# brainpy.integrators.fde.GL.GLShortMemory#

class brainpy.integrators.fde.GL.GLShortMemory(f, alpha, inits, num_memory=None, num_step=None, dt=None, name=None, state_delays=None)[source]#

Efficient Computation of the Short-Memory Principle in Grünwald-Letnikov Method 1.

According to the explicit numerical approximation of Grünwald-Letnikov, the fractional-order derivative $$q$$ for a discrete function $$f(t_K)$$ can be described as follows:

${{}_{k-\frac{L_{m}}{h}}D_{t_{k}}^{q}}f(t_{k})\approx h^{-q} \sum\limits_{j=0}^{k}C_{j}^{q}f(t_{k-j})$

where $$L_{m}$$ is the memory lenght, $$h$$ is the integration step size, and $$C_{j}^{q}$$ are the binomial coefficients which are calculated recursively with

$C_{0}^{q}=1,\ C_{j}^{q}=\left(1- \frac{1+q}{j}\right)C_{j-1}^{q},\ j=1,2, \ldots k.$

Then, the numerical solution for a fractional-order differential equation (FODE) expressed in the form

$D_{t_{k}}^{q}x(t_{k})=f(x(t_{k}))$

can be obtained by

$x(t_{k})=f(x(t_{k-1}))h^{q}- \sum\limits_{j=1}^{k}C_{j}^{q}x(t_{k-j}).$

for $$0 < q < 1$$. The above expression requires infinity memory length for numerical solution since the summation term depends on the discritized time $$t_k$$. This implies relatively high simulation times.

To reduce the computational time, the upper bound of summation needs to be modified by $$k=v$$, where

$\begin{split}v=\begin{cases} k, & k\leq M,\\ L_{m}, & k > M. \end{cases}\end{split}$

This is known as the short-memory principle, where $$M$$ is the memory window with a width defined by $$M=\frac{L_{m}}{h}$$. As was reported in 2, the accuracy increases by increaing the width of memory window.

Examples

>>> import brainpy as bp
>>>
>>> a, b, c = 10, 28, 8 / 3
>>> def lorenz(x, y, z, t):
>>>   dx = a * (y - x)
>>>   dy = x * (b - z) - y
>>>   dz = x * y - c * z
>>>   return dx, dy, dz
>>>
>>> integral = bp.fde.GLShortMemory(lorenz,
>>>                                 alpha=0.96,
>>>                                 num_step=500,
>>>                                 inits=[1., 0., 1.])
>>> runner = bp.integrators.IntegratorRunner(integral,
>>>                                          monitors=list('xyz'),
>>>                                          inits=[1., 0., 1.],
>>>                                          dt=0.005)
>>> runner.run(100.)
>>>
>>> import matplotlib.pyplot as plt
>>> plt.plot(runner.mon.x.flatten(), runner.mon.z.flatten())
>>> plt.show()

Parameters
• f (callable) – The derivative function.

• alpha (int, float, jnp.ndarray, bm.ndarray, sequence) – The fractional-order of the derivative function. Should be in the range of (0., 1.).

• num_step (int) –

The length of the short memory.

• num_memory (int) –

The length of the short memory.

Changed in version 2.1.11: Please use num_step instead. num_memory will be no longer supportted since 2.2.0.

• inits (sequence) – A sequence of the initial values for variables.

• dt (float, int) – The numerical precision.

• name (str) – The integrator name.

References

1

Clemente-López, D., et al. “Efficient computation of the Grünwald-Letnikov method for arm-based implementations of fractional-order chaotic systems.” 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2019.

2

M. F. Tolba, A. M. AbdelAty, N. S. Soliman, L. A. Said, A. H. Madian, A. T. Azar, et al., “FPGA implementation of two fractional order chaotic systems”, International Journal of Electronics and Communications, vol. 78, pp. 162-172, 2017.

__init__(f, alpha, inits, num_memory=None, num_step=None, dt=None, name=None, state_delays=None)[source]#

Methods

 __init__(f, alpha, inits[, num_memory, ...]) 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) reset(inits) Reset function of the delay 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

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