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Predictive control of rolling mills using neural network gauge estimation

  • US 5,586,221 A
  • Filed: 07/01/1994
  • Issued: 12/17/1996
  • Est. Priority Date: 07/01/1994
  • Status: Expired due to Fees
First Claim
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1. A control system for controlling a complex industrial process of the type having a plurality of nonlinear, time-varying states that are mutually coupled in an uncertain manner, the industrial process having a process input, and a process output that is dependent on said time-varying states, the industrial process being responsive to a control signal for changing said process output, the system comprising:

  • an artificial neural network having an input layer comprising a plurality of input nodes, said input nodes being coupled to state signals that are representative of said time-varying states at a current time and said neural network having a hidden layer and an output node for generating an output signal, said neural network being trained in a training cycle wherein said each state signal is delayed by a predetermined time when presented to said neural network with said process output, thereby sychronizing said process output with past state signals so that said output signal is predictive of said process output at a future time;

    comparator means coupled to said output signal and coupled to a reference signal for deriving an error signal that is representative of a difference therebetween; and

    control means responsive to said error signal for controlling said industrial process.

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