International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1187
Fuzzy Clustering Data Given in the Ordinal Scale
Статья научная
A fuzzy clustering algorithm for multidimensional data is proposed in this article. The data is described by vectors whose components are linguistic variables defined in an ordinal scale. The obtained results confirm the efficiency of the proposed approach.
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Статья научная
A task of clustering data given on the ordinal scale under conditions of overlapping clusters has been considered. It's proposed to use an approach based on membership and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions.
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Fuzzy Controller Design using FPGA for Sun Tracking in Solar Array System
Статья научная
The output power produced by high-concentration solar thermal and photovoltaic systems is directly related to the amount of solar energy acquired by the System, and it is therefore necessary to track the sun’s position with a high degree of accuracy. This paper presents sun tracking generating power system designed and implemented in real time. A tracking mechanism composed of photovoltaic module, stepper motor ,sensors, input/output interface and expert FLC implemented on FPGA, that to track the sun and keep the solar cells always face the sun in most of the day time. The proposed sun tracking fuzzy controller has been tested using Matlab/Simulink program; the simulation results verify the effectiveness of the proposed controller and shows an excellent result.
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Статья научная
Adaptive modulation is one of the recent technologies used to improve future communication systems. Many adaptive modulation techniques have been developed for the improving the performance of Orthogonal Frequency Division Multiplexing (OFDM) system in terms of high data rates and error free delivery of data. But uncertain nature of wireless channel reduces the performance of OFDM system with fixed modulation techniques. In this paper, modified adaptive modulation technique has been proposed which adapts to the nature of communication channel based upon present modulation order, code rate, BER and SNR characterizing uncertain nature of communication channel by using a Fuzzy Inference System which further enhances the performance of OFDM systems in terms of high transmission data rate and error free delivery of data.
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Fuzzy Logic Based Power System Contingency Ranking
Статья научная
Voltage stability is a major concern in planning and operations of power systems. It is well known that voltage instability and collapse have led to major system failures. Modern transmission networks are more heavily loaded than ever before to meet the growing demand. One of the major consequences resulted from such a stressed system is voltage collapse or instability. This paper presents maximum loadability identification of a load bus in a power transmission network. In this study, Fast Voltage Stability Index (FVSI) is utilized as the indicator of the maximum loadability termed as Qmax. In this technique, reactive power loading will be increased gradually at particular load bus until the FVSI reaches close to unity. Therefore, a critical value of FVSI was set as the maximum loadability point. This value ensures the system from entering voltage-collapse region. The main purpose in the maximum loadability assessment is to plan for the maximum allowable load value to avoid voltage collapse; which is important in power system planning risk assessment. The most important task in security analysis is the problem of identifying the critical contingencies from a large list of credible contingencies and ranks them according to their severity. The condition of voltage stability in a power system can be characterized by the use of voltage stability indices. This paper presents fuzzy approach for ranking the contingencies using composite-index based on parallel operated fuzzy inference engine. The Line Flow index (L.F) and bus Voltage Magnitude (VM) of the load buses are expressed in fuzzy set notation. Further, they are evaluated using Fuzzy rules to obtain overall Criticality Index. Contingencies are ranked based on decreasing order of Criticality Index and then provides the comparison of ranking obtained with FVSI method.
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Статья научная
In this paper, a fuzzy logic control (FLC) is proposed for maximum power point tracking (MPPT) in wind turbine connection to Permanent Magnet Synchronous Generator (PMSG). The proposed fuzzy logic controller tracks the maximum power point (MPP) by measurements the load voltage and current. This controller calculates the load power and sent through the fuzzy logic system. The main goal of this paper is design of the fuzzy logic controller in the model of DC-DC converter (boost converter). This method allows the MPPT controller output (duty cycle) adjusts the voltage input to the converter to track the maximum power point of the wind generator.
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Fuzzy Logic using Tahani Model on Food Commodity
Статья научная
Seasonal vegetable plants are one of food commodity in Indonesia which contributed significantly in supporting the growth of national economy. Fuzzy Logic using Tahani Model is appropriate to analyze the harvested area, production, and yields of food commodity. It is based on query in fuzzy database. It is also can be proposed as recommendation for both government and entrepreneur in achieving in the target based on query. Target is main priority of food commodity. Fuzzy query is harvested area (medium), production (sufficient), and yield (normal). Non fuzzy query is commodity (export commodities), harvest (plants harvested several times), and form of product (fresh fruit). Result showed that eggplant (0.6587), tomato (0.6023), cucumber (0.5865), capsicum frutescens (0.2901), and capsicum annum (0.1581) as recommendation for priority of national food policy.
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Fuzzy Pattern Recognition Based on Symmetric Fuzzy Relative Entropy
Статья научная
Based on fuzzy similarity degree, entropy, relative entropy and fuzzy entropy, the symmetric fuzzy relative entropy is presented, which not only has a full physical meaning, but also has succinct practicability. The symmetric fuzzy relative entropy can be used to measure the divergence between different fuzzy patterns. The example demonstrates that the symmetric fuzzy relative entropy is valid and reliable for fuzzy pattern recognition and classification, and its classification precision is very high.
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Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach
Статья научная
In this paper, a fuzzy predictive control scheme is proposed for controlling output voltage of a step-down DC-DC converter in presence of disturbance and uncertainty. The DC-DC converter is considered as a hybrid system and modeled by Mixed Logical Dynamical modeling approach. The main objective of the paper is to design a Fuzzy Predictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. A model predictive control is designed based on the hybrid model and applied to the system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from lack of robustness in presence of disturbance and uncertainty. So, to succeed in facing up to the problem a fuzzy supervisor is utilized to adjust the main predictive controller based on the measured states of the system. In this paper it is shown that the proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in normal operation and robustness in presence of disturbance and uncertainty. Through simulations effectiveness of the proposed method is shown.
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Fuzzy Sliding Mode Control Scheme for Vehicle Active Suspension System Optimized by ABC Algorithm
Статья научная
This paper suggests a proposed control scheme of fuzzy sliding mode and PID controller tuned with Artificial bee colony (ABC) algorithm to control vehicle suspension system. Suspension systems are utilized to provide vehicles safety and improve comfortable driving. The effects of the road roughness transmitted by the tires to the vehicle body can be reduced by using suspension systems. Fuzzy system is used for estimating the unknown parameters and uncertainty in the suspension system components (spring, damper and actuator). This study combines sliding mode with fuzzy strategy to design a robust control method. The ABC technique is used to optimize the controller parameters. The suggested control scheme endeavors to limit the vibration of the vehicle body by creating a suitable force for the suspension systems when passing on disturbance. Passive and active suspension systems are compared to test efficiency and ability of the proposed control scheme to enhance the safety and comfortable driving for different road profiles.
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Fuzzy clustering of sequential data
Статья научная
With the increase in popularity of the Internet and the advancement of technology in the fields like bioinformatics and other scientific communities the amount of sequential data is on the increase at a tremendous rate. With this increase, it has become inevitable to mine useful information from this vast amount of data. The mined information can be used in various spheres; from day to day web activities like the prediction of next web pages, serving better advertisements, to biological areas like genomic data analysis etc. A rough set based clustering of sequential data was proposed by Kumar et al recently. They defined and used a measure, called Sequence and Set Similarity Measure to determine similarity in data. However, we have observed that this measure does not reflect some important characteristics of sequential data. As a result, in this paper, we used the fuzzy set technique to introduce a similarity measure, which we termed as Kernel and Set Similarity Measure to find the similarity of sequential data and generate overlapping clusters. For this purpose, we used exponential string kernels and Jaccard's similarity index. The new similarity measure takes an account of the order of items in the sequence as well as the content of the sequential pattern. In order to compare our algorithm with that of Kumar et al, we used the MSNBC data set from the UCI repository, which was also used in their paper. As far as our knowledge goes, this is the first fuzzy clustering algorithm for sequential data.
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Fuzzy inference system optimization by evolutionary approach for mobile robot navigation
Статья научная
The problem in the autonomous navigation of a mobile robot is to define a strategy that allows it to reach the final destination and avoiding obstacles. Fuzzy logic is considered as an important tool to solve this problem. It can mimic reasoning abilities of the human being in navigation tasks. However a major problem of fuzzy systems is obtaining their parameters which are generally specified by human experts. This process can be long and complex. In order to generate optimal parameters of fuzzy controller, this work propose a learning and optimization process based on ant colony algorithm ACO and genetic algorithm operators (crossover and mutation).We present a comparison between inference system for autonomous navigation based on fuzzy logic before and after learning. The simulated results show clearly the impact of the optimization approach improves the fuzzy controller performance mainly in obstacle avoidance and detection of the shortest path.
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Fuzzy logic based energy aware routing protocol for internet of things
Статья научная
Maximizing the network lifetime is one of the major challenges in Low Power and Lossy Networks (LLN). Routing plays a major role in LLN, for minimizing the energy consumption across the network nodes. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for LLN. Though, RPL fulfilled the necessity of LLN, several issues like increasing the energy efficiency, quality of service and the network lifetime are to be focused. In LNN, the inefficient route selection results in increased network traffic, energy depletion and packet loss ratio across the network. In this paper, we propose a fuzzy logic based energy aware routing protocol (FLEA-RPL), which considers the routing metrics load, residual energy (RER) and expected transmission count (ETX) for the best route selection. FLEA-RPL applies fuzzy logic over these metrics, to select the best route to transfer the network data efficiently. The COOJA simulator is used to assess the efficiency of the proposed FLEA-RPL. The FLEA-RPL protocol is compared with similar protocol standard RPL, MRHOF (ETX) based RPL (MRHOF-RPL) and FL-RPL. The simulation result shows that FLEA-RPL improves the network lifetime by 10-12% and packet delivery ratio by 2-5%.
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Fuzzy logic using tsukamoto model and sugeno model on prediction cost
Статья научная
This paper aims to implement Fuzzy Logic for cost prediction. Fuzzy Logic using Tsukamoto Model and Sugeno Model. Predicted costs consist of communication cost, transportation cost, and social cost as the external cost. The external cost is one component of living cost. High external cost becomes one of the causes of the high cost of living. The high cost of living is one of the factors causing high-cost economy. In this case, the cost prediction using Fuzzy Logic. Experimental results show that Fuzzy Logic using Tsukamoto Model with value is 1891. Fuzzy Logic using Sugeno Model with value 1621. Both models produce a feasible cost prediction. Feasible is meaning accurate and proper (value cost between low cost and high cost from all of cost). There are 46.56 % of the population of middle class in Indonesia. This means that 46.56% of the population of Indonesia has the potential to reduce the high cost economy. High cost economy (living cost) can be reduced, it can drive economic growth (social cost) and be able to improve social welfare (social cost).
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Fuzzy-Based System for Determining the Severity Level of Knee Osteoarthritis
Статья научная
The task of medical diagnosis, unlike other diagnostic processes is more complex because a lot of vagueness, linguistic uncertainty, subjectivity, measurement imprecision, natural diversity are all prominently present in medical diagnosis. Osteoarthritis (OA) of the knee is a major public health issue causing chronic disability and reduction in quality of life; it is reported to be associated with a significant decline in function and causes a higher rate of disability than any other chronic condition. Osteoarthritis (OA) exacts a cost in terms of pain, limited mobility, and decreased function among a wide range of individuals. With improvement in science and technology, intelligent computing has been used to assist in enhancing qualitative services. This paper reports the development of a fuzzy-based system to determine the level of severity of knee osteoarthritis, given some input conditions. The system was implemented and simulated using MATLAB Fuzzy Logic Toolbox. The results are entrusting and promising based on the flexibility and case of adaptability.
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Fuzzy-Based XML Knowledge Retrieval Methods in Edaphology
Статья научная
In this paper, we propose a proficient method for knowledge management in Edaphology to assist the edaphologists and those related with agriculture in a big way. The proposed method mainly consists two sections of which the first one is to build the knowledge base using XML and the latter part deals with information retrieval by searching using fuzzy. Initially, the relational database is converted to the XML database. The paper discusses two algorithms, one is when the soil characteristics are inputted to have the plant list and in the other, plant names are inputted to have the soil characteristics suited for the plant. While retrieving the query result, the crisp numerical values are converted to fuzzy using the triangular fuzzy membership function and matched to those in database. And those which satisfy are added to the result list and subsequently the frequency is found out to rank the result list so as to obtain the final sorted list. Performance metrics used in order to evaluate the method and compare it to baseline paper are number of plants retrieved, ranking efficiency, and computation time and memory usage. Results obtained proved the validity of the method and the method obtained average computation time of 0.102 seconds and average memory usage of 2486 Kb, which all are far better than the previous method results.
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Fuzzy-based Bi-objective Energy Efficient Routing Protocol for Large Scale Mobile Ad-hoc Network
Статья научная
MANET is an infrastructure-less network, which is comprised of a greater number of nodes that are moving in a random manner. Establishing efficient and reliable routes is the crucial design issue in MANET. In addition, providing energy-efficient routes for performing communication operations in MANET is also a challenging goal. Increasing network lifetime and decreasing overall traffic load are the significant considerations while designing the MANET. An energy optimization routing mechanism with a fuzzy basis that is state-based and influenced by COVID-19 is used to prevent and control epidemics. Based on their lifespan and varying mobility speeds, it is dependent on energy optimization yield under various scenarios. Fuzzy-based bi-objective criteria in energy-efficient routing protocol for the mobile ad-hoc network to select the different constraints like link stability, bandwidth, and battery life. The main goal of this paper is to obtain the optimal solution to choose the best route, limit battery life, and change the movement of mobile nodes for large-scale mobile ad-hoc networks. It is demonstrated that the possible solution relates to the domain of selecting the variables by outlining the connection between the energy function’s variables. Researchers optimize a path's link lifespan, resulting in the choice of the shortest path being particularly efficient in COVID-19 pandemic prevention and treatment.
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Fuzzy-based user behavior characterization to detect HTTP-GET flood attacks
Статья научная
Internet was designed to serve the basic requirement of data transfer between systems. The security perspectives were therefore overlooked due to which the Internet remains vulnerable to a variety of attacks. Among all the possible attacks, Distributed Denial of Service (DDoS) attack is one of the eminent threats that target the availability of the online services to the intended clients. Now-a-days, attackers target application layer of the network stack to orchestrate attacks having a high degree of sophistication. GET flood attacks have been very much prevalent in recent years primarily due to advancement of bots allowing impersonating legitimate client behavior. Differentiating between a human client and a bot is therefore necessary to mitigate an attack. This paper introduces a mitigation framework based on Fuzzy Control System that takes as input two novel detection parameters. These detection parameters make use of clients' behavioral characteristic to measure their respective legitimacy. We design an experimental setup that incorporates two widely used benchmark web logs (Clarknet and WorldCup) to build legitimate and attack datasets. Further, we use these datasets to assess the performance of the proposed through well-known evaluation metrics. The results obtained during this work point towards the efficiency of our proposed system to mitigate a wide range of GET flood attack types.
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Статья научная
The distributed generation (DG) locations have significant impacts on network reliability and loss. This paper presents a method for optimal DG allocation and sizing in distribution systems in order to reduce network loss and improve system reliability at the lowest cost. For this purpose, a multi-objective cost function considering active power loss, reliability index and DG cost is used to find the optimal size and location of DG units. Also, a load duration curve including four load levels with different weighting factor is considered. Moreover, the optimization technique that is based on Genetic Algorithm (GA) is implemented. In this paper, ECOST index is considered for reliability evaluation in the cost function and also, the effects system reconfiguration, is also considered for reliability assessment. To evaluate the proposed algorithm, the IEEE 33-bus and 69-bus test systems are employed and the results obtained reveal the effectiveness of proposed method.
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GA_MLP NN: A Hybrid Intelligent System for Diabetes Disease Diagnosis
Статья научная
Diabetes is a condition in which the amount of sugar in the blood is higher than normal. Classification systems have been widely used in medical domain to explore patient's data and extract a predictive model or set of rules. The prime objective of this research work is to facilitate a better diagnosis (classification) of diabetes disease. There are already several methodology which have been implemented on classification for the diabetes disease. The proposed methodology implemented work in 2 stages: (a) In the first stage Genetic Algorithm (GA) has been used as a feature selection on Pima Indian Diabetes Dataset. (b) In the second stage, Multilayer Perceptron Neural Network (MLP NN) has been used for the classification on the selected feature. GA is noted to reduce not only the cost and computation time of the diagnostic process, but the proposed approach also improved the accuracy of classification. The experimental results obtained classification accuracy (79.1304%) and ROC (0.842) show that GA and MLP NN can be successfully used for the diagnosing of diabetes disease.
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