Memristive fuzzy edge detector

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

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.

In this paper, first we present a new explanation for the relation between\nlogical circuits and artificial neural networks, logical circuits and fuzzy\nlogic, and artificial neural networks and fuzzy inference systems. Then, based\non these results, we propose a new neuro-fuzzy computing system which can\neffectively be implemented on the memristor-crossbar structure. One important\nfeature of the proposed system is that its hardware can directly be trained\nusing the Hebbian learning rule and without the need to any optimization. The\nsystem also has a very good capability to deal with huge number of input-out\ntraining data without facing problems like overtraining.\n

The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress and avoids isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemistry, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently, which have a great relationship with semantics, vary widely, and have linguistic information. This negatively affects early diagnosis decision-making. Depending on the circumstances, the diagnosis is made solo on imaging and some medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer’s disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN respectively.

Seismic design codes define response spectra with crisp numerical classifications of seismic parameters, which mainly affect the spectrum's shape and determination of seismic design loads. The efficiency of structural safety and construction costs depends on the optimum design and accurately determined seismic forces. As presented in the seismic design codes, several parameters are utilized to calculate the seismic design forces with response spectra. This study proposes a rule-based fuzzy inference system (FIS) model with fuzzy set numbers to determine the relevant parameters. By defining the soil profile thickness and shear wave velocity as inputs, the model generates the spectrum characteristic periods specified in the Türkiye Building Earthquake Code (TBEC 2007). The response spectra of twenty different samples with the FIS and crisp models were generated and compared to assess the model's superiority. Unlike crisp seismic code classifications, the proposed FIS model accounts for imperfections in soil group selection and topmost soil layer thickness, offering a more realistic representation of uncertainties and proving to be an effective tool for addressing linguistic vagueness in seismic response spectra analysis. The comparison between fuzzy and crisp output seismic parameters revealed significant differences in response spectra shape and spectrum intensity values. The FIS model-generated spectra were more conservative in certain building locations, while in others, they provided similar or lower values, suggesting potential cost savings in design. The FIS model demonstrates its efficacy in producing more accurate and robust designs by considering the uncertainties inherent in the problem. Furthermore, this approach has the potential to be extended to study seismic parameters of other design codes, although further research is required to comprehensively explore its capabilities and limitations.

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

One of the most important physical properties of the soil is its mechanical strength. Increasing soil mechanical strength will lead to increments in the draft, fuel consumption, work duration and equipment wear, but will follow the reduction in the root growth. Awareness of the soil cone index (as a criterion of the arable soil compaction) in order to production management in connection with the soil physical properties have high importance, especially in precision farming. On the other hand, finding the methods and models that be able to create the best function or model to estimate the soil cone index at the least cost and use of available data are crucial for researchers. The aim of this study was predicting arable soils cone index values by effective parameters on the soil cone index, including bulk density, soil moisture content and soil electrical conductivity by using Fuzzy and neuro-fuzzy systems. In this study, for measurement and determination of the influencing factors on the soil cone index value, the experimental design was the factorial experiment based on a randomized complete block design with five replications. The experimental field had three types of loam, sandy loam and loamy sand soils. The modeling of soil cone index was performed by using effective parameters such as bulk density, moisture content and soil electrical conductivity in the fuzzy and adaptive neuro-fuzzy inference system (ANFIS) systems. For fuzzification of input and output parameters, the linguistic variables, including very low (VL), low (L), medium (M), high (H) and very high (VH) were used. Since determining the type and number of membership functions was conducted experimentally, the triangular membership function for both input and output variables was used due to the high accuracy and convenience in system design. In ANFIS model, 80% and 20% of total data were considered as training and test data, respectively. The numbers of membership functions were selected 5 for each input parameters. ANFIS training was done by the hybrid method. The average of coefficients of determination (R2) were obtained 80.1% and 97.9% for the fuzzy and ANFIS models, respectively. The obtained model through ANFIS presented the high accuracy of 2.54% rather than the fuzzy models with the accuracy of 9.68%. Also in comparison with regression models, ANFIS model has high accuracy and can be used to estimate the soil cone index in agricultural land.

Fuzzy Inference System (FIS) is used for pattern recognition and classification purposes in many fields such as emotion recognition. However, the performance of FIS is highly dependent on the radius of clusters which has a very important role for its recognition accuracy. Although many researcher initialize this parameter randomly which does not grantee the best performance of their systems. The purpose of this paper is to optimize FIS parameters in order to construct a high efficient system for speech emotion recognition. Therefore, an optimization algorithm based on particle swarm optimization technique is proposed for finding the best parameters of FIS classifier. In order to evaluate the proposed system it was tested using two emotional speech databases; Fujitsu and Berlin database. The simulation results show that the optimized system has high recognition accuracy for both languages with 97 % recognition accuracy for Japanese and 80 % for German database.

Fuzzy inference system (FIS) is one of the most powerful inference systems that is widely used in the field of classification. Indeed, in this approach, FIS is engaged to create a mapping from features (inputs) onto classes (outputs) using fuzzy set theory. So far, many efforts have been made to improve classification accuracy performed by FIS. Generally, these efforts have been put in the following areas: efficient fuzzy rule generation, fuzzy membership function tuning, fuzzy rule weight tuning, feature selection for the antecedent part of fuzzy rules, and so on. In this paper, we consider this issue and propose a method based on mutual information for applying the impact factor of input parameters on the fuzzy inference process for improving the accuracy of fuzzy classification. Finally, we test our proposed method for boosting classification on six different problems using manual and auto-generated FIS. The method provided promising classification results confirming its correctness.

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.

In order to improve the pressure tracking response of an artificial ventilator system, a novel proportional integral derivative (PID) controller is designed in the present work by utilizing an optimal rule-based fuzzy inference system (FIS) with a reshaped class-topper optimization algorithm (RCTO), which is named as (Fuzzy-PID). Firstly, a patient-hose blower-driven artificial ventilator model is considered, and the transfer function model is established. The ventilator is assumed to operate in pressure control mode. Then, a fuzzy-PID control structure is formulated such that the error and change in error between the desired airway pressure and actual airway pressure of the ventilator are set as inputs to the FIS. The gains of the PID controller (proportional gain, derivative gain, and integral gain) are set as outputs of the FIS. A reshaped class topper optimization algorithm (RCTO) is developed to optimize rules of the FIS to establish optimal coordination among the input and output variables of the FIS. Finally, the optimized Fuzzy-PID controller is examined for the ventilator under different scenarios such as parametric uncertainties, external disturbances, sensor noise, and a time-varying breathing pattern. In addition, the stability analysis of the system is carried out using the Nyquist stability method, and the sensitivity of the optimal Fuzzy-PID is examined for different blower parameters. The simulation results showed satisfactory results in terms of peak time, overshoot, and settling time for all cases, which were also compared with existing results. It is observed in the simulation results that the overshoot in the pressure profile is improved by 16% with the proposed optimal rule based fuzzy-PID as compared with randomly selected rules for the system. Settling time and peak time are also improved 60–80% compared to the existing method. The control signal generated by the proposed controller is also improved in magnitude by 80–90% compared to the existing method. With a lower magnitude, the control signal can also avoid actuator saturation problems.

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.

Edge detection is a very important area in the field of Computer Vision. Edge detectors behave very poorly, their behavior may fall within tolerance in specific situations and have difficulty in adapting to different situations. Human vision is inherently a multiscale phenomenon and is sensitive to orientation and elongation. This work proposes the hardware implementation of efficient fuzzy logic based algorithm, which is used to detect the edges of an image without determining the threshold value. Edge detection in software is not suited for strong real-time applications. This problem is resolved by using hardware implementation on field programmable gate arrays (FPGAs). Fuzzy inference system is developed with four input pixel containing two fuzzy sets (FSs) one for white and another for black and one output pixel containing three FSs for white, black and edge. Fuzzy if-then rules are used to modify the membership functions. Finally, Mamdanidefuzzifier method is used to form the final edge image. For comparison, the same work was implemented using sobel operator. The hardware part is developed by using Verilog language. The FPGA implementation is targeted on Virtex5 Starter kit (xc5vlx50tff1136-1) and Virtex7 starter kit (xc7vx485tffq1157-1) using the updated Xilinx PlanAhead within the ISE 13.4 development suite. The edge thickness can be changed easily by adding new rules or changing output parameters. That is, rule-based approach has flexible structure that can be easily adapted to any time or anywhere and the new fuzzy approach produces better result than sobel operator. Experimental results show the ability and high performance of the proposed algorithm.

Recently, there has been increased use of fuzzy logic in control applications. The kernel of a fuzzy logic controller (FLC) is a fuzzy inference engine which includes the knowledge base, the decision making logic, and the defuzzification unit, as shown in Figure 1. For most of the reported applications, fuzzy inference algorithms of the FLCs were implemented in software and executed on a standard Von Neumann type processor or mucrocontroller. Although software-based FLCs are in general more economical and flexible, they often have difficulty in dealing with control systems that require very high processing and I/O handling speeds. For this reason, in the recent years, a number of hardware fuzzy inference systems have been reported or proposed [1–5, 7, 9–16] that reflect the diversity in fuzzy inference methods in technoloies related to hardware design. In general, analog hardware is relatively simple but lacks accuracy and reliability; on the other hand, digital hardware is accurate and reliable but is more complex and lacks speed if an iterative algorithm is unavoidable. In recent years, rapid advances in digital technology allow system designers to design custom computing machines based on a variety of technologies: full custom application-specific IC (ASIC), standard-cell, gate array, FPGA, programmable logic device (PLD), standard IC, etc.. Among these, FPGA is especially suitable for fast implementation and quick hardware verification. Besides technology alternatives, design decisions have to be made based on tradeoffs on performance, hardware resource anf flexibility. In general, higher performance can be achieved at the expense of more hardware resource and less flexibility.

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.