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Abstract An original algorithm for tuning zero-order Sugeno-type fuzzy inference systems based on statistical data is presented. The algorithm is based on selecting areas around the reference points, finding the coordinates of the...
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Abstract An original algorithm for tuning zero-order Sugeno-type fuzzy inference systems based on statistical data is presented. The algorithm is based on selecting areas around the reference points, finding the coordinates of the center of mass of the selected areas, and using them to set up a fuzzy inference system. A convergence theorem is proven for the proposed algorithm. The paper presents the results of studying the quality of the algorithm under conditions of changing the number of membership functions of input variables and the number of statistical data points, on the basis of which the fuzzy inference systems were tuned.
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In this paper, we propose a novel fuzzy inference system on picture fuzzy set called picture inference system (PIS) to enhance inference performance of the traditional fuzzy inference system. In PIS, the positive, neutral and nega...
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In this paper, we propose a novel fuzzy inference system on picture fuzzy set called picture inference system (PIS) to enhance inference performance of the traditional fuzzy inference system. In PIS, the positive, neutral and negative degrees of the picture fuzzy set are computed using the membership graph that is the combination of three Gaussian functions with a common center and different widths expressing a visual view of degrees. Then, the positive and negative defuzzification values, synthesized from three degrees of the picture fuzzy set, are used to generate crisp outputs. Learning in PIS including training centers, widths, scales and defuzzification parameters is also discussed. The system is adapted for all architectures such as the Mamdani, the Sugeno and the Tsukamoto fuzzy inferences. Experimental results on benchmark UCI Machine Learning Repository datasets and an example in control theory - the Lorenz system are examined to verify the advantages of PIS.
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The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level...
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The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, etc., is growing exponentially. These biological data are analyzed using various computational techniques for knowledge discovery which is also one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays a pivotal role in understanding gene regulation processes and disease mechanism at the molecular level. From last couple of decades, researchers are interested in developing computational algorithms for GRN inference (GRNI) from high-throughput experimental data. Several computational approaches have been proposed for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression based approaches, probabilistic approaches (Bayesian networks, naive byes), artificial neural networks and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approaches, is a well-studied technique in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches developed during last two decades for GRNI.
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Abstract With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-...
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Abstract With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-fuzzy based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. For this purpose, the proposed model establishes peer-to-peer connections among neighboring traffic light junctions to fetch the respective real-time traffic conditions and congestion. A fuzzy membership function is utilized to generate an intelligent traffic light phase duration. Further, to obtain an effective fuzzy membership function input value considering real-time heterogeneous traffic scenarios, an adaptive neural network is utilized. The proposed system adopts three execution modes: Congestion Mode (CM), Priority Mode (PM), and Fair Mode (FM). It automatically activates and switches to the best mode based on the live traffic conditions. The performance of the proposed model is evaluated via a realistic simulation on the Gwalior city map of India using an open-source simulator known as Simulation of Urban Mobility (SUMO). The results evident the effectiveness of the proposed model over the existing state-of-the-art approaches.
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Abstract The article proposes an optimized algorithm that allows the use of a fuzzy inference system with a large number of inference rules (about 10 million) in computing systems limited by RAM and CPU power. Optimization is achi...
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Abstract The article proposes an optimized algorithm that allows the use of a fuzzy inference system with a large number of inference rules (about 10 million) in computing systems limited by RAM and CPU power. Optimization is achieved by redistributing the membership functions of input variables and the dynamic formation of inference rules, which allows fuzzy system to store only inference rules conclusions, without using a complete search of the rules in the process of fuzzy inference.
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In this paper, a new fuzzy inference modeling method is proposed for nonlinear systems. A proposed triangular pyramid fuzzy system (TPFS) which is proved to have second-order approximation accuracy is employed in the new modeling ...
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In this paper, a new fuzzy inference modeling method is proposed for nonlinear systems. A proposed triangular pyramid fuzzy system (TPFS) which is proved to have second-order approximation accuracy is employed in the new modeling method. Based on the interpolation mechanism of TPFS, practical systems (which can be described by a group of fuzzy inference rules) can be converted to a simplified linear model with variable coefficients. Expressions of the time-varying local equations appears significantly simple due to the linearity of the model by using the proposed fuzzy modeling method. The approximation performance superiority of the proposed modeling method is demonstrated by simulation results.
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Coronavirus is a deadly disease that affects animals and birds. In most cases, this virus infects humans by causing aerial precipitation of certain fluid secreted by the infected animal or its bodily portion. This virus is more vi...
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Coronavirus is a deadly disease that affects animals and birds. In most cases, this virus infects humans by causing aerial precipitation of certain fluid secreted by the infected animal or its bodily portion. This virus is more vital than other unintentional viruses. During December 2019, a new coronavirus family named Novel Coronavirus (Covid-19) was discovered inChina, at the place called Wuhan. The number of people infected with this virus has quickly increased in Wuhan and other nations since January 23, 2020.This study proposes a system for classifying and analyzing COVID 19 confirmed cases, cured cases, and deceased as inputs and vaccinated people as outputs for the most populous twelve districts in Tamil Nadu, India, for the trimester tenure fromJuly 2020 to September 2021. This research study can be analyzed using the Fuzzy Mamdani Inference study. This study clearly demonstrates the significance of vaccination in restoring the flourished glory of life, which we have been missing for the past two years.
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This paper presents a novel training algorithm for fuzzy inference systems. The algorithm combines the Levenberg- Marquardt algorithm with variable structure systems approach. The combination is performed by expressing the paramet...
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This paper presents a novel training algorithm for fuzzy inference systems. The algorithm combines the Levenberg- Marquardt algorithm with variable structure systems approach. The combination is performed by expressing the parameter Update rule in continuous time and application of sliding mode control method to the gradient-based training procedure. The proposed combination therefore exhibits a degree of robustness to the unmodeled multivariable internal dynamics of Leverberg-Marquardt technique. With conventional training procedures, the excitation of this dynamics during a training Cycle can lead to instability, which may be difficult to alleviate due to the multidimensionality of the solution space and the Ambiguities concerning the environmental conditions. This paper proves that a fuzzy inference mechanism can be trained Such that the adjustable parameter values are forced to settle down (parameter stabilization) while minimizing an appropriate Cost function (cost optimization). In the application example, control fo a two degrees of freedom direct drive SCARA Robotic manipulator is considered. As the controller, a standard fuzzy system architecture is used and the parameter tuning Is performed by the proposed algorithm.
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Bahan bakar merupakan salah satu kebutuhan yang saat ini tidak bisa dilepaskan dari masyarakat. Bahan bakar cair terutama bensin atau premium, pertalite, pertamax, dan solar merupakan bahan bakar yang digunakan pada alat transport...
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Bahan bakar merupakan salah satu kebutuhan yang saat ini tidak bisa dilepaskan dari masyarakat. Bahan bakar cair terutama bensin atau premium, pertalite, pertamax, dan solar merupakan bahan bakar yang digunakan pada alat transportasi. Semakin banyak alat transportasi yang dimiliki masyarakat dapat memungkinkan tidak tersedianya stok bahan bakar di salah satu SPBU (Stasiun Pengisian Bahan Bakar Umum) tertentu. Dengan tidak tersedianya stok persediaan bahan bakar di SPBU, hal ini dapat menimbulkan beberapa masalah, seperti antrian yang panjang pada salah satu SPBU, serta kurangnya kebutuhan bahan bakar pada masyarakat. Salah satu metode untuk memecahkan masalah tersebut adalah menggunakan metode Fuzzy Tsukamoto. Fuzzy Tsukamoto merupakan salah satu metode yang termasuk ke dalam sistem inferensi fuzzy. Metode ini dapat menentukan jumlah produksi, sehingga jumlah produksi sebuah barang dapat diprediksi. Hasil penelitian menunjukkan bahwa metode Tsukamoto mampu memprediksi jumlah penerimaan stok bahan bakar yang seharusnya diterima oleh SPBU pada periode berikutnya. Dimana, hasil pengujian ketepatan prediksi diukur dengan menggunakan metode MAPE (Mean Absolute Percentage Error) dengan nilai persentase error yang didapatkan adalah sebesar 16 %, sehingga kinerja sistem dalam memprediksi stok bahan bakar dapat dikatakan bagus.
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The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al....
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The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional-type SIRMs method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose a "kernel-type SIRMs method" which uses the kernel trick to the SIRMs method, and show that this method can treat XOR. Further, a learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and compared with the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions, medical diagnostic system and discriminant analysis of Iris data.
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