Neural networks type MLP in the process of identification chosen varieties of maize

During the adaptation process of the weights vector that occurs in the iterative presentation of the teaching vector, the the MLP type artificial neural network (MultiLayer Perceptron) attempts to learn the structure of the data. Such a network can learn to recognise aggregates of input data occurring in the input data set regardless of the assumed criteria of similarity and the quantity of the data explored. The MLP type neural network can be also used to detect regularities occurring in the obtained graphic empirical data. The neuronal image analysis is then a new field of digital processing of signals. It is possible to use it to identify chosen objects given in the form of bitmap. If at the network input, a new unknown case appears which the network is unable to recognise, it means that it is different from all the classes known previously. The MLP type artificial neural network taught in this way can serve as a detector signalling the appearance of a widely understood novelty. Such a network can also look for similarities between the known data and the noisy data. In this way, it is able to identify fragments of images presented in photographs of e. g. maze's grain. The purpose of the research was to use the MLP neural networks in the process of identification of chosen varieties of maize with the use of image analysis method. The neuronal classification shapes of grains was performed with the use of the Johan Gielis super formula.