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Novel Nonlinear Hypothesis for the Delta Parallel Robot Modeling

TLDR
This document proposes two nonlinear hypothesis which use different structures instead of using the linear bounded maps and their goal is to improve the second order processes modeling.
Abstract
In previous investigations, the nonlinear hypothesis use the linear bounded maps. Nonlinear hypothesis are described as the combination of the first order terms, and after of the mentioned combination, one bounded map is applied to alter the result. This document proposes two nonlinear hypothesis which use different structures instead of using the linear bounded maps. They are termed as novel nonlinear hypothesis and second order nonlinear hypothesis and their goal is to improve the second order processes modeling. The proposed nonlinear hypothesis are described as the combination of the first order and second order terms. Since the delta parallel robot is a second order process, it is an excellent platform to prove the effectiveness of the two proposed hypothesis.

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Risk evaluation in failure modes and effects analysis: hybrid TOPSIS and ELECTRE I solutions with Pythagorean fuzzy information

TL;DR: Two novel modified techniques, namely PFH-TOPSIS method and Pythagorean fuzzy hybrid Order of Preference by Similarity to an Ideal Solution method, are proposed to measure risk rankings in failure modes and effects analysis (FMEA) in order to overcome the flaws and shortcomings of traditional crisp risk priority numbers and fuzzy FMEA techniques.
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Integrated neuro-evolution-based computing solver for dynamics of nonlinear corneal shape model numerically

TL;DR: In this study, bio-inspired computational techniques have been exploited to get the numerical solution of a nonlinear two-point boundary value problem arising in the modelling of the corneal shape with reasonable precision and efficiency with minimal computational cost.
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Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting

TL;DR: It has been found out that in terms of both performance metrics, the proposed deep Bi-LSTM S2S day-ahead “peak” forecasting model has outperformed all the other models on both public holidays and normal days.
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Group decision-making framework using complex Pythagorean fuzzy information

TL;DR: A new strategy to address multi-criteria group decision-making problems named complex Pythagorean fuzzy VIKOR (CPF-VIKOR) method, designed to handle a great deal of vagueness and hesitation which are often present in human decisions.
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An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

TL;DR: The proposed approach is highly effective with clustering and also with classification of breast cancer and has been compared with other available fuzzy clustering methods to prove the efficacy.
References
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Journal ArticleDOI

A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network.

TL;DR: By comparing EDBN and DBN under different network structures, the results show that EDBN has better feature extraction and fault classification performance than traditional DBN.
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A robust and fixed-time zeroing neural dynamics for computing time-variant nonlinear equation using a novel nonlinear activation function

TL;DR: A robust and fixed-time zeroing neural dynamics model is proposed and analyzed for time-variant nonlinear equation (TVNE), and comparative results demonstrate the effectiveness, robustness, and advantage of the RaFT-ZND model for solving TVNE.
Journal ArticleDOI

Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network

TL;DR: A deep-learning algorithm was developed for an on-loom fabric defect inspection system by combining the techniques of image pre-processing, fabric motif determination, candidate defect map generation, and convolutional neural networks (CNNs).
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Neural-network-based iterative learning control of nonlinear systems

TL;DR: An iterative control update rule is developed through efficient data-driven scheme of neural network training to enhance the iterative learning scheme with neural networks applied for controller synthesis as well as for system output prediction.
Journal ArticleDOI

New results for sampled-data control of interval type-2 fuzzy nonlinear systems

TL;DR: Based on the Lyapunov–Krasovskii functional theory, a new relaxed sufficient condition with fewer linear matrix inequality (LMI) constraints is derived and the IT2 fuzzy sampled-data controller is devised to ensure the closed-loop system is asymptotically stable.
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