Sampling a training set to classify sea technical objects using a neural network
The paper discusses creation a monitoring system for marine areas based on non-linear rear-illuminated tracing hydroacoustics using neural network technology. Numerous experiments on training of neural networks showed that cumulative local optimization algorithm, "knocking out" the network of local minimum and procedures for increasing the number of neurons result in successful learning of neural networks. The following tasks were formulated for decision: simulating instant location of recognized object and its movement parameters; simulation of climatic variability of vertical distribution for speed of sound in water and sonar settings of fields using a database of typical vertical distribution curves of the speed of sound; simulation of information flows that occur in situations of «receiver - source»; development of module for control of simulation model; assessing the adequacy of the model; optimizing the simulation model. Solving these problems gives possibility to form a sample with a sufficient amount of data for training neural networks. Algorithmic basis and software package have been produced for successful training the neuro-qualifier of high efficiency. Results of scientific researches of authors on this subject are protected by patents, issued in the form of monographs and reflected in scientific papers.