Computer neural network supervisory process control system and m

Details Computer neural network supervisory process control system and m Computer neural network supervisory process control system and m

1990-08-03

1992-08-25

MacDonald, Allen R.

Boots, shoes, and leggings

395 22, 395 23, 395906, 395 68, 364149, G06F 1518

Patent

active

051426129

ABSTRACT:
A neural network for adjusting a setpoint in process control replaces a human operator. The neural network operates in three modes: training, operation, and retraining. In operation, the neural network is trained using training input data along with input data. The input data is from the sensor(s) monitoring the process. The input data is used by the neural network to develop output data. The training input data are the setpoint adjustments made by a human operator. The output data is compared with the training input data to produce error data, which is used to adjust the weights of the neural network so as to train it. After training has been completed, the neural network enters the operation mode. In this mode, the present invention uses the input data to predict output data used to adjust the setpoint supplied to the regulatory controller. Thus, the operator is effectively replaced. The present invention in the retraining mode utilizes new training input data to retrain the neural network by adjusting the weight(s).

REFERENCES:
Sanner et al., "Neuromorphic Pitch Attitude Regulation of An Underwater Telerobot", IEEE Control Systems Magazine, Apr. 1990, pp. 62-67.
Lippmann, "An Introduction to Computing with Neural Nets", IEEE ASSP Magazine, Apr. 1987, pp. 4-21.
Guez et al., "Neuromorphic Controller with a Human Teacher", IEEE Internat. Conf. of Neural Networks, 1988, vol. II, pp. 595-602.
Liv, "Building A General Architecture for Robot Handy Control", IEEE Internat. Conf. on Neural Networks, 1988, vol. 11, pp. 567-574.
Narendra et al., "Identification and Control of Dynamical Systems Using Neural Networks", IEEE Trans. on Neural Networks, vol. 1, No. 1 Mar. 1990, pp. 4-27.
Guez et al., "Neural Network Architecture for Control", IEEE Control Systems Magazine, Apr. 1988, pp. 22-25.
Chen, "Back-Propagation Neural Networks for Nonlinear Self-Tuning Adaptive Control", IEEE Control Systems Magazine, Apr. 1990, pp. 44-48.
Elsley, "A Learning Architecture for Control Based on Back-Propagation Neural Networks", Proc. IEEE Int'l Conf. on Neural Networks, pp. II-587-594.
Bhat et al., "Use of Neural Nets for Dynamic Modeling and Control of Chemical Process Systems", 1989 American Control Conf., pp. 1342-1347.
Hoskins et al., "Fault Diagnosis in Complex Chemical Plants Using Artifical Neural Networks", AICLE Journal, Jan. 1991, vol. 37, No. 1, pp. 137-141.
Astrom, "Toward Intelligent Control", IEEE Control Systems Magazine, Apr. 1989, pp. 60-64.
Bavarian, "Introduction to Neural Networks for Intelligent Control", IEEE Control Systems Magazine, Apr. 1988, pp. 3-7.
Psaltis et al., "A Multilayered Neural Network Controller", IEEE Control Systems Magazine, Apr. 1988, pp. 17-21.
Kraft et al. "A Comparison Between CMAC Neural Network Control and Two Adaptive Control Systems", IEEE Control Systems Magazine, Apr. 1990, pp. 36.varies.43.
Josin et al., "Robot Control Using Neural Networks", IEEE Internat. Conf. on Neural Networks, 1988, vol. II, pp. 625-631.
Liu et al. "Neural Network Architecture for Robot Hand Control", IEEE Control Systems Magazine, Apr. 1989, pp. 38-43.
Kuperstein et al., "Implementation of an Adaptive Neural Controller for Sensory-Motor Coordination", IEEE Control Sys. Mag., Apr. 1989, pp. 25-30.
Kuperstein et al., "Neural Controller for Adaptive Movements with Unforseen Payloads", IEEE Trans. on Neural Networks, vol. 1, No. 1, Mar. 1990, pp. 137-142.
Kung et al., "Neural Network Architectures for Robotic Applications", IEEE Trans. on Robotics and Automation, vol. 5, No. 5, Oct. 1989, pp. 641-657.
Parallel Distributed Processing, Explorations in the Microstructure of Cognition, by David E. Rumelhart and James L. McClelland, the MIT Press, Cambridge, Mass., USA, 1986.
Explorations In Parallel Distributed Processing, A Handbook of Models, Programs, and Exercises, by James L. McClelland and David E. Rumelhart, the MIT Press, Cambridge, MA, 1988.
Bhaget, An Introduction to Neural Nets, Aug. 1990.
Ballou, Technological Mix to Fuel Neural Network growth, Aug. 13, 1990.
Samdani, Neural Nets They Learn From Example, Aug. 1990.
Venkatasubrananian et al., Process Fault Detection And Diagnosis Using Neural Networks--I. Steady-State Processes, Feb. 15, 1990.
Ungar et al., Adaptive Networks For Fault Diagnosis And Process Control, Jan. 15, 1990.
Bhat et al., Use of Neural Nets For Dynamic Modeling And Control of Chemical Process Systems, Nov. 29, 1989.
Ydstie, Foresting And Control Using Adaptive Connectionist Networks, 1990.
Venkatasubramanian et al., A Neural Network Methodology For Process Fault Diagnosis, Dec. 1989.
Watanabe et al., Incipient Fault Diagnosis of Chemical Process via Artifical Neural Networks, 1989.
Hoskins et al., Artificial Neural Network Models of Knowledge Representation in Chemical Engineering, 1988.
Graft et al., A Neural Controller for Collison-Free Movement of General Robot Manipulators, Apr. 1988.
Sobajic et al., Robust Control of Nonlinear Systems Using Pattern Recognition, IEEE International Conference on Systems, Man, and Cybernetics, vol. 1, pp. 315-320, (1989).
Lant et al., A Comparison of Adaptive Estimation with Neural Based Techniques for Bioprocess Application, Proceedings of the 1990 American Control Conference, vol. 3, pp. 2173-2178, (1990).
Bhat et al., Use of Neural Nets for Dynamic Modeling and Control of Chemical Process Systems, 1989 American Control Conference, Pittsburgh (Jun. 21-23, 1989).
Sobajic et al., Intelligent Control of the Intelledex 605T Robot Manipulator, pp. 633-640, IEEE Internatiional Conference, San Diego (Jul. 24-27, 1988).
Guez et al., Neuromorphic Controller with a Human Teacher, pp. 595-602, IEEE International Conference San Diego (Jul. 24-27, 1988).
Guez et al., Neuromorphic Architectures for Fast Adaptive Robot Control, pp. 145-149, 1988 IEEE Int'l Conference (Apr. 24-29, 1988).
Shepanski et al., Teaching Artifical Neural Systems to Drive: Manual Training Techniques, pp. 231-238 Annual Workshop of SOA and Robotics (Aug. 5-7, 1987).

Affiliated with

Also associated with

Rating

Computer neural network supervisory process control system and m does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Computer neural network supervisory process control system and m, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Computer neural network supervisory process control system and m will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-391628