In the article, a new and efficient multilayer neural networks learning algorithm is presented. The key concept of this new algorithm is the two-stage implementation of the steepest descent method. At the first stage, it is used to search the optimal learning constant /spl eta/ and momentum term /spl alpha/ for each weights updating process. At the second stage, the Delta learning rule is then employed to modify the connecting weights in terms of the optimal /spl eta/ and /spl alpha/. Computer simulations show that the proposed new algorithm outmatches other learning algorithms both in convergence speed and success rate. On real industrial application, a self-tuning neural-network based PID controller for precise temperature control of an injection mode barrel system by using the developed algorithm is developed. Experiments show that the proposed self-tuning PID controller can precisely control the barrel temperature within /spl plusmn/0.5/spl deg/C.
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