# brainpy.integrators.fde.Caputo.CaputoL1Schema#

class brainpy.integrators.fde.Caputo.CaputoL1Schema(f, alpha, num_step, inits, dt=None, name=None, state_delays=None)[source]#

The L1 scheme method for the numerical approximation of the Caputo fractional-order derivative equations 3.

For the fractional order $$0<\alpha<1$$, let the fractional derivative of variable $$x(t)$$ be

$\frac{d^{\alpha} x}{d t^{\alpha}}=F(x, t)$

The Caputo definition of the fractional derivative for variable $$x$$ is

$\frac{d^{\alpha} x}{d t^{\alpha}}=\frac{1}{\Gamma(1-\alpha)} \int_{0}^{t} \frac{x^{\prime}(u)}{(t-u)^{\alpha}} d u$

where $$\Gamma$$ is the Gamma function.

The fractional-order derivative is capable of integrating the activity of the function over all past activities weighted by a function that follows a power-law. Using one of the numerical methods, the L1 scheme method 3, the numerical approximation of the fractional-order derivative of $$x$$ is

$\frac{d^{\alpha} \chi}{d t^{\alpha}} \approx \frac{(d t)^{-\alpha}}{\Gamma(2-\alpha)}\left[\sum_{k=0}^{N-1}\left[x\left(t_{k+1}\right)- \mathrm{x}\left(t_{k}\right)\right]\left[(N-k)^{1-\alpha}-(N-1-k)^{1-\alpha}\right]\right]$

Therefore, the numerical solution of original system is given by

$x\left(t_{N}\right) \approx d t^{\alpha} \Gamma(2-\alpha) F(x, t)+x\left(t_{N-1}\right)- \left[\sum_{k=0}^{N-2}\left[x\left(t_{k+1}\right)-x\left(t_{k}\right)\right]\left[(N-k)^{1-\alpha}-(N-1-k)^{1-\alpha}\right]\right]$

Hence, the solution of the fractional-order derivative can be described as the difference between the Markov term and the memory trace. The Markov term weighted by the gamma function is

$\text { Markov term }=d t^{\alpha} \Gamma(2-\alpha) F(x, t)+x\left(t_{N-1}\right)$

The memory trace ($$x$$-memory trace since it is related to variable $$x$$) is

$\text { Memory trace }=\sum_{k=0}^{N-2}\left[x\left(t_{k+1}\right)-x\left(t_{k}\right)\right]\left[(N-k)^{1-\alpha}-(N-(k+1))^{1-\alpha}\right]$

The memory trace integrates all the past activity and captures the long-term history of the system. For $$\alpha=1$$, the memory trace is 0 for any time $$t$$. When the fractional order $$\alpha$$ is decreased from 1, the memory trace non-linearly increases from 0, and its dynamics strongly depends on time. Thus, the fractional order dynamics strongly deviates from the first order dynamics.

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
>>>
>>> duration = 30.
>>> dt = 0.005
>>> inits = [1., 0., 1.]
>>> f = bp.fde.CaputoL1Schema(lorenz, alpha=0.99, num_step=int(duration / dt), inits=inits)
>>> runner = bp.integrators.IntegratorRunner(f, monitors=list('xz'), dt=dt, inits=inits)
>>> runner.run(duration)
>>>
>>> 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 total time step of the simulation.

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

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

• name (str) – The integrator name.

References

3(1,2)

Oldham, K., & Spanier, J. (1974). The fractional calculus theory and applications of differentiation and integration to arbitrary order. Elsevier.

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

Methods

 __init__(f, alpha, num_step, inits[, dt, ...]) hists([var, numpy]) Get the recorded history values. 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. 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. 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.