A framework for automatic detection of heart diseases using dynamic deep neural activation functions
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-020-01883-6
Abstract: Availability of the information related to the medical tests are wide and thus the demand for advanced analysis is continuously growing. In the recent past, a number of automatic disease detection algorithms are generated. The… read more here.
Keywords: detection; disease; activation functions; activation ... See more keywords
Global exponential stability of delayed complex-valued neural networks with discontinuous activation functions
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.02.006
Abstract: Abstract This paper studies the global exponential stability of delayed complex-valued neural networks with discontinuous activation functions. By introducing the complex-valued Filippov differential inclusion, we construct the framework of studying the dynamical behaviors of complex-valued… read more here.
Keywords: valued neural; neural networks; activation functions; networks discontinuous ... See more keywords
Multistability and instability of competitive neural networks with non-monotonic piecewise linear activation functions
Sign Up to like & getrecommendations! Published in 2019 at "Nonlinear Analysis: Real World Applications"
DOI: 10.1016/j.nonrwa.2018.08.005
Abstract: Abstract This paper addresses the issue of multistability for competitive neural networks. First, a general class of continuous non-monotonic piecewise linear activation functions is introduced. Then, based on the fixed point theorem, the contraction mapping… read more here.
Keywords: competitive neural; activation; non monotonic; neural networks ... See more keywords
Convolutional Neural Network-Based Skin Lesion Classification With Variable Nonlinear Activation Functions
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3196911
Abstract: One of the worst forms of skin cancer is melanoma which can be curable if it is diagnosed at an early stage. The earlier the cancer is diagnosed, the better is the outcome. The risk… read more here.
Keywords: activation functions; neural network; lesion; nonlinear activation ... See more keywords
Developing Novel Activation Functions Based Deep Learning LSTM for Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3205774
Abstract: This study proposes novel Long Short-Term Memory (LSTM)-based classifiers through developing the internal structure of LSTM neural networks using 26 state activation functions as alternatives to the traditional hyperbolic tangent (tanh) activation function. The LSTM… read more here.
Keywords: activation functions; functions based; classification; deep learning ... See more keywords
NeuroSCA: Evolving Activation Functions for Side-Channel Analysis
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2022.3232064
Abstract: The choice of activation functions can significantly impact the performance of neural networks. Due to an ever-increasing number of new activation functions being proposed in the literature, selecting the appropriate activation function becomes even more… read more here.
Keywords: activation functions; channel analysis; functions side; side channel ... See more keywords
Programmable Tanh-, ELU-, Sigmoid-, and Sin-Based Nonlinear Activation Functions for Neuromorphic Photonics
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Journal of Selected Topics in Quantum Electronics"
DOI: 10.1109/jstqe.2023.3277118
Abstract: We demonstrate a programmable analog opto-electronic (OE) circuit that can be configured to provide a range of nonlinear activation functions for incoherent neuromorphic photonic circuits at up to 10 Gbaud line-rates. We present a set… read more here.
Keywords: activation functions; photonics; nonlinear activation; sine squared ... See more keywords
Sigmoid and Beyond: Algebraic Activation Functions for Artificial Neural Networks Based on Solutions of a Riccati Equation
Sign Up to like & getrecommendations! Published in 2022 at "IT Professional"
DOI: 10.1109/mitp.2022.3204904
Abstract: Activation functions play a key role in neural networks, as they significantly affect the training process and the network’s performance. Based on the solution of a certain ordinary differential equation of the Riccati type, this… read more here.
Keywords: activation functions; neural networks; equation; sigmoid ... See more keywords
Multistability for Almost-Periodic Solutions of Takagi–Sugeno Fuzzy Neural Networks With Nonmonotonic Discontinuous Activation Functions and Time-Varying Delays
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Fuzzy Systems"
DOI: 10.1109/tfuzz.2019.2955886
Abstract: This article investigates the problem of multistability of almost-periodic solutions of Takagi–Sugeno fuzzy neural networks with nonmonotonic discontinuous activation functions and time-varying delays. Based on the geometrical properties of nonmonotonic activation functions, by using the… read more here.
Keywords: sugeno fuzzy; takagi sugeno; almost periodic; periodic solutions ... See more keywords
Multistability of Switched Neural Networks With Piecewise Linear Activation Functions Under State-Dependent Switching
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2018.2876711
Abstract: This paper is concerned with the multistability of switched neural networks with piecewise linear activation functions under state-dependent switching. Under some reasonable assumptions on the switching threshold and activation functions, by using the state-space decomposition… read more here.
Keywords: tex math; switched neural; neural networks; inline formula ... See more keywords
A Formal Characterization of Activation Functions in Deep Neural Networks.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3187538
Abstract: In this article, a mathematical formulation for describing and designing activation functions in deep neural networks is provided. The methodology is based on a precise characterization of the desired activation functions that satisfy particular criteria,… read more here.
Keywords: activation functions; neural networks; functions deep; methodology ... See more keywords