Patent
1990-08-03
1993-03-23
MacDonald, Allen R.
395 23, 395 68, 395906, G06F 1518
Patent
active
051971143
ABSTRACT:
A computer neural network regulatory process control system and method allows for the elimination of a human operator from real time control of the process. The present invention operates in three modes: training, operation (prediction), and retraining. In the training mode, training input data is produced by the control adjustment made to the process by the human operator. The neural network of the present invention is trained by producing output data using input data for prediction. The output data is compared with the training input data to produce error data, which is used to adjust the weight(s) of the neural network. When the error data is less than a preselected criterion, training has been completed. In the operation mode, the neutral network of the present invention provides output data based upon predictions using the input data. The output data is used to control a state of the process via an actuator. In the retraining mode, retraining data is supplied by monitoring the supplemental actions of the human operator. The retraining data is used by the neural network for adjusting the weight(s) of the neural network.
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 Competing with Neural Nets", IEEE ASSP Magazine, Apr. 1987, pp. 4-21.
Guez et al., "A Neuromorphic Controller with a Human Teacher", IEEE International Conf. on Neural Networks, 1988, pp. 595-602.
Liu et al., "Building a Generic Architecture for Robot Hand Control", IEEE Internat. Conf. on Neural Networks, 1988, pp. 567-574.
Bhat, "Use of Neural Nets for Dynamic Modelling and Control of Chemical Process Systems", 1989 American Control Conf., pp. 1342-1347.
Hoskins et al., "Fault Diagnosis in Complex Chemical Plants Using Artificial Neural Networks", AICHE 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.
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, "Backpropagation 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", IEEE Internal Conf. of Neural Networks, 1988, pp. II-587-594.
Psaltis et al., "A Multilayed Neural Network Controller", IEEE Control Systems Magazine, Apr. 1988, pp. 17-21.
Kraft et al, "A Comparison Between CMAC Neural Network Control and Two Traditional Adaptive Control Systems", IEEE Control Sys. Mag., Apr. 1990, pp. 36-43.
Josin et al.,. "Robot Control Using Neural Networks", IEEE International Conference 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 Systems Magazine, Apr. 1989, pp. 25-30.
Kuderstein et al., "Neural Controller for Adaptive Movements with Unfereseten 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.
Sobajic et al., Intelligent Control of the Intelledex 605T Robot Manipulator, pp. 633-640, 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 Artificial Neural Systems to Drive: Manual Training Techniques, pp. 231-238 Annual Workshop of SOA and Robotics (Aug. 5-7, 1987).
Parallel Distributed Processing, Explorations in the Microstructure of Cognition, by David E. Rumelhart and James L. McClelland, The MIT Press, Cambridge, Mass., U.S.A., 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, Mass. 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.
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 Artificial 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.
E. I. Du Pont de Nemours & Co., (Inc.)
MacDonald Allen R.
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