Abstract: In a method and arrangement for the neural modelling of a dynamic system with non-linear stochastic behavior wherein only a few measured values of the influencing variable are available and the remaining values of the time series are modelled, a combination of a non-linear computerized recurrent neural predictive network and a linear error model are employed to produce a prediction with the application of maximum likelihood adaption rules. The computerized recurrent neural network can be trained with the assistance of the real-time recurrent learning rule, and the linear error model is trained with the assistance of the error model adaption rule that is implemented on the basis of forward-backward Kalman equations. This model is utilized in order to predict values of the glucose-insulin metabolism of a diabetes patient.

Abstract: A process is set forth in which cancer of the colon is assessed in a patient. The probabilities of developing cancer involves the initial step of extracting a set of sample body fluids from the patient. Fluids can be evaluated to determine certain marker constituents in the body fluids. Fluids which are extracted have some relationship to me development of cancer, precancer or tendency toward cancerous conditions. The body fluid markers are measured and other quantified. The marker data then is evaluated using a nonlinear technique exemplified through the use of a multiple input and multiple output neural network having a variable learning rate and training rate. The neural network is provided with data from other patients for the same or similar markers. Data from other patients who did and did not have cancer is used in the learning of the neural network which thereby processes the data and provides a determination that the patient has a cancerous condition, precancer cells or a tendency towards cancer.