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Is there a python module to solve/integrate a system of stochastic differential equations?

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问题 I have a system of stochastic differential equations that I would like to solve. I was hoping that this issue was already address. I am a bit concerned about constructing my own solver because I fear my solver would be too slow, and there could be the issues with numerical stability. Is there a python module for such problems? If not, is there a standard approach for solving such systems. 回答1: There is one: http://diffusion.cgu.edu.tw/ftp/sde/ Example from the site: """ add required Python

passed parameters to boost odeint in C++

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问题 This answer is helpful, but I would like to know how to pass multiple parameters of different types to the ODE model, perhaps in a struct. For my immediate use case, I need to be able to pass one std::array<double, 6> , two std::vector<std::vector<double>> and two two double scalars for a total of four parameters to be passed. In the linked example, as well as in harmonic_oscillator.cpp, there is only a single double passed parameter. Thanks. Here's an example of the struct I would need

Is there a python module to solve/integrate a system of stochastic differential equations?

阅读更多关于 Is there a python module to solve/integrate a system of stochastic differential equations?

I have a system of stochastic differential equations that I would like to solve. I was hoping that this issue was already address. I am a bit concerned about constructing my own solver because I fear my solver would be too slow, and there could be the issues with numerical stability. Is there a python module for such problems? If not, is there a standard approach for solving such systems. There is one: http://diffusion.cgu.edu.tw/ftp/sde/ Example from the site: """ add required Python packages """ from pysde import * from sympy import * """ Variables acclaimed """ x,dx=symbols('x dx') r,G,e,d

Simulink: PID Controller - difference between back-calculation and clamping for anti-windup?

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问题 I need to implement an anti-windup (output limitation) for my PID controller. Simulink is offering two options: back calculation and clamping (documentation) which seem to deliver equal results. I know what back calculation is doing mathematically. It requires to define the back-calculation gain Kb . This gain is dependent on how long my controller is saturated, therefore it is actually a dynamic value (because I may have a high variation of saturation times). Do you see a way to control this