Learnable Leaky ReLU (LeLeLU): An Alternative Accuracy-Optimized Activation Function
Information, 2021 In neural networks, a vital component in the learning and inference process is the activation function. There are many different approaches, but only nonlinear activation functions allow such networks to compute non-trivial problems by using only a small
Andreas Maniatopoulos +1 more
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A novel zeroing neural network for dynamic sylvester equation solving and robot trajectory tracking
Frontiers in Physics, 2023 To solve the theoretical solution of dynamic Sylvester equation (DSE), we use a fast convergence zeroing neural network (ZNN) system to solve the time-varying problem.
Lv Zhao +6 more
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Dynamic Neural Network Models for Time-Varying Problem Solving: A Survey on Model Structures
IEEE Access, 2023 In recent years, neural networks have become a common practice in academia for handling complex problems. Numerous studies have indicated that complex problems can generally be formulated as a single or a set of time-varying equations.
Cheng Hua +4 more
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Design of Activation Functions for Inference of Fuzzy Cognitive Maps: Application to Clinical Decision Making in Diagnosis of Pulmonary Infection [PDF]
Healthcare Informatics Research, 2012 ObjectivesFuzzy cognitive maps (FCMs) representing causal knowledge of relationships between medical concepts have been used as prediction tools for clinical decision making.
In Keun Lee, Hwa Sun Kim, Hune Cho
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Intrusion Detection System Based on Gradient Corrected Online Sequential Extreme Learning Machine
IEEE Access, 2021 Nowadays, Intrusion Detection System (IDS) is an active research topic with machine learning nature. A single-hidden layer feedforward neural network (SLFN) trained on the approach of extreme learning machine (ELM) is used for (IDS).
Nedhal Ahmad Hamdi Qaiwmchi +2 more
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Robust Image Classification Using a Low-Pass Activation Function and DCT Augmentation
IEEE Access, 2021 Convolutional Neural Network’s (CNN’s) performance disparity on clean and corrupted datasets has recently come under scrutiny. In this work, we analyse common corruptions in the frequency domain, i.e., High Frequency corruptions (HFc, e.g.,
Md Tahmid Hossain +3 more
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Target detection based on a new triple activation function
Systems Science & Control Engineering, 2022 As one of the important parts of Neural Network, activation function plays a very important role in model training in Neural Network. In this paper, the status quo, advantages and disadvantages of the existing common activation functions are analysed ...
Guanyu Chen +3 more
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Lightweight Tennis Ball Detection Algorithm Based on Robomaster EP
Applied Sciences, 2023 To address the problems of poor recognition effect, low detection accuracy, many model parameters and computation, complex network structure, and unfavorable portability to embedded devices in traditional tennis ball detection algorithms, this study ...
Yuan Zhao +4 more
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Method for Segmentation of Banana Crown Based on Improved DeepLabv3+
Agronomy, 2023 As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device.
Junyu He +9 more
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Deep Physical Informed Neural Networks for Metamaterial Design
IEEE Access, 2020 In this paper, we propose a physical informed neural network approach for designing the electromagnetic metamaterial. The approach can be used to deal with various practical problems such as cloaking, rotators, concentrators, etc.
Zhiwei Fang, Justin Zhan
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