linear neural network model based on the metric methods of recognition (options)

摘要

1.the neural network model u0434u0432u0443u0445u0441u043bu043eu0439u043du043eu0439 without zero layer for the task of pattern recognition, different, strictly implements metric methods for recognition i challengewhere the number of templates is equal to the number of images, the number of neurons and connections, as well as the values of the weights is determined on the basis of the initial conditions for strictly to: the number of imagesbenchmarks and indicators of, and table scale is determined for every pair of templates based on the voltage characteristics of the proximity of the u043cu0435u0442u0440u0438u0447u0435u0441u043au043eu043c u0440u0430u0441u043fu043eu0437u043d method u0430u0432u0430u043du0438u044f. 2.the neural network model u0442u0440u0435u0445u0441u043bu043eu0439u043du043eu0439 without zero layer for the task of pattern recognition, different, strictly implements metric methods for recognition i challengewhere the number of benchmarks over the number of images, the number of neurons and connections, as well as the values of the weights is determined on the basis of the initial conditions of strictly adachi, the number of imagesbenchmarks and indicators of, and table scale is determined for every pair of templates based on the voltage characteristics of the proximity of the u043cu0435u0442u0440u0438u0447u0435u0441u043au043eu043c u0440u0430u0441u043fu043eu0437u043d method u0430u0432u0430u043du0438u044f. 3.the neural network model u0442u0440u0435u0445u0441u043bu043eu0439u043du043eu0439 zero layer for the task of pattern recognition, different, strictly implements the metric methods of recognition for the taskswhere the number of templates is equal to the number of images, the number of neurons and connections, as well as the values of the weights is determined on the basis of the initial conditions for strictly to: the number of images, templatessign and table scale is defined for each benchmark on the basis of the characteristics of the proximity of u043cu0435u0442u0440u0438u0447u0435u0441u043au043eu043c recognition method. 4.the neural network model u0447u0435u0442u044bu0440u0435u0445u0441u043bu043eu0439u043du043eu0439 zero layer for the task of pattern recognition, different, strictly implements the metric methods of recognition of dr. la taskswhere the number of benchmarks over the number of images, the number of neurons and connections, as well as the values of the weights is determined on the basis of the initial conditions of strictly adachi, the number of images, templatessign and table scale is defined for each benchmark on the basis of the characteristics of the proximity of u043cu0435u0442u0440u0438u0447u0435u0441u043au043eu043c recognition method. 5.the model u0442u0440u0435u0445u0441u043bu043eu0439u043du043eu0439 optimized neural network without the zero layer for the task of pattern recognition, different, strictly implements the metric method s recognition task.where the number of benchmarks over the number of images, the number of neurons and connections, as well as the values of the weights is determined on the basis of the initial conditions of strictly adachi, the number of images, templatessign and table scale is determined for every pair of references, not belonging to one image, based on the voltage characteristics of the proximity of the u043cu0435u0442u0440u0438 u0447u0435u0441u043au043eu043c recognition method. 6.the model u0447u0435u0442u044bu0440u0435u0445u0441u043bu043eu0439u043du043eu0439 optimized neural network with zero layer for the task of pattern recognition, different, strictly implements metric technique for the recognition task.where the number of benchmarks over the number of images, the number of neurons and connections, as well as the values of the weights is determined on the basis of the initial conditions of strictly adachi, the number of images, templatessign and table scale is defined for each benchmark on the basis of the characteristics of the proximity of u043cu0435u0442u0440u0438u0447u0435u0441u043au043eu043c recognition method.