Abstract

The Kohonen neural networks are modelled on the topological properties of the human brain. These networks are also known as self-organizing feature maps (SOFM). One advantage of suggesting a procedure is the ability of the SOFM neural network to determine the degree of similarity occurring between classes. The SOFM network can also be used to detect regularities occurring in the obtained empirical data. 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 SOFM 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 presenting photographs of orchard pests, for example. The resulting model of the Kohonen neural turns to be effective without reference classifier. The average classification error SOFM network during its operation was 0.05532 for the learning set and 0.0762 for the validtion set.

Key words: Orchard pests identification, fruit tree pests, Kohonen neural networks, image recognition.