The recent article continues research on neural networks, specifically focusing on supervised learning using activation functions. By implementing a multilayer perceptron (MLP) with an embedded ADAM optimization algorithm, the article evaluates how different activation functions affect interpolation accuracy and convergence rate in neural networks. The neural network employs the hyperbolic tangent and various other functions.

Key components of the MLP implementation include the C_Neuro class for neurons, the S_NeuronLayer structure for neuron layers, and methods for importing and exporting weights. The study tests the modified ADAMm optimization method against the classical ADAM to determine the impact of activation functions on training efficiency.

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