Abstract
We consider a new type of hybrid neuro-fuzzy system based on fuzzy and neural computing in hierarchical sequential structure, the total effect exceeds the effect of each component separately. The proposed system can be applied to multi-criteria analysis, automatic classification on signs and obtain evidence-based estimates of the efficiency of scientific and technical solutions and technologies, engineering and robotics. An example of a neuro-fuzzy system measuring the intensity of the emotions of a robot, with the extraction of diagnostic decision rules “If & then”.
Highlights
IntroductionThe concept of soft computing (soft computing) was first mentioned in the work of Zadeh
The concept of soft computing was first mentioned in the work of Zadeh
In the course of the study of neuro-fuzzy systems were obtained the following results: 1. The developed method of synthesis of neuro-fuzzy classifier based on decision tree, neural network includes adapting parameters of the constructed decision tree
Summary
The concept of soft computing (soft computing) was first mentioned in the work of Zadeh Zadeh) in 1994 [1]. Soft computing - is a complex computer methodology based on fuzzy logic, evolutionary computation, Neurocomputing and probabilistic calculations, with the later inclusion of chaotic systems, trust networks and learning theory sections. Components do not compete, but they create a synergistic effect. The guiding principle of soft computing - is accounting inaccuracies, uncertainty, partial truth, and approximation to achieve robustness, low cost solutions that better fit with reality.
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