caa a22 4500 475821408 CHVBK 20180406123819.0 cr unu---uuuuu 170329e20000601xx s 000 0 eng 10.1007/BF02508628 doi (NATIONALLICENCE)springer-10.1007/BF02508628 Self-adaptation algorithm for neural networks with search behavior [Elektronische Daten] [T. Baskanova, Yu. Lankin] In the present paper, a training algorithm with nontraditional capabilities and self-adaptation is suggested for a new generation of neural networks with search behavior. The results obtained open up new opportunities for progress in the physics of living systems in the direction of modeling of purposeful search behavior at the insect level. They enable systems of adaptive control, considering changes in environmental conditions and in properties of the controllable object in the control process, to be developed. The algorithm allows both traditional supervisory training of a neural network with a priori known answers and finish learning during its functioning in accordance with preassigned criteria when knowledge of required states of neuron network outputs is lacking. Kluwer Academic/Plenum Publishers, 2000 Baskanova T. aut Lankin Yu aut Russian Physics Journal Kluwer Academic Publishers-Plenum Publishers 43/6(2000-06-01), 483-487 1064-8887 43:6<483 2000 43 11182 https://doi.org/10.1007/BF02508628 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF02508628 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Baskanova T. aut NATIONALLICENCE 700 1- Lankin Yu aut NATIONALLICENCE 773 0- Russian Physics Journal Kluwer Academic Publishers-Plenum Publishers 43/6(2000-06-01), 483-487 1064-8887 43:6<483 2000 43 11182 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer