Sugeno Fuzzy Inference System Research Articles

Modeling of Fuzzy Systems Based on the Competitive Neural Network

This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the parameters of the membership functions are adapted according to the values of the input data. In the approach, two fuzzy inference systems, Sugeno and Mamdani, are proposed. Additionally, variations of these models are presented using three types of membership functions, including Trapezoidal, Triangular, and Gaussian functions. The proposed models are applied to three classification datasets: Wine, Iris, and Wisconsin Breast Cancer (WDBC). The simulations and results present higher classification accuracy when implementing the Sugeno fuzzy inference system compared to the Mamdani system, and in both models (Mamdani and Sugeno), better results are obtained when the Gaussian membership function is used.

Sugeno fuzzy inference system modeling and DFT calculations for the treatment of pesticide-laden water by newly developed arginine functionalized magnetic Mn-based metal organic framework.

The uncontrolled utilization of pesticides poses a significant risk to the environment and human health, making its management essential. In this regard, a new arginine functionalized magnetic Mn-based metal-organic framework (Arg@m-Mn-MOF) was fabricated and assessed for the removal of cypermethrin (CYP) and chlorpyrifos (CHL) from aqueous system. The Arg@m-Mn-MOF was characterized by scanning electron microscopy, energy dispersive X-ray, Fourier transform infrared spectroscopy, X-ray diffraction, and Brunauer-Emmett-Teller analysis. Various parameters were optimized in a series of batch experiments and the following conditions were found optimal: pH: 4 and 5, contact time: 20min, adsorbent dosage: 0.6 and 0.8g L-1 with initial concentration: 10mg L-1 and temperature: 298K for CYP and CHL, respectively. The composite attained a maximum removal capacity of 44.84 and 71.42mgg-1 for CYP and CHL, respectively. The elucidated data was strongly fitted to the pseudo-second-order model of kinetics (R2 > 0.98) and Langmuir isotherm (R2 > 0.98). Based upon 350 experimental datasets obtained from batch studies and interpolated data, the adsorption capacity of the adsorbent was elucidated with R2 > 0.97 (CHL) and > 0.91 (CYP). The adsorption energy (- 11.67kcalmol-1) calculated by Gaussian software suggests a good interaction between arginine and CHL through H-bonding. The present study's findings suggested the prepared Arg@m-Mn-MOF as a promising adsorbent for the efficient removal of pesticides from agriculture runoff.

Mamdani vs. Takagi-Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models.

The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi-Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi-Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi-Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model.

Enhanced sugeno fuzzy inference system with fuzzy AHP and coefficient of variation to diagnose cardiovascular disease during pregnancy

Accurate prediction of cardiovascular disease (CVD) risks during pregnancy is vital for preventing and treating life-threatening condition. Existing research has explored fuzzy inference systems and machine-learning techniques for predicting cardiovascular disease risks. However, these models have not explicitly addressed pregnancy-related cardiovascular disease. This study proposes a novel hybrid system that combines the fuzzy analytic hierarchy process (fuzzy-AHP), coefficient of variation (CV) and Sugeno fuzzy inference system with the optimized rule to diagnose cardiovascular disease during pregnancy. The study considers 12 cardiovascular disease risk factors and involves an experienced cardiac clinician and gynaecologist in determining their contributions. Fuzzy-AHP is employed for assigning weights to risk factors and distributing them among sub-risk factors based on their relative contributions. The coefficient of variation is utilized for calculating the weights of the strings, while the nearest neighboring method is employed to cluster the potential strings. The obtained rules are incorporated into the Sugeno fuzzy inference system to compute the output of CVD during pregnancy. The system’s performance is evaluated using an online clinical dataset of 1015 maternal health risks, performance measures, receiving operating system, and statistical analysis. The hybrid system detects cardiovascular disease with 98.71% accuracy, 98.73% sensitivity, and 98.91% precision. This suggest that the proposed technique could be an accurate and valuable tool for predicting cardiovascular disease risks during pregnancy in clinical settings.

A Wheeled Mobile Robot Obstacles Avoidance for Navigation Control in a Static and Dynamic Environments

This research focuses on the performance of the obstacle avoidance feature implemented on a wheeled mobile robot by using Sugeno Fuzzy Inference System (FIS). The test was done using a simulation software Webots and the layout was first draft out using walls, floors, and objects in order to set a boundary and obstacles for the simulation. To test the effectiveness and efficiency in terms of time and distance, the simulation time and trajectory of the Khepera implementing the Sugeno FIS was recorded and compared with the Khepera without implementing the Sugeno FIS. The inputs of the ultrasonic sensors were also recorded in order to analyze whether if the Khepera detects incoming objects or if Khepera follows the rules set in the algorithm.

Application of interval type-2 fuzzy logic and type-1 fuzzy logic-based approaches to social networks for spam detection with combined feature capabilities.

Social networks are large platforms that allow their users to interact with each other on the Internet. Today, the widespread use of social networks has made them vulnerable to malicious use through different methods such as fake accounts and spam. As a result, many social network users are exposed to the harmful effects of spam accounts created by malicious people. Although Twitter, one of the most popular social networking platforms, uses spam filters to protect its users from the harmful effects of spam, these filters are insufficient to detect spam accounts that exhibit new methods and behaviours. That's why on social networking platforms like Twitter, it has become a necessity to use robust and more dynamic methods to detect spam accounts. Fuzzy logic (FL) based approaches, as they are the models such that generate results by interpreting the data obtained based on heuristics viewpoint according to past experiences, they can provide robust and dynamic solutions in spam detection, as in many application areas. For this purpose, a data set was created by collecting data on the twitter platform for spam detection. In the study, fuzzy logic-based classification approaches are suggested for spam detection. In the first stage of the proposed method, a data set with extracted attributes was obtained by applying normalization and crowdsourcing approaches to the raw data obtained from Twitter. In the next stage, as a process of the data preprocessing step, six attributes in the binary form in the data set were subjected to a rating-based transformation and combined with the other real-valued attribute to create a database to be used in spam detection. Classification process inputs were obtained by applying the fisher-score method, one of the commonly used filter-based methods, to the data set obtained in the second stage. In the last stage, the data were classified based on FL based approaches according to the obtained inputs. As FL approaches, four different Mamdani and Sugeno fuzzy inference systems based on interval type-1 and Interval Type-2 were used. Finally, in the classification phase, four different machine learning (ML) approaches including support vector machine (SVM), Bayesian point machine (BPM), logistic regression (LR) and average perceptron (Avr Prc) methods were used to test the effectiveness of these approaches in detecting spam. Experimental results were obtained by applying different FL and ML based approaches on the data set created in the study. As a result of the experiments, the Interval Type-2 Mamdani fuzzy inference system (IT2M-FIS) provided the highest performance with an accuracy of 0.955, a recall of 0.967, an F-score 0.962 and an area under the curve (AUC) of 0.971. However, it has been observed that FL-based spam models have a higher performance than ML-based spam models in terms of metrics including accuracy, recall, F-score and AUC values.

Analysis of fuzzy based intelligent health care application system for the diagnosis of mental health in women with ovarian cancer using computational models

Depending on the characteristics of the cancer and the specific treatment required, each type of cancer comes with a unique set of challenges in the psychological wellbeing of women. This research work mainly focuses on Ovarian cancer since the it is one of the 5th leading cancers among women. As per the statistics of 2021, by the American Cancer Society, 21,410 women would be diagnosed with ovarian cancer and 13,770 women might die from ovarian cancer. Both physically and psychologically, ovarian cancer presents several challenges. To control the growth of the tumour, multiple treatments are required. The psychological issues in women with ovarian cancer is mainly due to “loss of femininity” that affects them while they proceed through the phases of diagnosis, treatment and recurrence. Psychological factors associated with both, having ovarian cancer and being at risk are considered in this study. In the proposed work, PHQ 9 and GAD 7 are the tools used to measure depression and anxiety among women who are undergoing treatment for ovarian cancer. The data, collected with the help of these tools, is analysed using the popular Machine Learning algorithms such as k-Nearest Neighbour (kNN), Random Forest, Support Vector Machine (SVM), Artificial Neural Network etc. The results of Machine Learning algorithms are then compared with Mamdani and Sugeno fuzzy inference models. The Sugeno fuzzy inference system outperformed in comparison to all other models, with an accuracy of 96.2% for depression and 98.83% for anxiety, followed by Mamdani fuzzy inference system giving 94.3% accuracy for depression and 96.7% for anxiety. The performance is then compared with the linear SVM which obtained an accuracy of 91.52% for depression and 93.78% for anxiety. The analysed performance of the data using computational algorithms are compared with that of expert clinical psychologists. The severely affected women are advised to visit a psychiatrist.

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

With the maturation of nonlinear systems, considerable endeavors have been made to provide valid and high-speed controllers to supervise superior and more complex systems. Artificial intelligence has been remembered as the head topic among designers in the last decade. One of the popular control techniques is fuzzy logic, which is known to provide a controller that simulates the behavior of an expert operator. On the other hand, due to the necessity of change in human energy sources and the popularity of solar energy, attention to the greatest utilization of this category of green resources has significantly increased. Maximum power point tracking (MPPT) in solar systems is a headed topic, with innovative methods being presented every day despite numerous articles. However, the less discussed topic is the choice of a fuzzy inference system. In this article, the two classes of Mamdani and Sugeno are discussed to introduce the best controller for extracting more power from a solar system by implementing both types and gaining an understanding of their differences. In addition, the influence of the number of input membership functions on the controller performance is investigated. Therefore, two different input membership functions are given to each fuzzy system model. It should be noted that fuzzy system setup has been done by genetic algorithm to respond to the mortal desire to automate various processes, which is a subset of artificial intelligence. Accordingly, four different fuzzy systems have been designed and implemented on a solar system. The results were tested and summarized in various radiations in MATLAB Simulink.

An adaptive neuro-fuzzy inference system white-box model for real-time multiphase flowing bottom-hole pressure prediction in wellbores

The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure (FBHP) predictions when real-time field well data are used. This is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental datasets. In addition, most machine learning (ML) FBHP prediction models were constructed with real-time well data points and published without any visible mathematical equation. This makes it difficult for other readers to use these ML models since the datasets used in their development are not open-source. This study presents a white-box adaptive neuro-fuzzy inference system (ANFIS) model for real-time prediction of multiphase FBHP in wellbores. 1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi–Sugeno fuzzy inference systems (FIS) structures. The dataset was divided into two sets; 80% for training and 20% for testing. Statistical performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction performance. The optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter sets. Trend analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on FBHP. In addition, graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models, empirical correlations, and machine learning models. New training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.

Development of nominal rules on the Fuzzy Sugeno method to determine the quality of power transformer insulation oil using Dissolved Gas Analysis data

This paper aims to develop the nominal rules on the Fuzzy Logic Method using the Sugeno-Fuzzy Inference System (FIS) for Dissolved Gas Analysis (DGA) and determine the quality of the power Transformer 1 and Transformer 6 insulating oil at the Buduran 150 kV substation. The nominal number of proposed fuzzy rules is 1920 rules. Implementing the Fuzzy-Sugeno method on Transformers 1 and 6 shows that the six input variables from the DGA test can produce a Total Dissolved Combustible Gas (TDCG) output value of 32.67 and 26.19 ppm, respectively. Both values indicate that the insulating oil of Transformers 1 and 6 are in condition one and, at the same time, indicates that the dissolved gas composition is in Normal status. Furthermore, the TDCG value, condition, and quality status of the insulating oil have the same or 100 % accuracy compared to the DGA test by PLN (UPT Surabaya). Thus, the nominal development of fuzzy rules using the Fuzzy-Sugeno method can perform DGA analysis more accurately to determine the quality of power transformer insulation oil compared to previous studies.

Perancangan Sistem Pakar untuk Mendiagnosa Tingkat Stress Belajar pada Siswa SMA dengan Menggunakan Metode Forward Chaining

The purpose of this research is to create an expert system using Forward Chaining to diagnose the level of learning stress in high school students using Sugeno's Fuzzy Inference System method. Data Flow Diagrams (DFD) are used to describe a data logic model or process created by the system while prototype is a development method that is used as a stage in developing applications and testing. Based on the results of research and testing of expert system design to diagnose learning stress levels in high school students using the Website-based Forward Chaining method that has been carried out by the author, several conclusions can be drawn, namely; This research succeeded in making an expert system design to diagnose the level of learning stress in high school students using the Forward Chaining method, and this study succeeded in carrying out an analysis of the results of IF THEN ELSE calculations using Forward Chaining.

Optimized MAC Protocol Using Fuzzy-Based Framework for Cognitive Radio AdHoc Networks

Cognitive radio networks (CRNs) have been widely used in various applications for effective radio spectrum utilization in recent years. It is essential to fend off the growing demand for this finite natural resource for next-generation communications. In CRNs, detecting the activity of the primary user requires opportunistic spectrum sensing for efficient usage of the available radio spectrum, which is a limited and exquisite resource. Thus, CRNs are the key component in solving the spectrum scarcity issue in the presence of primary user bands through secondary users. Cognitive radio AdHoc networks (CRAHNs) are a unique kind of CRNs where infrastructures less cognitive radio (CR) nodes are furnished. In CRAHN, the CR-MAC protocol works slightly differently from the traditional wireless network MAC protocols. Particularly, the proposed method includes a high traffic scenario under contention-based IEEE 802.11 DCF MAC protocol. Accordingly, it can be observed that both, throughput and delay, increase as the CW size and packet length of the 802.11 (DCF) MAC protocol for CRAHN varies. Therefore, this paper proposed a fuzzy-based optimization framework for the 802.11 (DCF) MAC protocol in CRAHNs. Furthermore, it optimized throughput and delay by training a database of input parameters, contention window, and packet length for the Mamdani and Sugeno fuzzy inference system (FIS) models of the 802.11 (DCF) protocol simultaneously. The experimental result of the proposed framework for CRAHN with FIS shows that altering the contention window increases throughput by 25% and reduces the delay by 38% compared to the IEEE802.11 (DCF) protocol for CRAHNs without FIS. Moreover, it is also revealed that the throughput is increased by and 7% and the delay is reduced by 40% to 50% due to altering the packet length.

Design of red chili commodity pricing using the BPMN approach and Sugeno's fuzzy inference system

Red chili is Indonesia's leading commodity. Red chili is a raw material for various food products, cosmetics, pharmaceuticals and others. Fluctuations in the availability of red chili commodity supply affect the price of red chili commodity products. Pricing can occur because of supply and demand. Un­cer­tain conditions also influence pricing due to fluctuations in raw material prices, ultimately affecting the price of carrageenan products. This condition makes price determination very difficult. Therefore, this study aims to analyze and design a pricing mechanism and determine the optimal margin in the red chili commodity marketing system. This study uses a systems analysis and design approach. Input-process-output (IPO) diagrams describe system requirements. Industrial business processes are described by the Business Process Model and Notation (BPMN) ver. 16.0. Meanwhile, to determine the optimal margin, Sugeno's fuzzy inference system approach is used by simulating the model in 3 margin scenarios: pessimistic, moderate, and optimistic. The simulation results were tested using the MAPE test, in which the results were compared between fuzzy price results and markup prices using markup values of 20%, 25%, and 30%. The analysis results show that the price is determined by demand and supply. The price obtained from the formulation of the Sugeno fuzzy model shows an optimal margin of Rp. 16,600.

AI-TASFIS: An Approach to Secure Vehicle-to-Vehicle Communication

ABSTRACT VANET provides communication between vehicles. VANET nodes are highly dynamic. Therefore, it is essential to increase the stability of the communication between nodes. The cluster head and cluster node provide stable communication between vehicles. Vehicle communications are being challenged by factors such as security, confidential communication, and severe delay. This work proposes an Artificial Intelligence (AI)-based Sugeno fuzzy inference system to overcome these issues. The proposed secure trust-based cluster techniques are less complex, with lesser communication delays, lower overhead, and more efficient in accurately locating trusted nodes for communication. Vehicular Ad hoc Networks (VANET) should send data between vehicles and use traffic safety indicators using an Enhanced Cluster-based routing protocol. AI-TASFIS is Artificial Intelligence-based Trust Authentication Sugeno fuzzy inference system that uses ANFIS-based Sugeno Fuzzy inference systems to calculate the node weights for choosing trusted cluster head and cluster member that reduces malicious attacks like Black Hole Attacks, Wormhole attacks, and Timing Attacks while transferring data packets. Simulation results show that the proposed Artificial Intelligence (AI)-based Sugeno fuzzy inference system provides network security, reduces end-to-end delay, and increases packet delivery ratio and throughput.

Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion

Collision-free navigation of mobile robots is a challenging task, especially in unknown environments, and various studies have been carried out in this regard. However, the previous studies have shortcomings, such as low performance in cluttered and unknown environments, high computational costs, and multiple controller models for navigation. This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) and global positioning system (GPS) for control and navigation to overcome these problems. The proposed method automates the navigation of a mobile robot while averting obstacles in unknown and densely cluttered environments. Furthermore, the mobile robots’ global path planning and steering are controlled using GPS and heading sensor data fusion to achieve the target coordinates. A fuzzy inference system (FIS) is adopted to model obstacle avoidance where distance sensors data is converted into fuzzy linguistics. Moreover, a type-1 Takagi–Sugeno FIS is used to train a five-layered neural network for the local planning of the robot, and ANFIS parameters are tuned using a hybrid learning method. In addition, an algorithm is designed to generate a dataset for testing and training the ANFIS controller. All the testing and training are conducted in MATLAB, while simulations are carried out using CoppeliaSim. Comprehensive experiments are performed to validate the robustness of the proposed method. The results of the experiments show that the proposed approach outperforms various state-of-the-art neuro-fuzzy, CS-ANFIS, multi-ANFIS, and hybrid ANFIS navigation and obstacle avoidance methods in finding a near-optimal path in unknown environments.

Change in Normal Health Condition Due to COVID-19 Infection: Analysis by ANFIS Technique.

The COVID-19 pandemic has crippled the world population. Our present work aims to formulate a model to analyze the change in normal health conditions due to COVID-19 infection. For this purpose, we have collected data of seven parameters, namely, age, systolic pressure (SP), diastolic paper (DP), respiratory distress (RD), fasting blood sugar (FBS), cholesterol (CHL), and insomnia (INS) of 156 persons of Birnagar municipality, Nadia, India; before and after COVID-19 infection. Ultimately, using an adaptive neuro-fuzzy inference system (ANFIS), we have formulated our desired model, a Takagi–Sugeno fuzzy inference system. Further, with the help of this model, we have established one’s change in health condition with age due to COVID-19 infection. Finally, we have derived that older people are more affected by COVID-19 infection than younger people.

The Use of the Fuzzy Inference System in Determining the Quality of Goatskin as Raw Material for the Leather Industry

Leather products are affected by the quality of the raw hide, the pre-treatment, and the tanning process. Furthermore, some leather products use quality goatskins with the right determination as raw materials. This study aims to determine the quality of goatskin through the use of Fuzzy Logic science. It was conducted using the Sugeno Fuzzy Inference System (FIS), while data analysis was carried out through the Matlab R2018a program. The criteria for using this method were the factors that affected the quality of raw goatskin such as thickness, length of the backline, skin area, number of scratches on the skin, number of holes, presence of boils, lice bumps, and hair loss. The results of the analysis showed that Sugeno FIS could be used to help determine the quality of goatskin with a good accuracy level with a MAPE value of 18%. Furthermore, the FIS method could be used as an appropriate modeling system for determining the quality of goatskin as a raw material for the leather tanning industry.

Модель нейронной сети для прогнозирования предэвакуационного поведения людей при пожаре

This article discusses a comprehensive study of pre-evacuation behavior of people in the event of an emergency. In this regard, it is advisable to use machine learning approaches, in particular neural networks, for data mining in the field of security. Statistical data obtained in emergency situations may be limited and generally uncertain, for this reason it is recommended to choose a neural network architecture - adaptive fuzzy inference Network System (ANFIS) based on the Takagi–Sugeno fuzzy inference system. The neural network architecture considered in the article in the form of an adaptive fuzzy inference network system architecture consists of five layers, where each layer performs a well-defined function. Data on the behavioral reaction of people before the evacuation to train an artificial neural network were obtained using such approaches as interviewing, questionnaire, survey.

Development of Fuzzy-Based Smart Drip Irrigation System for Chili Cultivation

Chili plants often fail to harvest in the cultivation process due to improper irrigation. Soil temperature and humidity are essential parameters that affect the amount of water needed by plants in the watering process. This research aimed to apply fuzzy logic to the chili plants' irrigation system. The function of this system was to regulate watering due to the needs of the Chili plant automatically in a real-time fashion. The Sugeno fuzzy inference system (FIS) is embedded in a microcontroller to regulate the water based on the plant's needs appropriately. The system was tested on Chili plants located in the iSurf Computer Science Lab IPB University greenhouse. After four days of testing, the soil moisture sensor results were stable at optimal conditions, between 60%-80% after watering. It shows that the irrigation system has automatically regulated watering due to the Chili plant's needs.

Prediction of bone microstructures degradation during osteoporosis with fuzzy cellular automata algorithm

A novel fuzzy cellular automata is proposed to simulate bone degradation during osteoporosis. The initial three-dimensional (3D) bone microstructure is obtained from computed tomography (CT) images. Cellular automata algorithm is implemented to the 3D lattice and a Sugeno Fuzzy inference system is designed with nine sets of fuzzy rules to simulate the degradation process. A distance vector parameter is defined to describe the number of neighborhood cells that each cell can have a connection with. It is shown that by increasing the value of this distance vector, the results converge toward a quasi-constant degraded microstructure. The obtained microstructure is considered to be the final result and compared to prediction of bone degradation of the literature based on phase exchange calculated from mechanical strain energy. It is shown that the fuzzy cellular automata model predicts a more realistic bone degradation and microstructure distribution than the phase exchange method while having a model significantly simpler.