Abstract

According to the present invention, a method for recognition of normal and abnormal conditions can be performed with at least one neural network. First, trend data of an object system, before a recognition-step, are entered as input data to an input layer of each neural network and data of this system at the recognition-step are entered as objective output data to an output layer of the neural network. Thus, multiple sets of trend data showing at least one normal condition of this system are formed in the neural network in order to obtained learned weights and biases. Next, output data at every recognition-step are predicted by entering actual trend data as input data to the neural network, while the learned weights and biases are utilized. Then, the predicted output data are compared with actual output data at every recognition-step. Finally, the normal and abnormal conditions of this system can be recognized by real time interpretation of deviations between the predicted output data and the actual output data. The method of the present invention particularly can be applied to a control system requiring the recognition of abnormal conditions such as a control system for the operation of a plant, an automobile, a robot, an aircraft, a marine vessel, a medical apparatus, security apparatus and the like.

Other References

• Lippmann, R. P., "An Introduction to Computing with Neural Nets", IEEE ASSP Magazine, Apr. 1987, 4-20
• Sobajic et al., "On-line Monitoring and Diagnosis of Power System Operating Conditions Using Artificial Neural Networks", 1989 IEEE Intl. Symp. on Circuits and Systems, May 1989, 2243-2246
• Bhat et al., "Use of Neural Nets for Dynamic Modeling and Control of Chemical Process Systems," 1989 American Control Conference, Jun. 1989, 1342-1347
• Narendra et al., "Identification and Control of Dynamical Systems Using Neural Networks," IEEE Trans. on Neural Networks, Mar. 1990, 4-27
• Explorations in Parallel Distributed Processing, A Handbook of Models, Programs and Excercises, James L. McClelland et al., 1988
• A Multilayered Neural Network Controller, Demetri Psailtis, et al., 1988
• Neural Network Models for the Learning Control of Dynamical System With Application to Robotics, Pourboghrat et al., 1989
• Skeletonization: A Technique for Trimming the Fat From a Network Via Relevance Assessment by Michael C. Mozer, 1989
• Neural Network Approach to Fault Detection Under Unsteady State Operation by Ichiro Koshijima, Apr. 199