It is important that an optimal learning problem is proved to be NP-hard and the heuristic algorithm for solving the problem has to be given. This paper deals with a learning problem appearing in the process of simplifying fuzzy rules, proves that the solution optimization is NP-hard and gives its h
“Live” neuron and optimal learning rule
✍ Scribed by L. B. Emelyanov-Yaroslavsky; V. I. Potapov
- Publisher
- Springer-Verlag
- Year
- 1992
- Tongue
- English
- Weight
- 503 KB
- Volume
- 67
- Category
- Article
- ISSN
- 0340-1200
No coin nor oath required. For personal study only.
✦ Synopsis
A concept of the live unit as an automatic regulation system with a few admissible states areas in the space of states is considered. Energetic profit of oscillatory behavior consisting in the consecutive transitions of system from one admissible states area to another is shown. It is stated, that external disturbances cause the energy consumption of oscillatory system to decrease. On the basis of this concept and some neurophysiological data, the "live" energy-consuming nonlinear three-state neuron model is proposed and the existence of energy optimal generation frequency Vopt is proved. For the realization of tendency to Vop t the optimal learning rule is proposed, which provides unsupervised learning and interlinked short-term and long-term memories with forgetting. The model proposed explains the genesis of neural network, is promising in the sense of network self-organization and allows to solve the problem of internal activity in the researches on artificial intelligence.
📜 SIMILAR VOLUMES
Self-organization of orientation maps due to external stimuli in the primary visual area of the cerebral cortex is studied in a two-layered neural network which consists of formal neuron models with a sigmoidal output function. A cluster learning rule is proposed as an extended Hebbian learning rule