Introduction Of Fuzzy Logic Research Articles

Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae

Enhancing biohydrogen production from microalgae is crucial in addressing environmental and energy challenges. It provides a sustainable, clean energy source while reducing greenhouse gas emissions. Moreover, it advances microalgae-based biotechnology, enabling innovative biofuel production and ecological revitalization. The main target of this study is to develop a robust ANFIS model to simulate the biohydrogen production process from microalgae within photobioreactors. The study focuses on enhancing hydrogen yield by optimizing three critical process parameters: sulfur concentration (%), run time (hours), and wet biomass concentration (g/L). Initially, an adaptive neuro-fuzzy inference system (ANFIS) model for biohydrogen production process is constructed based on empirical data. Subsequently, the red-tailed hawk algorithm (RTH) is used to determine the optimal values for the process parameters, corresponding to maximum hydrogen yield. The performance of ANFIS model in predicting hydrogen yield is assessed using root mean square error (RMSE) and coefficient-of-determination (R2) values. The obtained RMSE values for training and testing data are 2.8477 × 10−05 and 1.2807, respectively, while the corresponding R2 values are 1.0 and 0.9911 for training and testing. The introduction of fuzzy logic into the model significantly improves its predictive accuracy, as evidenced by the drop in RMSE from 10.79 with ANOVA to 0.7159 with ANFIS, representing a substantial 93.4 % decrease. The remarkable precision of the ANFIS model, indicated by its low RMSE and high R2 values, underscores the success of the modeling stage. The combination between ANFIS with the RTH technique yields impressive results, leading to a hydrogen yield enhancement of 6.87 % and 26.65 % when compared to both measured data and ANOVA.

Fuzzy Swarm Control Based on Sliding-Mode Strategy With Self-Organized Omnidirectional Mobile Robots System

This article proposes the fuzzy swarm control based on sliding-mode strategy with self-organized omnidirectional mobile robots (OMRs) system. In previous work, swarm control is realized by using sliding-mode strategy. However, chattering in the OMRs system is very serious. To overcome this drawback, a new fuzzy swarm control for OMRs system is designed, which introduces the fuzzy logic to replace switching control in traditional sliding-mode control (SMC). The fuzzy SMC (FSMC) can weaken chattering for sliding-mode strategy in the OMRs system. However, it is difficult to design swarm control law for OMRs because of model uncertainties, external disturbance and the introduction of fuzzy logic. The FSMC is proposed in this article and its stability is analyzed. Simulation and experiment results show that the fuzzy swarm control based on sliding-mode strategy with self-organized OMRs system can realize stability asymptotically and chattering is weakened.

User-Experience-Oriented Fuzzy Logic Controller for Adaptive Streaming

HyperText Transfer Protocol (HTTP) streaming has been widely used for multimedia delivery nowadays. To adapt to the heterogeneous networks and different terminals, the rate adaptation controller is developed as the core part of the video transmission system. In this paper, a three-input fuzzy controller is designed to enhance the users' quality-of-experience (QoE) on watching online video. The introduction of fuzzy logic is able to solve the problem that there is no rule to set the reasonable buffer thresholds in the buffer-based adaptation controller. The normalized throughput, the buffer level and the buffer variation are used as the inputs of the controller to minimize the inevitable mismatch caused by limited bitrate levels and to help the system converge to a stable state. The proposed controller is compared with three other controllers under three simulation network conditions and an actual vehicle condition. A QoE model is simultaneously used to get intuitive and comprehensive results. The results show that among all the four conditions, the proposed controller can provide better QoE than other algorithms.

Detection of Opinion

Opinion Mining is an area that has attracted many researchers, which resulted in several works. There are two types of approaches for detecting opinion: Approaches based on the corpus (Corpus-based Approach) and other approaches based on dictionary (Dictionary-based Approach). In this article, the authors will make a comparative study between the two approaches: the Bayesian method of Turney for the first approach; for the second approach, the authors will study the detection of opinion by the SentiWordNet and then the introduction of fuzzy logic is seen in this method. This study aims to study the theoretical and practical limitations of these two approaches.

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root mean square error (RMSE), absolute error mean (AEM) and scatter index (SI) between target and predicted sand fraction values. The achieved estimation accuracy may diverge minutely depending on geological characteristics of a particular study area. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.

Handling of Fuzzy Queries using Relational DBMS

Handling crisp and precise data in SQL is an easy process but classical data models often suffer from their incapability of representing and manipulating imprecise and uncertain information which is found in many real world applications. Since the early 1980’s, Zadeh’sfuzzy logic has been used to improve and modify various data models. This introduction of fuzzy logic in databases enhances the capability of classical models so that uncertain and imprecise information could easily be represented and manipulated.This paper proposes an algorithm with the help of which crisp values are converted into fuzzy values by calculating their membership value at the database level. The paper then uses a GUI through which the result of fuzzy queries can be obtained from the database. With the help of proposed algorithm, the calculated membership value will be stored in the database for differentpredefined categories (e.g.-child, young, middle age and old in case of ages). These membership values helps in fetching the result of fuzzy queries from the database with the help of developed GUI (the database used here is oracle 10g but other databases can also be used).The fuzzy queries have a wider retrieved space and can be used to identify the characteristic of an individual (marks in this case).

Harmonic Control Based on Fuzzy Logic

Proliferation of nonlinear loads in power systems has increased harmonic pollution and deteriorated power quality. Passive filtering has typically been the standard technology for harmonic and reactive power compensation .With the advancements in power electronics, active filtering is being more widely considered given its flexibility and precise control. However, cost, complexity, and reliability are considered the major drawbacks of active filters. In this paper a new fuzzy logic is introduced to control the harmonic in the power system, which has more advantages such as simplicity, ease of application, flexibility, speed and ability to deal with imprecision and uncertainties .The introduction of fuzzy logic can not only reduce harmonic,but also correct the power factor.

Fuzzy expert systems and challenge of new product pricing

This paper is focused on the representation and treatment of knowledge and data uncertainty within the context of an important industrial challenge, i.e., new product pricing. The most well known participating factor in pricing process is cost meanwhile the other factors like customer value and firm’s strategy should be considered in the pricing process, as well. Besides, there are other important factors like the risks that consumer bear in purchasing new product which must be carefully analyzed and considered. Nonetheless, many of these factors are blended with uncertainty. In recent decades, fuzzy logic was well developed and implemented in many applications to treat vagueness in complicated systems. Finding the pricing process a critical and complicated process which includes many vague parameters, we tried to design a fuzzy expert system to cope with this challenge. In this paper, after a brief introduction of fuzzy logic which has revealed a methodology to work with uncertainty and imitate humans reasoning, the pricing factors are introduced. Then a fuzzy expert system is designed to find the appropriate price of the new product considering the related parameters.

LEARNING FUZZY CLASSIFIER SYSTEMS: ARCHITECTURE AND EXPLORATION ISSUES

Introducing fuzzy logic in knowledge representation is a general technique to improve flexibility and performances of knowledge based and control software. Many researchers propose to introduce fuzzy logic representation in learning algorithms. Interesting features arise when fuzzy sets substitute the interval-based classification of input in a learning system; some of them imply an improvement in performance others an increased structural complexity in the architecture of the system and in the learning process. Focusing on Learning Classifier Systems, the introduction of fuzzy logic produces some new interesting features in this class of learning algorithms from many points of view: a new approach to classifier competition, the birth of competition vs. cooperation dilemma, and the introduction of an appropriate fuzzy interface with external world. In this paper, we discuss a fuzzyfication of the classical architecture of a learning classifier system (Holland's approach) and the improvements deriving from the use of fuzzy logic. In this work we especially discuss the competition vs. cooperation dilemma, analyzing the influence of exploration policy on the performance of crisp and fuzzy versions of learning classifier systems. We mainly focus on the use of fuzzy classifier systems to implement behaviors for reactive autonomous agents in the mobile robotics domain.

An interactive computer graphics interface for the introduction of fuzzy inference in environmental education

Fuzzy logic is based on sets of rules that can be easily understood by the students, since they bear a close resemblance to natural language. The introduction of fuzzy logic, within the framework of Environmental Education, is considered to be necessary in order to provide an insight to the complex environmental interactions. Fuzzy inference is introduced in this paper as an extension of hypothetico-predictive argumentation and it allows the investigation of alternative hypotheses. This is achieved through the development of an interactive computer graphics environment that encompasses a set of fuzzy logic analysis tools and a fuzzy inference model of a lake. The fuzzy model guarantees the scientific integrity of the simulation results, and the graphical interface presents to the students only the comprehensible characteristics of the environmental stressors in the ecosystem of the lake. The proposed graphical interface was developed in successive design stages, with the active participation of the students. The results of the students' experimentation with the graphical interface indicate that their comprehension of the significant environmental problems of the lake is considerably improved and some misconceptions are resolved. Thus, it is considered valuable as an aid to environmental education.

Fuzzy Non-Homogeneous Markov Systems

In this paper the theory of fuzzy logic and fuzzy reasoning is combined with the theory of Markov systems and the concept of a fuzzy non-homogeneous Markov system is introduced for the first time. This is an effort to deal with the uncertainty introduced in the estimation of the transition probabilities and the input probabilities in Markov systems. The asymptotic behaviour of the fuzzy Markov system and its asymptotic variability is considered and given in closed analytic form. Moreover, the asymptotically attainable structures of the system are estimated also in a closed analytic form under some realistic assumptions. The importance of this result lies in the fact that in most cases the traditional methods for estimating the probabilities can not be used due to lack of data and measurement errors. The introduction of fuzzy logic into Markov systems represents a powerful tool for taking advantage of the symbolic knowledge that the experts of the systems possess.

Quantifying Stenosis in Renal Arteriograms: A Fuzzy Syntactic Analysis

The introduction of fuzzy logic improves a system for the automatic quantification of renal artery lesions seen in digital subtraction angiograms. A two-step approach has been followed. An earlier system based on non-fuzzy syntactic analysis provided a clear symbolic description of the stenotic lesions. Although this system worked correctly, it did not take into account the variability and uncertainty inherent to image processing and to knowledge on the reference diameter. This system has been improved by the introduction of fuzzy logic in the representation of the reference diameter. It provides a description of the stenosis in terms of fuzzy quantities. To illustrate the benefits of the fuzzy approach, the results of the two systems have been compared by plotting the differences of an index of variability. It appears that the differences are statistically different when using a two-tailed paired t-test (t = 2.37; p = 0.025). The result shows that the fuzzy approach is better than a non-fuzzy approach in the sense that the index of variability is reduced significantly.

The Application of Expert Systems and Fuzzy Logic to Electric Power Systems in Japan - Experience, Problems and Solutions-

This paper describes the current state of attempts to introduce expert systems and fuzzy logic methods to electric power systems in Japan. First experiences and problems in the introduction of expert systems are presented from the standpoints of on-line and off-line systems. Next experiences and problems regarding the introduction of fuzzy logic, which are fewer, are presented. Lastly, we consider the future prospects for the application of new information processing technologies such as artificial intelligence (AI), fuzzy logic and neural networks.