the significance of their differences to determine each country’s place in the overall European economic system. Research objectives: (1) to identify the necessary macroeconomic indicators for the research; (2) to determine the direction of the impact of these indicators on the economic situation of European countries; (3) to carry out a cluster division of the studied countries with the identification of the main characteristics of each cluster; (4) to identify the main macroeconomic indicators that determine the level of welfare of European countries, (5) to reduce the dimension of the multi-dimensional economic space using integrated latent factors, (6) to build a fuzzy mathematical model to predict the level of welfare of the country when the specified values of latent factors are achieved. The methodological basis of the analysis is the methods of processing multi-dimensional information, such as multi-dimensional scaling, cluster analysis, factor analysis, multivariate regression analysis, analysis of variance, discriminant analysis, and fuzzy modelling methods. The multivariate data processing was performed using the SPSS and FuzzyTech computer programs. The results obtained in the article can be useful in carrying out macroeconomic reforms to improve the economic condition of the countries.

A fuzzy analysis for porous bioceramic-magnetite nanoparticles-zirconia scaffold prepared by space holder technique with poly vinyl pyrrolidone (PVP) coating: Investigation of mechanical and biological properties

Many efforts were made to make the biodegradable implants for bone repair in the last two decades. In this study, coral aragonite were successfully used to replace broken bones as these materials can make strong chemical bonds with soft tissues and bone. Among them, calcium-based ceramics have been important in terms of their ability to form the strong chemical bonds with tissues and bones. In this research, the porous scaffold is made from marine coral (calcium carbonate) composed of the magnetite nanoparticles (MNPs) and zirconia nanoparticles by space holder method under hot system pressing with a uniaxial hydraulic press. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analysis are performed to evaluate the phase, structure, and morphology of samples. The biological investigation of sample performed using phosphate buffer saline (PBS) and simulated body fluid (SBF) test was used to evaluate the ability of apatite formation and biodegradation on the porous scaffolds. The sample without any reinforcement shows a compressive strength of about 1.3–1.4 MPa at a strain rate of 10% and a fracture strength of 1.7 MPa. Moreover, the sample with MNPs reinforcement shows higher compressive strength than the sample containing zircon nanoparticles with 7.4 MPa at the strain of about 32 MPa. The porosity results of the samples examined under SEM images show that the porosity reduced from 65% to 58%, and the pore size was about 100–200 μm. A fuzzy logic modeling is also used to predict the outputs for any set of input values using the experimental data in this study. The results show that the developed model is a precise one, and the fuzzy modeling method can be used in similar studies.

T–S Fuzzy-Based Event-Triggering Attitude-Tracking Control for Elastic Spacecraft With Quantization

Based on the T–S fuzzy modeling method, this article aims to deal with the problem of event-triggered integral sliding-mode attitude-tracking control for a nonlinear elastic spacecraft system with the unknown actuator dead-zone and a redundant reaction wheel over digital communication channels. Considering the effect of external disturbance and the restrictions imposed by wireless network transmission, a classical dynamic logarithmic sensor-to-controller quantizer and an event-triggered mechanism are introduced to perform the analysis and design work. The T–S fuzzy model is introduced to describe the nonlinear attitude dynamic property of the flexible spacecraft, and an integral sliding surface is employed in this article. The robustness of the closed-loop attitude-tracking control system and the finite-time reachability of the sliding-surface domain are guaranteed by the presented quantized event-triggering adaptive sliding-mode control law. Simulation results are provided to verify the feasibility of the proposed attitude-tracking control strategy.