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Article title DEVELOPMENT OF MODELS AND ALGORITHMS FOR TRACKING STATE CHANGES OF A TECHNICAL OBJECT BASED ON A MODIFIED HAMMING NEURAL NETWORK
Authors S.I. Klevtsov
Section SECTION I. METHODS AND ALGORITHMS FOR SIGNAL PROCESSING
Month, Year 11, 2014 @en
Index UDC 629.3.066.3
DOI
Abstract To avoid and prevent abnormal situations, a model tracking state changes of a technical object in real time is developed. It is often enough to analyze the behavior of one the most important and relevant parameters, without regard to the other parameters that affect its state to the solution of this problem. Due to the fact that the tracking state change should take place in real-time microprocessor microcontroller unit, and preferably in the background pattern the requirements set to the algorithm and model on the ease of computation, and adaptation to characteristics of microprocessor data processing. Time for identification should be small, not significantly affect the performance of other tasks. For solving the problem of tracking changes in the state is used the model that is based on the Hamming neural network. The resulting modified Hamming neural network algorithm implements a simple operation, simple learning algorithm. Its capacity is not dependent on the dimension of the input signal. The resulting modified neural network model and tracking algorithm changes the state of the object function based on sequence analysis of the monitored parameters. The input network served binary input signals that can be effectively used in the implementation of the algorithm in the microcontroller.

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Keywords Neural network; the object state change; real time.
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