This free podcast describes the concepts of self-interest and competition in a market economy, and their importance as the invisible hand that guides the economy. Branches:
Radial basis function network. In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control.
Because the FRF response is normalized to the input , the peaks in the resulting FRF function are resonant frequencies of the test object. Imaginary FRFs and Mode Shapes A FRF is a complex function which contains both an amplitude (the ratio of the input force to the response, for example: g/N) and phase (expressed in degrees, which indicates whether the response moves in and out of phase with the input).