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Journal articles on the topic 'Single Input Rule Module (SIRM)'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Yaw, Chong Tak, Shen Young Wong, and Keem Sian Yap. "An ELM-based single input rule module and its application in power generation." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 1 (2020): 359. http://dx.doi.org/10.11591/ijpeds.v11.i1.pp359-366.

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Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. In traditional fuzzy inference method which was the "if-then" rules, all the input and output objects were assigned to antecedent and consequent component respectively. However, a major dilemma was that the fuzzy rules' number kept increasing until the system and arrangement of the rules became complicated. Therefore, the single input rule modules connected type fuzzy inference (SIRM) method where consociated the ou

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2

Chong, Tak Yaw, Young Wong Shen, and Sian Yap Keem. "An ELM-based single input rule module and its application in power generation." International Journal of Power Electronics and Drive System (IJPEDS) 11, no. 1 (2020): 359–66. https://doi.org/10.11591/ijpeds.v11.i1.pp359-366.

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Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. In traditional fuzzy inference method which was the "if-then" rules, all the input and output objects were assigned to antecedent and consequent component respectively. However, a major dilemma was that the fuzzy rules' number kept increasing until the system and arrangement of the rules became complicated. Therefore, the single input rule modules connected type fuzzy inference (SIRM) method where cons

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3

Yubazaki, Naoyoshi, Jianqiang Yi, and Kaoru Hirota. "SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 1 (1997): 23–30. http://dx.doi.org/10.20965/jaciii.1997.p0023.

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A new fuzzy inference model, SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model, is proposed for plural input fuzzy control. For each input item, an importance degree is defined and single input fuzzy rule module is constructed. The importance degrees control the roles of the input items in systems. The model output is obtained by the summation of the products of the importance degree and the fuzzy inference result of each SIRM. The proposed model needs both very few rules and parameters, and the rules can be designed much easier. The new model is first applied to typical second

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4

Ren, Weina, Chengdong Li, and Peng Wen. "A novel purification machine and fuzzy inference method based hybrid model for wind speed forecasting." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 4059–70. http://dx.doi.org/10.3233/jifs-200205.

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As one kind of readily available renewable energy sources, wind is widely used in power generation where wind speed plays an important role. Generally speaking, we need to forecast the wind speed for improving the controllability of wind power generation. However, there exists considerable randomness and instabilities in wind speed data so that it is difficult to obtain accurate forecasting results. In this paper, we propose a novel fuzzy inference method based hybrid model for accurate wind speed forecasting. In this hybrid model, we adopt two strategies to enhance the estimation performance.

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5

Yi, Jianqiang, Naoyoshi Yubazaki, and Kaoru Hirota. "Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 4 (2000): 302–12. http://dx.doi.org/10.20965/jaciii.2000.p0302.

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A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy inference model is proposed. For each of the three input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy inference result of the SIRMs

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6

Shen, Macheng, and Jonathan P. How. "Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 578–87. http://dx.doi.org/10.1609/icaps.v31i1.16006.

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This paper studies decision-making in two-player scenarios where the type (e.g. adversary, neutral, or teammate) of the other agent (opponent) is uncertain to the decision-making agent (protagonist), which is an abstraction of security-domain applications. In these settings, the reward for the protagonist agent depends on the type of the opponent, but this is private information known only to the opponent itself, and thus hidden from the protagonist. In contrast, as is often the case, the type of the protagonist agent is assumed to be known to the opponent, and this information-asymmetry signi

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7

Chiolino, N., A. M. Francis, J. Holmes, M. Barlow, and C. Perkowski. "470 Celsius Packaging System for Silicon Carbide Electronics." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2021, HiTEC (2021): 000083–88. http://dx.doi.org/10.4071/2380-4491.2021.hitec.000083.

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Abstract High temperature Silicon Carbide (SiC) integrated circuit (IC) processes have enabled devices that operate at &gt;450°C for more than a year. These results have established the need for more advanced and practical packaging strategies. Off the shelf state of the art packages cannot withstand the same high temperatures as the semiconductor can for long periods of time. Packaging SiC die to survive temperatures &gt;450°C, while also maintaining a reasonable packaging strategy that is agile, rapid, and modular, presents new challenges. Presented is a technique for packaging SiC d

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8

Han, Choongyong, Mojdeh Delshad, Kamy Sepehrnoori, and Gary Arnold Pope. "A Fully Implicit, Parallel, Compositional Chemical Flooding Simulator." SPE Journal 12, no. 03 (2007): 322–38. http://dx.doi.org/10.2118/97217-pa.

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Summary A fully implicit, parallel, compositional reservoir simulator has been developed that includes both a cubic equation of state model for the hydrocarbon phase behavior and Hand's rule for the surfactant/oil/brine phase behavior. The aqueous species in the chemical model include surfactant, polymer, and salt. The physical property models include surfactant/oil/brine phase behavior, interfacial tension, viscosity, adsorption, and relative permeability as a function of trapping number. The fully implicit simulation results were validated by comparison with results from our IMPEC chemical f

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9

Benmakhlouf, Abdeslam, Abdellah Meraoumia, and Mohammed Lakhder Louazene. "Mobile Robot Path Following Controller Based On the Sirms Dynamically Connected Fuzzy Inference Model." International Journal of Integrated Engineering 15, no. 1 (2023). http://dx.doi.org/10.30880/ijie.2023.15.01.021.

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In this paper, the single input rule modules (SIRMs) dynamically connected fuzzy inference model is employed to design a path following controller for a unicycle wheeled mobile robot(WMR).The SIRMs model is mainly used to reduce the number of fuzzy inference rules and consequently reduce the processing time of the conventional Fuzzy Logic Control (FLC) schemes. The adopted path following strategy uses two control unites working in parallel to drive the WMR to follow a path composed of a succession of discrete waypoints. The first unit is a heading controller charged of generatingthe angular ve

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10

Hirofumi, Miyajima, Kishida Kazuya, Shigei Noritaka, and Miyajima Hiromi. "Learning Algorithms for Fuzzy Inference Systems Composed of Double- and Single-Input Rule Modules." January 5, 2016. https://doi.org/10.5281/zenodo.1112031.

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Most of self-tuning fuzzy systems, which are automatically constructed from learning data, are based on the steepest descent method (SDM). However, this approach often requires a large convergence time and gets stuck into a shallow local minimum. One of its solutions is to use fuzzy rule modules with a small number of inputs such as DIRMs (Double-Input Rule Modules) and SIRMs (Single-Input Rule Modules). In this paper, we consider a (generalized) DIRMs model composed of double and single-input rule modules. Further, in order to reduce the redundant modules for the (generalized) DIRMs model, pr

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11

Zhu, Binwu, Xinyun Zhang, Yibo Lin, Bei Yu, and Martin Wong. "DRC-SG 2.0: Efficient Design Rule Checking Script Generation via Key Information Extraction." ACM Transactions on Design Automation of Electronic Systems, May 6, 2023. http://dx.doi.org/10.1145/3594666.

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Design Rule Checking (DRC) is a critical step in integrated circuit design. DRC requires formatted scripts as the input to design rule checkers. However, these scripts are manually generated in the foundry, which is tedious and error-prone for generation of thousands of rules in advanced technology nodes. To mitigate this issue, we propose the first DRC script generation framework, leveraging a deep learning-based key information extractor to automatically identify essential arguments from rules and a script translator to organize the extracted arguments into executable DRC scripts. We further

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12

Li, Hao, and Jie Yang. "Design of fire alarm system of intelligent camera based on fuzzy recognition algorithm." Journal of Intelligent & Fuzzy Systems, February 10, 2021, 1–13. http://dx.doi.org/10.3233/jifs-189708.

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Aiming at the problems of low fire detection accuracy and high false alarm rate of the current intelligent camera fire accident alarm system, a fire accident alarm system based on fuzzy recognition algorithm is designed. By analyzing the structural principle of the fire detection and alarm system, selecting the CO gas, temperature and smoke sensor selection, designing the corresponding fire signal detection circuit, and designing the single-chip system circuit, including the single-chip clock circuit, reset circuit, power supply circuit and A/D conversion circuit design, on the basis of in-dep

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13

Ren, Chang, and Bin Wu. "A Robust joint coverless image steganography scheme based on two independent modules." Cybersecurity 7, no. 1 (2024). https://doi.org/10.1186/s42400-024-00299-5.

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AbstractWith the development of deep learning technology, great progress has been made in the field of coverless steganography based on deep learning technology, including some selection-based steganography methods that use deep learning technology and all generation-based steganography methods, however both of which have their limitations. The former is difficult to meet actual communication requirements in terms of communication capacity and completeness due to the limit of the algorithm. Due to the irreversibility of the process of generating secret images from message codeword, the recover

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14

Xu, Liyou, Haojie Dong, Sixia Zhao, Yiwei Wu, and Xiaoliang Chen. "A Tractor Transmission Systems Vibration Fault Data Augmentation and Diagnosis Method Based on DCGAN." Transactions of the Canadian Society for Mechanical Engineering, April 8, 2025. https://doi.org/10.1139/tcsme-2024-0167.

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The actual production process is often faced with insufficient fault data, Traditional data augmentation algorithms are prone to problems such as overfitting and pattern collapse. To address the issues, a tractor transmission systems vibration fault data augmentation and diagnosis method based on deep convolutional generative adversarial network is proposed. First, the original vibration signal is converted to a time-frequency diagram, which serves as the input to the generative adversarial network. Subsequently, the Two Timescale Update Rule(TTUR) strategy and gradient penalty are applied to

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15

Zamora Zapata, Mónica, Kari Lappalainen, Adam Kankiewicz, and Jan Kleissl. "Comparing solar inverter design rules to subhourly solar resource simulations." Journal of Renewable and Sustainable Energy 15, no. 5 (2023). http://dx.doi.org/10.1063/5.0151042.

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The input of a solar inverter depends on multiple factors: the solar resource, weather conditions, and control strategies. Traditional design calculations specify the maximum current either as 125% of the rated module current or as the maximum 3 h average current from hourly simulations over a typical year, neglecting extreme irradiance conditions: cloud enhancement events that usually last minutes. Inverter power-limiting control strategies usually prevent extreme events to cause strong currents at the inverter, but in some cases, they can fail, leading to high currents. In this study, we aim

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