Relevant bibliographies by topics / Artificial orthogonalization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Artificial orthogonalization'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Artificial orthogonalization"

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Robinson, P. John, and A. Saranraj. "Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems." Indian Journal Of Science And Technology 17, no. 24 (2024): 2529–37. http://dx.doi.org/10.17485/ijst/v17i24.1386.

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Objectives: To propose a suitable decision-making model based on Intuitionistic Fuzzy sets (IFSs) and Gram-Schmidt orthogonalization process for Artificial Neural Network (ANN). Methods: The IFS data sets appearing in the form of matrices are aggregated using the available aggregation operators in the literature and then the collective aggregated information is processed through Gram-Schmidt orthogonalization for the revised input vectors which is then fed into the ANN algorithm following Delta Learning Rule for the next phase. The weight updation is performed through the ANN and the output is
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P, John Robinson, and Saranraj A. "Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems." Indian Journal of Science and Technology 17, no. 24 (2024): 2529–37. https://doi.org/10.17485/IJST/v17i24.1386.

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Abstract <strong>Objectives:</strong>&nbsp;To propose a suitable decision-making model based on Intuitionistic Fuzzy sets (IFSs) and Gram-Schmidt orthogonalization process for Artificial Neural Network (ANN).&nbsp;<strong>Methods:</strong>&nbsp;The IFS data sets appearing in the form of matrices are aggregated using the available aggregation operators in the literature and then the collective aggregated information is processed through Gram-Schmidt orthogonalization for the revised input vectors which is then fed into the ANN algorithm following Delta Learning Rule for the next phase. The weig
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Bezsonov, O. O., K. O. Oliinyk, O. S. Romanyk, O. G. Rudenko, and N. M. Serdiuk. "Factorized Perseptron Training Algorithms In The Problem Of Constructing A Nonlinear Model." Bionics of Intelligence 1, no. 94 (2020): 23–29. http://dx.doi.org/10.30837/bi.2020.1(94).04.

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In order to improve the computational properties of training procedures for artificial neural networks (ANNs),which, being universal approximators, make it possible to restore any arbitrarily complex continuous nonlinear functionwith a given accuracy, their factorized forms have been developed based on the Cholesky, Householder transformationsand Gram-Schmidt orthogonalization. The analysis of their properties showed that the most effective way to increasethe stability of the learning algorithm is to use the Householder transformation
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Dmitriy, Domin. "ARTIFICIAL ORTHOGONALIZATION IN SEARCHING OF OPTIMAL CONTROL OF TECHNOLOGICAL PROCESSES UNDER UNCERTAINTY CONDITIONS." Eastern-European Journal of Enterprise Technologies 5, no. 9 (65) (2013): 45–53. https://doi.org/10.5281/zenodo.3602372.

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The paper describes further development of the methods of artificial orthogonalization of passive experiment designs describing the experimental values of the output function in a multi-dimensional factor space of a small sample of fuzzy data. This allows fuzzy clustering for the formation of subspaces and further local description of the response function; building local regression equations in the subspaces of full factorial space; calculating the values of the response function at the points of the orthogonalized design of experiment. The methods for processing the asymmetrical design of fa
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Domin, Dmitriy. "Artificial orthogonalization in searching of optimal control of technological processes under uncertainty conditions." Eastern-European Journal of Enterprise Technologies 5, no. 9(65) (2013): 45–53. http://dx.doi.org/10.15587/1729-4061.2013.18452.

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Oku, Makito, Takaki Makino, and Kazuyuki Aihara. "Pseudo-Orthogonalization of Memory Patterns for Associative Memory." IEEE Transactions on Neural Networks and Learning Systems 24, no. 11 (2013): 1877–87. http://dx.doi.org/10.1109/tnnls.2013.2268542.

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Zeng, Guoqiang, Baihai Zhang, Fenxi Yao, and Senchun Chai. "Modified bidirectional extreme learning machine with Gram–Schmidt orthogonalization method." Neurocomputing 316 (November 2018): 405–14. http://dx.doi.org/10.1016/j.neucom.2018.08.029.

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D., Demin. "Application of artificial orthogonalization in search for optimal control of technological processes under uncertainty." Eastern-European Journal of Enterprise Technologies 5, no. 9 (65) (2013): 45–53. https://doi.org/10.15587/1729-4061.2013.18452.

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The aim of research is to develop a methodology for determining the structure and parameters of models that describe technological processes in conditions of uncertainty, which allows finding optimal control at all the main stages of such processes. The object of research is a technological process that can be described by a mathematical model of the &quot;composition - properties&quot; type, which determines the quality of the product at the output of such a process. In this case, the technological process was considered as a generalized concept, a characteristic feature of which is the gener
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HAGIWARA, Katsuyuki. "Nonparametric Regression Method Based on Orthogonalization and Thresholding." IEICE Transactions on Information and Systems E94-D, no. 8 (2011): 1610–19. http://dx.doi.org/10.1587/transinf.e94.d.1610.

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Sánchez-Sánchez, Pablo, and Marco A. Arteaga-Pérez. "Improving force tracking control performance in cooperative robots." International Journal of Advanced Robotic Systems 14, no. 4 (2017): 172988141770896. http://dx.doi.org/10.1177/1729881417708969.

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In order to obtain the environment’s information, cooperative robots could need a lot of sensors. A possible solution to reduce the number of sensors might be the use of control–observer structures. In this article, we have designed a control algorithm by using a modified hybrid computed torque method based on the principle of orthogonalization, but in order to avoid the use of tachometers in the implementation, we are including a velocity observer. The stability proof is developed by using the theory of Lyapunov. Simulation of the proposed control structure compared with a well-known control
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Dissertations / Theses on the topic "Artificial orthogonalization"

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Димко, Єгор Павлович. "Моделі та методи оптимального керування індукційним дуплекс-процесом за умов невизначеності". Thesis, Національний технічний університет "Харківський політехнічний інститут", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/38635.

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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.03 – системи та процеси керування. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2018. Дисертація присвячена вирішенню актуальної науково-практичної задачі – розробці методів оптимального управління в умовах невизначеності. Показана можливість побудови адекватної математичної моделі індукційного дуплекс-процесу плавки як об'єкта управління в умовах неможливості реалізації плану активного експерименту в виробничих умовах. На основі цього запропоновано для опису кінцевого
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Димко, Єгор Павлович. "Моделі та методи оптимального керування індукційним дуплекс-процесом за умов невизначеності". Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/38290.

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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.03 – системи та процеси керування. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2018. Дисертація присвячена вирішенню актуальної науково-практичної задачі – розробці методів оптимального управління в умовах невизначеності. Показана можливість побудови адекватної математичної моделі індукційного дуплекс-процесу плавки як об'єкта управління в умовах неможливості реалізації плану активного експерименту в виробничих умовах. На основі цього запропоновано для опису кінцевог
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(8740647), William Steven Singleton. "Increasing CNN Representational Power Using Absolute Cosine Value Regularization." Thesis, 2020.

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The Convolutional Neural Network (CNN) is a mathematical model designed to distill input information into a more useful representation. This distillation process removes information over time through a series of dimensionality reductions, which ultimately, grant the model the ability to resist noise, and generalize effectively. However, CNNs often contain elements that are ineffective at contributing towards useful representations. This Thesis aims at providing a remedy for this problem by introducing Absolute Cosine Value Regularization (ACVR). This is a regularization technique hypothesized
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Book chapters on the topic "Artificial orthogonalization"

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Li, Xuan, Kaicheng Li, Xiangui Xiao, and Beiao Li. "A VMD Harmonic Detection Method Based on Improved SVD Denoising." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231216.

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Harmonic detection is a vital problem in power system, but the accuracy of harmonic detection is affected by noise. In order to suppress noise, this paper proposes the improved singular value decomposition (SVD) to denoise. Then, the optimal number of decomposition modes of variational mode decomposition (VMD) is determined with the aim of minimizing the residual of signal energy after Schmidt orthogonalization. The amplitude and frequency of each harmonic are detected by the Hilbert transform. The simulation shows that the algorithm can accurately identify the information characteristics of e
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Khan, Ahmad Saeed, Erik Schaffernicht, and Johannes Andreas Stork. "On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240818.

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Estimating treatment effects from observational data is paramount in healthcare, education, and economics, but current deep disentanglement-based methods to address selection bias are insufficiently handling irrelevant variables. We demonstrate in experiments that this leads to prediction errors. We disentangle pre-treatment variables with a deep embedding method and explicitly identify and represent irrelevant variables, additionally to instrumental, confounding and adjustment latent factors. To this end, we introduce a reconstruction objective and create an embedding space for irrelevant var
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Conference papers on the topic "Artificial orthogonalization"

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Zhang, Yiling, You Wu, Li Zeng, and Xiaocao Ouyang. "Orthogonalization-guided sparse transformer for signal time series classification." In International Symposium on Artificial Intelligence Innovations (ISAII 2025), edited by Xin Xu and Weihua Ou. SPIE, 2025. https://doi.org/10.1117/12.3073280.

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Xiao, Weilin, Ming Xu, Yonggui Lin, Jin Chen, Haitao Zou, and Qinhao Wu. "An open-world object detection model based on multiscale feature orthogonalization." In International Symposium on Artificial Intelligence Innovations (ISAII 2025), edited by Xin Xu and Weihua Ou. SPIE, 2025. https://doi.org/10.1117/12.3073402.

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Rose, Adam I., Bindu Chandna, and Kevin B. Burke. "Phase-Only Artificial Noise for Secure Communication with Multiple Antennas Using Simultaneous Orthogonalization." In 2022 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2022. http://dx.doi.org/10.1109/wifs55849.2022.9975471.

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Mohamed, M. M. A., and B. Far. "A Fast Technique for White Blood Cells Nuclei Automatic Segmentation Based on Gram-Schmidt Orthogonalization." In 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/ictai.2012.133.

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Dall'Aqua, Marcelo J., Emilio J. R. Coutinho, Eduardo Gildin, Zhenyu Guo, Hardik Zalavadia, and Sathish Sankaran. "Input-Output Invariant Fast Proxy Models for Production Optimization." In SPE Latin American and Caribbean Petroleum Engineering Conference. SPE, 2023. http://dx.doi.org/10.2118/213117-ms.

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Abstract This work aims to obtain reduced-order models for fluid flows in porous media that can be used for optimal well-control design and are they are equipped with input-output tracking capabilities. Meeting the net-zero emission paradigm will require a realignment of hydrocarbon production strategies with other forms of energy production, such as hydrogen and geothermal. Profiting from all these energy sources is only possible if accurate and timely predictions of the injection-production behavior of fluids, including geomechanics issues in the subsurface, can be attained. High-fidelity re
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