Hi,

This set of questions are related to system identification of a nonlinear system with artificial neural network (ANN) followed by its control.
Our system consist of two input vectors namely I1, I2 and an output vector
O.
'I1(T)' is the external control inputs to the system at time 'T',
'I2(T)' is the state of the system at time 'T',
'O(T)' is the state of the system at time 'T'
The system can be defined as a function 'F'
O(T+1) = F(I1(T), I2(T))
which means that the next state of the system is a function of its current
state and control inputs.
where
I2(T) = O(T-1)
ie.
O(T+1) = F( I1(T), O(T-1))
or
O(T) = F(I1(T-1), I2(T-1) )

We have loged data set of I1(T), I2(T), O(T) from the actual system observations.

Can someone suggest a particular ANN type which can be trained to learn O(T+1) = F(I1(T), I2(T)) (ie. system identification with ANN)?

Can we use this ANN to find I1(T) given I2(T) and O(T+1) ie. how we can use this ANN to the required I1(T) if we know the current state I2(T) and desired next state O(T+1) of the system?

I1(T) = X(I2(T),O(T+1)) ==> X=?

I1(T) ====>| | | F |===>O(T+1) I2(T) ====>| |

O(T+1) ====>| | | X |===>I1(T) I2(T) ====>| |

This set of questions are related to system identification of a nonlinear system with artificial neural network (ANN) followed by its control.

We have loged data set of I1(T), I2(T), O(T) from the actual system observations.

Can someone suggest a particular ANN type which can be trained to learn O(T+1) = F(I1(T), I2(T)) (ie. system identification with ANN)?

Can we use this ANN to find I1(T) given I2(T) and O(T+1) ie. how we can use this ANN to the required I1(T) if we know the current state I2(T) and desired next state O(T+1) of the system?

I1(T) = X(I2(T),O(T+1)) ==> X=?

I1(T) ====>| | | F |===>O(T+1) I2(T) ====>| |

O(T+1) ====>| | | X |===>I1(T) I2(T) ====>| |