Single multiplicative neuron model based on generalized mean

Attia, Mohamed A.; Sallam, Elsayed A.; Fahmy, Mahmoud

doi:10.1109/icces.2012.6408495

Cited by 2 publications

References 33 publications

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A new statistical training algorithm for a single multiplicative neuron model artificial neural network

Gul,

Egrioglu,

Bas

2024

Granul. Comput.

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The single multiplicative neuron model has been frequently used by researchers in recent years, as it does not have a complex structure and does not include the hidden layer unit number problem, unlike many feed-forward arti cial neural network models. The model of single multiplicative neuron model arti cial neural networks does not have statistical assumptions just like in many arti cial neural network models. Random error term is not used in the mathematical model of single multiplicative neuron model arti cial neural networks. This situation is not acceptable considering that arti cial neural networks work with random samples. Based on this idea, for the rst time, by including a random error term in the single multiplicative neuron model arti cial neural network model, mathematical equations of likelihood functions are given for Normal, Cauchy, Logistic, Gumbel and Laplace distributions. A new statistical training algorithm is proposed to obtain optimal weights and bias values of the network. In the new training algorithm, particle swarm optimization proposed by Kennedy and Eberhart (1995) is used in maximizing likelihood functions. In the performance evaluation of the proposed method, Nasdaq and S&P500 time series in different years are analyzed and the analysis results are compared with many arti cial neural network models in the literature. Finally, it is concluded that the proposed method produces very successful forecasting results.

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A new statistical training algorithm for a single multiplicative neuron model artificial neural network

Gul,

Egrioglu,

Bas

2024

Granul. Comput.

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CMOS-memristor dendrite threshold circuits

Zhanbossinov

Smagulova

James

2016

2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

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Non-linear neuron models overcomes the limitations of linear binary models of neurons that have the inability to compute linearly non-separable functions such as XOR. While several biologically plausible models based on dendrite thresholds are reported in the previous studies, the hardware implementation of such non-linear neuron models remain as an open problem. In this paper, we propose a circuit design for implementing logical dendrite non-linearity response of dendrite spike and saturation types. The proposed dendrite cells are used to build XOR circuit and intensity detection circuit that consists of different combinations of dendrite cells with saturating and spiking responses. The dendrite cells are designed using a set of memristors, Zener diodes, and CMOS NOT gates. The circuits are designed, analyzed and verified on circuit boards.

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Single multiplicative neuron model based on generalized mean

Cited by 2 publications

References 33 publications

A new statistical training algorithm for a single multiplicative neuron model artificial neural network

A new statistical training algorithm for a single multiplicative neuron model artificial neural network

CMOS-memristor dendrite threshold circuits

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