Structure identification of Bayesian classifiers based on GMDH
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A dynamic classifier ensemble selection approach for noise data
Information Sciences: an International Journal
Brief paper: A model reduction technique for nonlinear systems
Automatica (Journal of IFAC)
Model discrimination using an algorithmic information criterion
Automatica (Journal of IFAC)
System identification-A survey
Automatica (Journal of IFAC)
The characteristics of a biased estimator applied to the adaptive GMDH
Mathematical and Computer Modelling: An International Journal
Medical image diagnosis of liver cancer by feedback GMDH-type neural network using knowledge base
Artificial Life and Robotics
Artificial Life and Robotics
| Hi-index | 22.15 |
The systems, or programs, of heuristic self-organization are defined as those which include the generators of random hypotheses, or combinations, and several layers of threshold self-sampling of useful information. The complexity of combinations increases from layer to layer. A known system, Rosenblatt's perceptron, may be taken as an example. The Group Method of Data Handling (GMDH) based on the principles of heuristic self-organization is developed to solve complex problems with large dimensionality when the data sequence is very short. Two examples are given to illustrate how this method applies to problems of predicting random processes and to identifying characteristics of a multiextremum plant. One: Heuristics are groundless decisions which have no mathematical proofs. They give us the results which are only good enough for practice, but they are not the best ones. The other: No! Heuristics are decisions in a field irrelevant to the subject and competence of mathematics. The results of heuristics are often much better than those which can be obtained from a formalized approach.