Neurocomputing method loosely based on the model of the brain as a network of simple processing elements interconnected in accordance with neurons. This method of their power comes from the collective processing artificial neuron, its main advantage is that the system can learn and adapt to environmental changes. In a knowledge-based neurocomputing, emphasis was placed on the use and representation of knowledge about the application. explicit modeling of knowledge represented by such systems remains a major research topic. The reason is that humans are difficult to interpret the numerical representation of the neural network. The key assumptions neurocomputing science-based knowledge derived from, or may be represented by a system neurocomputing in the form of a human being can understand. That is, knowledge embedded in neurocomputing system also can be represented in symbolic form or a well-structured, such as Boolean functions, automata, rule, or other ways you know. The focus is on knowledge-based computational methods to encode prior knowledge and to extract, refine, and revise the knowledge in the system neurocomputing:. Contributors C. Aldrich, J. Drossu, JJ Schmitz, J. Spott, A. Weisbrod, J.