Learning methods (G06N3/08)

Method of training artificial neural network

Method comprises steps of: determining the required number of training vectors; limiting the input vector space with a certain region O; indicating M vectors which describe the most typical representatives of each of the investigated classes of objects belonging to the region O; generating K training vectors of input signals of artificial neural networks (ANN), first in the vicinity of the M vectors, with subsequent expansion to the region O; creating visual patterns clearly describing objects specified by the generated training vectors; determining one of M classes to which each of the K generated training vectors of input signals of ANN is associated; recording the generated training vectors and reference signals corresponding to classes of objects, to which the generated vectors relate, in form of pairs; reading the recorded pairs and transmitting to ANN inputs; correcting the vector of synaptic weights of neurons w(n) with a correction step η until training of the ANN is complete.

Training convolutional neural network on graphics processing units

Method comprises the following steps: receiving graphics data representing a state of the convolutional neural network and comprising one or more textures representing one or more neural network variables, wherein said textures comprise a texture with two-dimensional addressing, and at least one of the textures represents a neural network variable with addressing of more than two dimensions which has been flattened into two dimensional addressing, the convolutional neural network comprising at least one layer comprising a plurality of patches; executing one or more programs on the graphics processing unit (GPU) in order to perform a forward pass in the convolutional neural network, executing one or more programs to perform a backward pass in the convolutional neural network, the executing including performing convolution operations on the patches; executing one or more programs in order to modify the patches in the convolutional neural network by changing the graphics data based on results of the backward pass; and repeating execution of one or more programs to perform forward passes, backward passes, and to modify the graphics data until the convolutional neural network is trained.

Neural network number-to-frequency converter

Device has two adders, two OR elements, two delay elements, a counter, a decoder, code memory, four AND elements, a weight coefficient memory unit and a training unit.