A Multi-strategy Differential Evolution Algorithm for Financial Prediction with Single Multiplicative Neuron

Single multiplicative neuron model based on generalized mean

“…This model is based on a polynomial structure. The simulation results show the effectiveness of the neuron model [2,14,13,17]. The SMN neuron model is based on the concept of arithmetic mean of the multiplicative inputs.…”

“…The SMN model is first proposed in [2] for solving various problems in [12][13][14][15][16][17]. This model is based on a polynomial structure.…”

“…These neuron models include an aggregation function based on the concept of generalized mean of all inputs. In [2] the authors presented a single multiplicative neuron (SMN) model based on a polynomial architecture, and its application for solving various problems in [12][13][14][15][16][17]. Unlike the higher-order neuron model, the SMN model has a simple non-linear aggregation function.…”

Error estimation for reconstruction of neuronal spike firing from fast calcium imaging

“…The differential algorithm is a simple and classical method to rebuild spike trains, but it is incredibly sensitive to noise [12,23]. We improved this method by only analyzing the differential signals that corresponded to the rising segment of the Ca 2+ transient (named local differential method, LDM).…”

“…Various reconstruction methods have been developed to date and significant progress has been achieved [5,[7][8][9][10][11][12][13][14]. However, there is no error estimation method that evaluates the corresponding reconstruction results associated with unclear nonlinearities in the spikecalcium relationship [9,15,16], contamination of signals from other cellular parts [4], system noise [17], and the often relatively low temporal resolution of the calcium signal recording compared to the electrical signal [14], along with the lack of a strict mathematical model describing the relationship between the Ca 2+ trace and spike firing (especially burst firing) [18,19].…”