Date of publication: 2017-09-03 06:38
I would like to know what programming language is better for NLP to starting with? I have found many of questions and answers in this regard. But I am still lost in choosing which one to use.
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I have tried to find resources about one-dimensional spatial point process but I have not gotten. I need to analyse one-dimensional spatial point process using R language and I need to know theory references about it.
Constraints make OCP in MPC different from other dynamic programming problems, and I don't know how can traditional methods for dynamic programming (like HJB equations) deal with these constraints. Constraints themselves can be nonlinear.
I find lane-based microscopic simulation models inadequate for modelling realistic trajectories of human driven vehicles and highly automated vehicles that follow a 7D path. Any suggestions or ideas are welcome.
Directed to Dr. Seyed Hamed Hashemi Mehne, Shvetsov, Parsapoor and Kumar, Yes Sir, pardon me first for late response.
I got data for all of them. I even got the PSO codings that i will be implementing in Scilab but for that i will need an equation which i had somewhat tried to implement.
I tried to formulate them one by one and also added the standard wiener process.
If i could have all of your opinions on it, it would be grateful.
And to Dr. Soumitra K Mallick, I am a Student only not a Doctor yet, Thank you though. :)
I will definitely read upon the materials you listed.
Dynamic programming was the brainchild of an American Mathematician, Richard Bellman, who described the way of solving problems where you need to find the best decisions one after another. In the forty-odd years since this development, the number of uses and applications of dynamic programming has increased enormously.
Instead, a mathematical way of thinking about it is to look at what you should do at the end, if you get to that stage. So you think about the best decision with the last potential partner (which you must choose) and then the last but one and so on. This way of tackling the problem backwards is Dynamic programming.
It means that you consider power losses as a utility and voltage as a control policy and solve an optimization problem dynamically. I suspect, approximately since "standard" DP works backwards in time, while ADP is forward in time (suitable for online applications). This is only my suggestion, I can't tell exactly without looking at the paper.
The interpolation relation in dynamic programming in optimal control for a discrete constrained LQR system is discussed in the article entitled:"On infinite horizon switched LQR problems with state and control constraints" by Maximilian Balandat, Wei Zhang, and Alessandro Abate.