A feedforward neural network with function shape autotuning
Neural Networks
Multilayer perceptron network with modified sigmoid activation functions
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Fast learning process of multilayer neural networks using recursiveleast squares method
IEEE Transactions on Signal Processing
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
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The paper presents some novel methods of the activation function selection in the last hidden layer of a multilayer perceptron. For this selection, the least squares method is used. The proposed ways make it possible to decrease the cost function value. They enable achievement of a good compromise between the network complexity and the results being obtained. The methods do not require a start of learning of neural networks from the very beginning. They fit very well for improvement of the action of learnt multilayer perceptrons. They may be particularly useful for construction of the devices under microprocessor control, that have not a big memory nor computing power.