Historical database training method for neural networks

A neural network/expert system process control system and method combines the decision-making capabilities of expert systems with the predictive capabilities of neural networks for improved process control. Neural networks provide predictions of measurements which are difficult to make, or supervisory or regulatory control changes which are difficult to implement using classical control techniques. Expert systems make decisions automatically based on knowledge which is well-known and can be expressed in rules or other knowledge representation forms. Sensor and laboratory data is effectively used. In one approach, the output data from the neural network can be used by the controller in controlling the process, and the expert system can make a decision using sensor or lab data to control the controller(s). In another approach, the output data of the neural network can be used by the expert system in making its decision, and control of the process carried out using lab or sensor data. In another approach, the output data can be used both to control the process and to make decisions.

Neural network/expert system process control system and method

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).

Computer neural network supervisory process control system and method.

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 neural 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.

Computer neural network regulatory process control system and method

This article presents a distributed computational architecture for the motor planning functions of the posterior parietal cortex, which is organized as a computational map and combines a paradigm of self-organization (for building robust and coherent maps of the different motor spaces) with an attractor dynamics (for run-time integration of task constraints). The model, named SO-BoS (self-organizing body-schema), is illustrated with simple simulation results.

Self-Organizing Body Schema for Motor Planning

A theory and the prototype of a neural controller called INFANT that learns sensory-motor coordination from its own experience are presented. INFANT adapts unforeseen changes in the geometry of the physical motor system and to the location, orientation, shape, and size of objects. It can learn to accurately grasp an elongated object without any information about the geometry of the physical sensory-motor system. This new neural controller relies on the self-consistency between sensory and motor signals to achieve unsupervised learning. It is designed to be generalized for coordinating any number of sensory inputs with limbs of any number of joints. INFANT is implemented with an image processor, stereo cameras, and a 5 degrees-of-freedom robot arm. Its average grasping accuracy after learning is 3% of the arm's length in position and 6 degrees in orientation. >

http://ieeexplore.ieee.org/iel2/842/3401/00118715.pdf

Implementation of an adaptive neural controller for sensory-motor coordination

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Implementation of an adaptive neural controller for sensory-motor coordination