Identification of incompletely specified fuzzy unate logic function

The paper presents a novel, transistor level, implementation of selected fuzzy set operators suitable for fuzzy control systems realized in low-power hardware. We propose a fully digital, asynchronous realization of basic fuzzy logic (FL) functions, such as the bounded sum, bounded difference, bounded product, bounded complement, fuzzy logic union (MAX) and fuzzy logic intersection (MIN). All of the proposed operators has been implemented in the CMOS TSMC 180nm Technology and verified by means of transistor level simulations in Hspice environment. The proposed structures of the FL functions can easily be scaled to any signal resolutions.

This paper proposes a new class of functions, i.e., fuzzy majority functions, based on the fuzzy logic which takes any value in the interval [0, 1] as the truth value of its proposition. Fuzzy majority functions are characterized by weights and thresholds, and may be represented by a logic formula involving constants. Specifically, the proposed class intersects with the well‐known fuzzy logic functions but is not a subset of them. On the other hand, the logic formula of a fuzzy majority function has a close relation with binary threshold functions. From this viewpoint, a theoretical limit in the utility of the fuzzy majority functions is discussed.

Normal forms for fuzzy logic functions and their approximation ability

We discuss extensions to a controlled natural language allowing spatiotemporal expressions to be interpreted as fuzzy logic functions. The extensions first required new sentence templates. Next, changes to a GPSG parser modified its lexicon, and then extended its parsing and logical form rules to allow user-defined spatial and temporal constraints to be extracted. The sentence templates ground user-defined culturally-specific times and places to boundaries surrounding prototypical ideals. Query points, defined by location and time, are compared to these definitions using Gaussians centered at prototypical 'ideal' times or places. The Gaussians provide soft fall-off at the boundaries. Fuzzy logic operators allow larger expressions to be interpreted, analogous to Boolean combinations of terms. The mathematically-interpreted spatiotemporal terms act as domain features for a machine learning algorithm. They allow easy specification (compared to programming) of basis functions for an inverse reinforcement learning algorithm that detects anomalous vehicle tracks or suspicious agent behavior.

Improving feeding strategies for shrimp farming using fuzzy logic, based on water quality parameters

Copyright c 2013 Omar Salazar, Jairo Soriano, and Humberto Serrano. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract It is known that any binary logic function (in binary logic) can be rep- resented by at least one logic formula, however this is not always true for a ternary logic function (in ternary logic). A ternary logic function can be represented by at least one logic formula if and only if it satises a mathematical condition which is called regularity. Also, it is known that a fuzzy logic formula (in standard fuzzy logic) can be obtained by means of truth tables in ternary logic. This paper presents a method destined for extending a binary logic function into one (or more) ter- nary logic function(s) representable by at least one logic formula, and therefore, to obtain fuzzy logic formulas from a binary logic function. Mathematics Subject Classication: 03B52, 06D72, 08A72, 94D05

This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.

This paper presents the way of implementation of fuzzy set operators at the transistor level. We propose a novel realization of basic fuzzy logic (FL) functions, such as bounded sum, bounded difference, bounded product, bounded complement, fuzzy logic union (MAX) and fuzzy logic intersection (MIN). The proposed structures of the operators may be easily connected in chains, creating larger FL systems.

The processes’ modeling is an important aspect in the industry since it allows to obtain high productivity with energy and material savings. The hybrid process electrochemical discharge machining (ECDM) is subject to uncertainty and inaccuracy levels in the parameters, so a viable option to model this process is through soft computing techniques such as fuzzy logic and artificial neural networks. In this work, we present three models using fuzzy logic, backpropagation network, and radial basis function network for the prediction of the material removal rate (MRR). The gap voltage (Vg), peak current (Ip), and frequency (f) were taken as input parameters. A 3-factor full factorial design was developed with 2 levels (23), two replicas, and four central points. The model with the higher accuracy according to experimental result was radial basis function artificial neural network with 97.25% of accuracy.

Introduction: With regard to increasing population of country, need to high agricultural production is essential. The most suitable method for this issue is high production per area unit. Preparation much food and other environmental resources with conservation of biotic resources for futures will be possible only with optimum exploitation of soil. Among effective factors for the most production balanced addition of fertilizers increases production of crops higher than the others. With attention to this topic, determination of soil fertility degree is essential tobetter use of fertilizers and right exploitation of soils. Using fuzzy logic and Analytic Hierarchy Process (AHP) could be useful in accurate determination of soil fertility degree. Materials and Methods: The study area (at the east of Rasht city) is located between 49° 31' to 49° 45' E longitude and 37° 7' to 37° 27' N latitude in north of Guilan Province, northern Iran, in the southern coast of the Caspian sea. 117 soil samples were derived from0-30 cm depth in the study area. Air-dried soil samples were crushed and passed through a 2mm sieve. Available phosphorus, potassium and organic carbon were determined by sodium bicarbonate, normal ammonium acetate and corrected walkly-black method, respectively. In the first stage, the interpolation of data was done by kriging method in GIS context. Then S-shape membership function was defined for each parameter and prepared fuzzy map. After determination of membership function weight parameters maps were determined using AHP technique and finally soil fertility map was prepared with overlaying of weighted fuzzy maps. Relative variance and correlation coefficient criteria used tocontrol groups separation accuracy in fuzzy fertility map. Results and Discussion: With regard to minimum amounts of parameters looks some lands of study area had fertility difficulty. Therefore, soil fertility map of study area distinct these lands and present soil fertility groups for better management of soil and plant nutrition. Weight of soil parameters was0.54, 0.29 and 0.17 for organic carbon, available phosphor and potassium, respectively. Fuzzy map of study area includes five soil fertility groups as: 22.9% very high fertility, 27.7% high fertility, 35.53% medium fertility, 10.48% low fertility and 3.39% very low fertility. Consequently, a separated map for soil fertility prepared to evaluate soil fertility of study area for rice cultivation. Toinvestigatethe efficiency of fuzzy model and AHP in increasing the accuracy of soil fertility map, soil fertility map with Boolean method prepared as well. Boolean map showed 58.88% fertile and 41.12% unfertile.15 soil samples from different soil fertility groups of study area were derived fromcontrol of maps accuracy. 13 renewed samples of 15 and 9 soil samples have matched with fuzzy and Boolean map, respectively. Comparison of parameters mean in fuzzy map fertility groups showed that parameters mean amounts of very high and high fertility groups are higher than optimum level except potassium that is a few lower than optimum level in high fertility group, therefore, addition of fertilizers in these groups could not be useful to increase rice crop production. Phosphorus parameter amount is lower than the critical level in very low, low and medium fertility groups, then in these groups phosphorus fertilizer should be added to the soil toincreaserice production. The amount of potassium parameter is higher than the critical level and lower than optimum limit in very low, low, medium and high fertility groups, then in these groups addition of potassium fertilizer will results in theincrease of production. Organic carbon amount is lower than optimum level in very low and low fertility groups. With regard to the relation between organic carbon andnitrogen and phosphorus, therefore, the addition of organic carbon fertilizer could compensate deficit of nitrogen and phosphorus in these groups as well. Attention to the presented explanations and comparison of fuzzy and Boolean maps using parameters amounts in renewed sampling points for control of maps accuracy, it is distinct that fuzzy logic could influencetheoptimum using of fertilizers with increasing map efficiency and accuracy. In addition, relative variance and correlation coefficient amounts showed that fuzzy map has separatedquite wellparameters changes. Conclusion: Effective parameters in soil fertility, includingorganic carbon, phosphorus and potassium were used topreparesoil fertility map for rice cultivation. With regard to the minimum amounts of parameters looks some lands of study area had fertility difficulty. Therefore, soil fertility map of study area distinct these lands and presents soil fertility groups tobetter management of soil and plant nutrition. Fuzzy and Boolean methods were used topreparesoil fertility map. Comparison of these two approaches showed that fuzzy method with AHP caused to increase theefficiency and accuracy of fertility map for rice. Separated and distinguish soil fertility groups in fuzzy map help suitable distribution and optimum use of fertilizers for rice production.

The aim of this project was to propose an original approach c to predetermine the topology of a surface from the point cloud obtained during a digitizing process. This method has been applied for the predefinition of the different main regions of a surface, in order to be able to divide the point cloud in different separate clouds corresponding to these regions. It should be also possible to optimize locally the surface when taking into account tangency and curvature conditions. The main advantage of this method is that, for a given accuracy, the complexity of the surface is lower than a traditional method, an the computations are quite the same than those obtained when using traditional surface modeling algorithms. This method allows a qualitative decomposition of the surface defined from a function z equals f(x,y). In the paper, the fuzzification and defuzzification process and a study of the influence of the main parameters will also be presented. Some fuzzy logic algorithms and functions have been compared and analyzed when applied on the example.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In practice, gasoline engine torque is improved using hydrogen-hydrogen-oxygen gas (HHO) supplementation, which adheres to the fixed HHO flow rate method. However, the arising hypothesis reveals that the fixed HHO flow rate method does not optimally improve an engine’s air-fuel ratio (AFR) for various loads, so engine torque is not optimal. The research aims to propose a model of an adaptive HHO controller, which manages the electric current of the HHO generator so that the produced HHO flow rate adapts to engine speed and load variations. Hence, the AFR is optimal for any engine operation. The Matlab-Simulink-based simulation integrates the models of an adaptive HHO controller, a 1500cc gasoline engine coupled with an HHO generator, and a proportional-integral (PI) engine speed controller. Hence, the research can investigate the improvement of engine torque and AFR error against the commanded AFR due to an adaptive HHO flow rate method. The adaptive HHO controller is designed based on fuzzy logic and polynomial function controls involving real-time engine data, such as AFR, mass airflow (MAF), and the commanded AFR issued by the engine control unit (ECU). The research also compares the results due to the fixed and adaptive HHO flow rates. The results show that the proposed adaptive HHO controller consistently improves the AFR error against the commanded AFR by 100%, so the AFR always meets the commanded AFR for various loads at the commanded AFR = 12.6, 14.7, and 15.4. On the contrary, with the same engine operation, the fixed HHO flow rate provides inferior and uneven AFR error improvement (0%-100%), depending on load and the commanded AFR. As an implication, the adaptive HHO controller improves engine torque by 3.06%-53.6%, better than the fixed HHO flow rate method, improving by 0%-31.42%.

In this paper, an overview of several strategies for fault ride-through (FRT) capability improvement of a doubly-fed induction generator (DFIG)-based wind turbine is presented. Uncertainties and parameter variations have adverse effects on the performance of these strategies. It is desirable to use a control method that is robust to such disturbances. Auto disturbance rejection control (ADRC) is one of the most common methods for eliminating the effects of disturbances. To improve the performance of the conventional ADRC, a modified ADRC is introduced that is more robust to disturbances and offers better responses. The non-derivability of the fal function used in the conventional ADRC degrades its efficiency, so the modified ADRC uses alternative functions that are derivable at all points, i.e., the odd trigonometric and hyperbolic functions (arcsinh, arctan, and tanh). To improve the efficiency of the proposed ADRC, fuzzy logic and fractional-order functions are used simultaneously. In fuzzy fractional-order ADRC (FFOADRC), all disturbances are evaluated using a nonlinear fractional-order extended state observer (NFESO). The performance of the suggested structure is investigated in MATLAB/Simulink. The simulation results show that during disturbances such as network voltage sag/swell, using the modified ADRCs leads to smaller fluctuations in stator flux amplitude and DC-link voltage, lower variations in DFIG velocity, and lower total harmonic distortion (THD) of the stator current. This demonstrates the superiority over conventional ADRC and a proportional-integral (PI) controller. Also, by changing the crowbar resistance and using the modified ADRCs, the peak values of the waveforms (torque and currents) can be controlled at the moment of fault occurrence with no significant distortion.

High speed data transmission over communication channels distort the transmitted signals in both amplitude and phase due to presence of inter symbol interference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the effects of such ISIs and attempts to recover the transmitted symbols. The multilayer perceptron (MLP), fuzzy logic (FL) and radial basis function (RBF) based equalizers are relatively new soft computing based equalizers which aim to minimize the ISI present in the channels particularly for nonlinear channels. However they suffer from long training time and undesirable local minima. In the present paper we propose a new adaptive channel equalizer using a novel bacterial foraging optimization (BFO) technique which is essentially a derivative free optimization tool. This algorithm has been suitably used to update the weights of the equalizer. The performance of the proposed equalizer has been evaluated and has been compared with its LMS based counter part. It is observed that the new equalizer offers improved performance both in terms of rate of convergence as well as bit-error-rate

Liver tumour segmentation from abdominal CT image is a challenging task in biomedical image processing. Complex segmentation methods may provide good segmentation results but are difficult for implementing in clinical environment. Hence, in this paper, we propose a simple thresholding technique based on fuzzy logic and Shannon's entropy function to segment the tumour from liver CT images. Tumour dimensions are then measured subsequently. The proposed method provides better results than the classical segmentation methods.