# brainpy.integrators.ode.explicit_rk.Ralston4#

class brainpy.integrators.ode.explicit_rk.Ralston4(f, var_type=None, dt=None, name=None, show_code=False, state_delays=None, neutral_delays=None)[source]#

Ralston’s fourth-order method for ODEs.

It has the characteristics of:

• method stage = 4

• method order = 4

• Butcher Tables:

$\begin{split}\begin{array}{c|cccc} 0 & 0 & 0 & 0 & 0 \\ .4 & .4 & 0 & 0 & 0 \\ .45573725 & .29697761 & .15875964 & 0 & 0 \\ 1 & .21810040 & -3.05096516 & 3.83286476 & 0 \\ \hline & .17476028 & -.55148066 & 1.20553560 & .17118478 \end{array}\end{split}$

References

1

Ralston, Anthony (1962). “Runge-Kutta Methods with Minimum Error Bounds”. Math. Comput. 16 (80): 431–437. doi:10.1090/S0025-5718-1962-0150954-0

__init__(f, var_type=None, dt=None, name=None, show_code=False, state_delays=None, neutral_delays=None)#

Methods

 __init__(f[, var_type, dt, name, show_code, ...]) build() load_states(filename[, verbose]) Load the model states. nodes([method, level, include_self]) Collect all children nodes. register_implicit_nodes(nodes) register_implicit_vars(variables) save_states(filename[, variables]) Save the model states. set_integral(f) Set the integral function. train_vars([method, level, include_self]) The shortcut for retrieving all trainable variables. unique_name([name, type_]) Get the unique name for this object. vars([method, level, include_self]) Collect all variables in this node and the children nodes.

Attributes

 A B C arg_names arguments All arguments when calling the numer integrator of the differential equation. dt The numerical integration precision. integral The integral function. name neutral_delays neutral delays. parameters The parameters defined in the differential equation. state_delays State delays. variables The variables defined in the differential equation.