12-06-2014, 12:53 PM
NEURAL NETWORKS IN PROCESS CONTROL
NEURAL NETWORKS.PPT (Size: 88.5 KB / Downloads: 18)
ABSTRACT
The effectiveness of operation of process is decided by the level of control associated with it. Over a period of time necessity is being felt to make the control technique intelligent to increase the effectiveness of the control strategies. In order to achieve the above objective we can in cooperate the intelligence features in the control algorithms to make it adaptive with time and take care of all variation and fluctuation by in cooperating the elements of intelligence using ANN. If it try to realize it analytically we will be handle conveniently ect . With the advent of computers it is possible to develop intelligent control systems for dedicated and shared application in process industry.
CONCLUSION
This paper present the state of ANN in process control applications. The
Ability of MLNN to model arbitrary non linear process is used for the
identification and control of a complex process. Since the unknown
Complex system are online modeled. And are controlled by the
input_output dependent neural networks,the control mechanism are robust
For for varying system parameters.
it is found that the MLNN with EBP training algorithm are best
suited for identification and control since the learning is of supervised nature
And can handle the nonlinearity present in the plants with only input_output
Information. however, there are difficulties in implementing MLNN with EBP.
Like selection of learning rate, momentum factor, selection of network size etc
thus it becomes very much essential to have some concrete guard
Lines for selecting the network. further ,there is lot of scope in developing
Different effective configurations based on ANN for identification and control
of the complex process.