Common activation functions include sigmoid, ReLU, and tanh, each with its own advantages and drawbacks depending on the application. In backpropagation, activation functions determine how errors are propagated backward through the network, influencing weight updates.
In this paper, we propose some activation functions designed to simplify the computational complexity and the hardware implementation of neural and fuzzy ...
In this paper, we propose some activation functions designed to simplify the computational complexity and the hardware implementation of neural and fuzzy neural ...
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These functions approximate the sigmoidal and the Gaussian shapes using only simple arithmetic operations and the simulation results show the good ...
A neural network activation function is a function that is applied to the output of a neuron. Learn about different types of activation functions and how they
Nov 19, 2024 · Explore the crucial role of activation functions in neural networks, including Sigmoid, Tanh, RELU, and Softmax.
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Activation functions are crucial in neural networks, determining how inputs are transformed into outputs. They introduce non-linearity, enabling models to ...
Oct 22, 2024 · A novel fuzzy-based activation function for artificial neural networks is proposed. This approach provides easy hardware implementation and ...
This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory ...
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Nov 6, 2024 · We propose two straightforward yet powerful adaptive activation functions: a weighted average function that adjusts activation functions by directly ...