Dear Dr. Alkadhim,

great question. Maybe the following website is helpful:

http://mlg.eng.cam.ac.uk/tutorials/06/es.pdf

Yours sincerely,

Rainer

Thanks Rainer , i want to implement an example for more explanation.

For Gaussian process models; the best way to understand how to use these models for linear and nonlinear systems is KF and EKF respectively. In both cases the Kalman filtering conditions have to fit correctly to our problem. Then we have to determine the necessary parameters and the initial values of the filter in order to iterate through the estimates. This is a probabilistic based approach (with Gaussian distribution); these filters are normally used for decision making problems or to solve a problem like SLAM where the environment is mostly dynamic and sometimes stochastic.

Neural Networks; is an adaptive mechanism that enables computer to learn from its experiences. The environment here is mostly deterministic (The training models to be memorised are normally limited). The knowledge here is stored as synaptic weights between neurons (it is not a dynamic model of a system) in other words the knowledge is embedded in the entire network; it cannot be broken into individual pieces, and any change of a synaptic weight may lead to unpredictable results (While using probabilistic rules; the knowledge can be divided into individual rules and the user can see and understand the piece of knowledge applied by the system). Finally; some Neural Networks can learn with or without human intervention (we speak about Self-organizing neural networks). Neural Networks are mostly used for achieving classification; ex. pattern recognition problems.

This is my own point of view for both of them. You can find a lot of ideas about them on the websites.

Thanks for your question

I have no information about your question

  Greetings