Fuzzy entropy DEMATEL inference system for accurate and efficient cardiovascular disease diagnosis

A fuzzy inference- fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases

China started Sponge City Programme (SCP) to manage urban floods and to improve the quality of water environment in 2015. So far, the first two batches of pilot cities of SCP have been finished construction and two more batches of demonstration cities are ongoing. How to transfer the knowledge which learnt by the pilot cities to other cities is a key problem to enhance both the efficiency and the performance of SCP. This paper introduces the first step to develop a fuzzy system to represent knowledge which is acquired from stakeholders and decision makers, then to transform into if-then rules. This fuzzy logic inference system aims to simulate scenarios of real-life decision making. How to rate the performance of SCP in one city is chosen as an example in this paper. A Mamdani fuzzy inference system is applied, the fuzzy variables and fuzzy rules are hypothetical based on surveys of Qian’an, one of the pilot cities of SCP. Data from Qian’an is used to test and results show that the performance of SCP in Qian’an is good (74.1). The Mamdani fuzzy system is proved to be a powerful tool to represent knowledge and in the future other aspects of knowledge from SCP will be applied.

Greenhouse cultivation is an important technique for producing high-quality crops with a high profit margin. Fuzzy inference systems have been effectively utilized in prediction and control models to enable good management of environmental factors. The aim of this review is to determine the different relationships in the fuzzy inference systems currently used for modelling, prediction and humidity control in greenhouses and their evolution over time in order to develop more robust and reliable models easy to understand. The main objective is to identify the different relationships within fuzzy inference systems their configurations and models through optimization algorithms currently used for prediction, control and humidity modelling in greenhouses. The methodology follows the PRISMA working guide. A total of 93 surveys were reviewed in 4 academic databases; its bibliometric aspects have been extracted and analysed, which contributes to the objective of the survey. Finally, it was determined that using Mamdani's fuzzy inference systems in conjunction with optimization and fuzzy clustering approaches, as well as strategies like model-based predictive control, ensures high levels of accuracy and interpretability.

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.

The fuzzy logic inference system was developed to identify driving intention. The membership functions and rules of the fuzzy logic inference system were built by using mathematical statistics and neural network. The vehicle model was built based on a series–parallel hybrid vehicle using Cruise software. The driving intention inference system was designed in Simulink. The simulation is done based on Cruise and Simulink. The simulation results prove that the fuzzy inference system can identify driving intentions excellently and the control strategy based on driving intentions can help to reduce more fuel consumption.KeywordsDriving intentionFuzzy inferenceHybrid vehiclesNeural networkSimulation

PurposeLimited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.Design/methodology/approachA Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.FindingsResults of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.Originality/valueThis paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.

Interpretability improvements to find the balance interpretability-accuracy in fuzzy modeling: an overview.- Regaining comprehensibility of approximative fuzzy models via the use of linguistic hedges.- Identifying flexible structured premises for mining concise fuzzy knowledge.- A multiobjective genetic learning process for joint feature selection and granularity and contexts learning in fuzzy rule-based classification systems.- Extracting linguistic fuzzy models from numerical data-AFRELI algorithm.- Constrained optimization of fuzzy decision trees.- A new method for inducing a set of interpretable fuzzy partitions and fuzzy inference systems from data.- A Feature Ranking Algorithm for Fuzzy Modelling Problems.- Interpretability in multidimensional classification.- Interpretable semi-mechanistic fuzzy models by clustering, OLS and FIS model reduction.- Trade-off between approximation accuracy and complexity: TS controller design via HOSVD based complexity minimization.- Simplification and reduction of fuzzy rules.- Effect of rule representation in rule base reduction.- Singular value-based fuzzy reduction with relaxed normalization condition.- Interpretability, complexity, and modular structure of fuzzy systems.- Hierarchical genetic fuzzy systems: accuracy, interpretability and design autonomy.- About the trade-off between accuracy and interpretability of Takagi-Sugeno models in the context of nonlinear time series forecasting.- Accurate, transparent and compact fuzzy models by multi-objective evolutionary algorithms.- Transparent fuzzy systems in modeling and control.- Uniform fuzzy partitions with cardinal splines and wavelets: getting interpretable linguistic fuzzy models.- Relating the theory of partitions in MV-logic to the design of interpretable fuzzy systems.- A formal model of interpretability of linguistic variables.- Expressing relevance and interpretability of rule-based systems.- Conciseness of fuzzy models.- Exact trade-off between approximation accuracy and interpretability: solving the saturation problem for certain FRBSs.- Interpretability improvement of RBF-based neurofuzzy systems using regularized learning.- Extracting fuzzy classification rules from fuzzy clusters on the basis of separating hyperplanes.

Wireless Multimedia Sensor Networks (WMSNs) are resource constrained (compared to the requirement of video communication), where the sensor nodes have limited bandwidth, energy, processing power and memory. Resource mapping is required in such networks, which is based on user requirements to offer better communication services as well as to use optimal resources. This paper proposes Mamdani's fuzzy inference system (FIS) to map the user requirement to resource demands by considering video/image. The input fuzzy parameters used are: available node energy, available bandwidth, and user quality needs. Output fuzzy parameter is frames to be transmitted per second (fps). By using ‘fps’ and the resolution of an image, the sensor node computes bandwidth and buffer requirements. The various defuzzification methods: centroid, smallest of maximum, and mean of maximum are used to get the response from the fuzzy inference system, and later they are compared.

Type-2 fuzzy sets and systems can better handle uncertainties compared to its type-1 counterpart, and the widely applied Mamdani and TSK fuzzy inference approaches have been both extended to support interval type-2 fuzzy sets. Fuzzy interpolation enhances the conventional Mamdani and TKS fuzzy inference systems, which not only enables inferences when inputs are not covered by an incomplete or sparse rule base but also helps in system simplification for very complex problems. This paper extends the recently proposed fuzzy interpolation approach TSK+ to allow the utilization of interval type-2 TSK fuzzy rule bases. One illustrative case based on an example problem from the literature demonstrates the working of the proposed system, and the application on the cart centering problem reveals the power of the proposed system. The experimental investigation confirmed that the proposed approach is able to perform fuzzy inferences using either dense or sparse interval type-2 TSK rule bases with promising results generated.

In the condition of large alignment angles which brought about nonlinear problem in SINS, a precise SINS error model was established on the concept of Euler platform error angles. To reduce the computation of initial alignment in SINS, a Simplified UKF (SUKF) could be used since its state equation was nonlinear while the measurement equation was linear. A novel method combined Fuzzy Inference System (FIS) and system noise's online estimation together on the basis of SUKF was proposed to adjust the system noise covariance, and online improve the performance of the SINS initial alignment. In the SINS alignment for large misalignment angles simulation conditions, the SUKF via FIS showed higher accuracy, better stability and also better real-time performance compared with conventional UKF.

With the increasing need for flexibility and adaptivity in computerized systems, the application of fuzzy expert systems is becoming increasingly commonplace in today's industry. Fuzzy logic expert systems often improve performance by allowing knowledge to generalize without requiring the knowledge engineer to anticipate all possible situations. Thus, for many types of applications, soft such as Fuzzy logic can incur lower overhead in terms of representing and engineering task knowledge. My project investigates the application of fuzzy expert systems to motion tracking. Previous research showed that Fuzzy Logic can be used to track the motion of a brightly colored object against a dark background, with relatively low development and run-time costs. The system I am developing identifies, tracks, and predicts the motion of multiple objects using unique identification patterns against a dark background. My poster will describe the fuzzy inference systems for tracking and motion prediction of such objects. An essential step to obtaining the fuzzy inputs for the motion tracking fuzzy inference system is to use convolution correlation data to obtain the centers of mass of the objects. Image processing information from the region around the center of each object provides good fuzzy inputs for recognizing object patterns and determining orientation. I am investigating two fuzzy inference systems for motion tracking and prediction in order to identify their strengths and weaknesses. In the long run, I hope to exploit the strengths of both by dividing the task between the two approaches. The Adaptive Neuro Fuzzy Inference System (ANFIS) is effective for pattern recognition. Fuzzy Proportional Derivative (FPD) is useful for computing errors and tuning inferences. Cascading the two fuzzy systems allows the system first to acquire generalized patterns and then fine-tune them for error tolerance. To perform this work, I am using the Matlab fuzzy logic toolkit, which is a simple and flexible tool for designing fuzzy inference systems.

Interpolativity of at-least and at-most models of monotone fuzzy rule bases with multiple antecedent variables

A self-adapting fuzzy inference system for the evaluation of agricultural land

Conventional methods for determining AC skin impedance with a wide range of frequency process DC to 1 MHz resulting from the off-line estimation are accurate but lack real-time convenience. In the author's previous proposed methods, the direct measurement is faster than conventional methods because it uses a real-time, on-line measurement and computation, and requires only one high frequency input signal. However, the algorithm to specify the suitable updating value (the defuzzification output multiplied by a gain) in fuzzy inference system is not unique, and the selecting an appropriate updating method for the estimation process of AC skin impedance in fuzzy inference system is necessitated. In order to speed-up measurement, an advanced updating gain, which has a significant effect on the time-performance, is introduced in the real-time analysis of AC skin impedance based on a fuzzy inference system.

A fuzzy inference system (FIS) was developed to detect obstructive sleep apnea (OSA) by analyzing the respiratory airflow signal in adults. The parameters analyzed were the normalized area and the standard deviation of consecutive 3-second intervals of baseline adjusted and rectified airflow signal. Fuzzy logic was used to process these parameters to detect apnea and hypopnea when the output values were within a specified range extracted from OSA patient data. The FIS comprised of three major stages of computation: fuzzification, fuzzy rule evaluation and defuzzification. Seven males and two females with an average age of 48 years (range: 26 - 66 years), an average weight of 102 kg (range: 63 - 159 kg), an average height of 1.7 m (range: 1.5 - 1.8 m) and an average body mass index (BMI) of 33 kg/m/sup 2/ (range: 21 - 42 kg/m/sup 2/) participated in this study. Patients spent at least 8 hours in an accredited sleep laboratory. However, patient data was collected for only part of this time. The total amount of test time for all nine patients was 38.83 hours with an average of 4.31 hours/patient (range: 1.92 - 7.63 hours). The total number of apnea events occurring during this time was 808, and the number of hypopnea events was 694. The membership functions for the FIS were derived by analyzing apnea and hypopnea events in four patients. The data from all nine patients were used in algorithm performance evaluation. The apnea and hypopnea events were scored by a sleep specialist and were used to test the correct detection rate by the FIS. The results demonstrated that the FIS reached an overall correct detection rate of 83% across all patients. The false negative rate was 17% and the false positive rate was 12%. The correct detection rate varied from patient to patient and correct rates greater than 90% were achieved in three patients. This study suggests that fuzzy inference could provide an intelligent algorithm for control of a continuous positive airway pressure (CPAP) machine. It would detect apnea and hypopnea events and automatically adjust the pressure to eliminate them. The performance of the algorithm could be further optimized to give increased detection rates. This could be achieved by performing further studies on a larger OSA patient population and utilizing augmentative methods such as neural networks to better sense the fuzzy patterns in the OSA data.