Efficient transistor level implementation of selected fuzzy logic operators used in control systems

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.

Fuzzy logic function f is unate if and only if f is either monotone increasing or monotone decreasing with regards to every variable. This paper studies some fundamental properties of fuzzy unate logic function. Based on the properties, this paper clarifies a necessary and sufficient condition of any given incompletely specified function to be unate fuzzy logic function.

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.

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.

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.

Normal forms for fuzzy logic functions and their approximation ability

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

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

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.

This study aims to develop a variable-order fuzzy fractional proportional-integral-differential (VOFFPID) control system for controlling the mover position of a newly designed voice coil motors- (VCMs-) driven dual-axis positioning stage. First, the operation principle and dynamics of the stage are analyzed. After that, the design of a fuzzy fractional proportional-integral-differential (FFPID) control system is introduced on the basis of a fractional calculus and fuzzy logic system. With an additional degree of freedom to the control parameters and fuzzy operation, the FFPID control system can upgrade the contour tracking performance of a conventional proportional-integral-differential (PID) control system with respect to the specified dynamics of the stage. Moreover, the VOFFPID control system is designed to further improve the tracking responses of the FFPID control system. In this system, the five control parameters are optimized with the cuckoo search algorithm via an adaptive strategy. Lastly, nominal and payload conditions attributed to two nonlinear contour demands are provided to evaluate the contouring performance of the PID, FFPID, and VOFFPID control systems. The experimental results subjected to different performance measures demonstrate that the proposed VOFFPID controller outperforms PID and FFPID controllers in terms of the designed VCMs-driven dual-axis positioning stage under both conditions.

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.

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.

Multi-sensor fusion is perfect in reliable estimation of the environment data through spreader sensor network. This paper has explored the sensor data fusion with emerging technique governed through the technique of clustering, maximum like hood technique along with the fuzzy logic and fitness function, and maximum potential. The investigation focus on the probabilistic analysis of each gathered data set and finding the best frequent item sets with maximum like hood function. This technique has pre-assumption that negligible loss in clustering while only loss in master node with sink node or base station node has been accountable for the investigation. KeywordsMulti-sensorRecognitionProbabilistic analysisMaximum potentialFitness functionFuzzy logicKernel based approach

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.