230. Химмельблау Д. Прикладное нелинейное программирование. М.: Мир, 1975. 534 с.
231. Хинтон Дж. Е. Обучение в параллельных сетях / Реальность и прогнозы искусственного интеллекта. — М.: Мир, 1987.— С. 124–136.
232. Царегородцев В.Г. Транспонированная линейная регрессия для интерполяции свойств химических элементов // Нейроинформатика и ее приложения. Тезисы докладов 5 Всероссийского семинара, 3–5 октября 1997 г. / Под ред. А.Н.Горбаня. Красноярск: изд. КГТУ, 1997. С. 177–178.
233. Цыганков В.Д. Нейрокопьютер и его применение. — М.: "Сол Систем", 1993.
234. Цыпкин Я.З. Основы теории обучающихся систем. М.: Наука, 1970. 252 с.
235. Шайдуров В.В. Многосеточные методы конечных элементов. — М.: Наука, 1989.
236. Шварц Э., Трис Д. Программы, умеющие думать // Бизнес Уик. — 1992.— n.6.— С. 15–18.
237. Шенк Р., Хантер Л. Познать механизмы мышления / Реальность и прогнозы искусственного интеллекта. — М.: Мир, 1987.— С. 15–26.
238. Щербаков П.С. Библиографическая база данных по методам настройки нейронных сетей // Нейрокомпьютер, 1993. № 3,4. С. 5–8.
239. Aleksander I., Morton H. The logic of neural cognition // Adv. Neural Comput.- Amsterdam etc., 1990.- PP. 97-102.
240. Alexander S. Th. Adaptive Signal Processing. Theory and Applications. Springer. 1986. 179 p.
241. Allen J., Murray A.. Development of a neural network screening aid for diagnosing lower limb peripheral vascular disease from photoelectric plethysmography pulse waveforms // Physiol. Meas.- 1993.- V.14, N.1.- P.13-22.
242. Amari Sh., Maginu K. Statistical Neurodynamics of Associative Memory // Neural Networks, 1988. V.1. N1. P. 63-74.
243. Arbib M.A. Brains, Machines, and Mathematics. Springer, 1987. 202 p.
244. Astion M.L., Wener M.H., Thomas R.G., Hunder G.G., Bloch D.A. Application of neural networks to the classification of giant cell arteritis // Arthritis Reum.- 1994.- V.37, N.5.- P.760-770.
245. Aynsley M., Hofland A., Morris A.J. et al. Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks) // Adv. Biochem. Eng. Biotechnol.- 1993.- N.48.- P.1-27.
246. Baba N. New Topics in Learning Automate Theory and Applications. Springer, 1985. 131 p. (Lec. Not. Control and Information, N71).
247. Barschdorff D., Ester S., Dorsel T et al. Phonographic diagnostic aid in heart defects using neural networks // Biomed. Tech. Berlin.- 1990.- V.35, N.11.- P.271-279.
248. Bartsev S.I., Okhonin V.A. Optimization and Monitoring Needs: Possible Mechanisms of Control of Ecological Systems. Nanobiology, 1993, v.2, p.165-172.
249. Bartsev S.I., Okhonin V.A. Self-learning neural networks playing "Two coins"// Proc. of International Workshop "Neurocomputers and attention II", Manchester Univ.Press, 1991, p.453-458.
250. Bartsev S.I., Okhonin V.A. The algorithm of dual functioning (back-propagation): general approuch, versions and applications. Preprint of Biophysics Institute SB AS USSR, Krasnoyarsk, 1989, №107B, 16 p.
251. Bartsev S.I., Okhonin V.A. Variation principle and algorithm of dual functioning: examples and applications// Proc. of International Workshop "Neurocomputers and attention II", Manchester Univ.Press, 1991, p.445-452.
252. Baxt W.G. A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching // Med. Decis. Making.- 1994.- V.14, N.3.- P.217-222.
253. Baxt W.G. Analysis of the clinical variables driving decision in an artificial neural network trained to identify the presence of myocardial infarction // Ann. Emerg. Med.- 1992.- V.21, N.12.- P.1439-1444.
254. Baxt W.G. Complexity, chaos and human physiology: the justification for non-linear neural computational analysis // Cancer Lett.- 1994.- V.77, N.2-3.- P.85-93.
255. Baxt W.G. Use of an artificial neural network for the diagnosis of myocardial infarction // Ann. Intern. Med.- 1991.- V.115, N.11.- P.843-848.
256. Borisov A.G., Gilev S.E., Golovenkin S.E., Gorban A.N., Dogadin S.A., Kochenov D.A., Maslennikova E.V., Matyushin G.V., Mirkes Ye.M., Nozdrachev K.G., Rossiyev D.A., Savchenko A.A., Shulman V.A. "MultiNeuron" neural simulator and its medical applications // Modelling, Measurement & Control, C.- 1996.- V.55, N.1.- P.1-5.
257. Bruck J., Goodman J. W. On the power of neural networks for solving hard problems // J. Complex.- 1990.- 6, № 2.PP. 129-135.
258. Budilova E.V., Teriokhin A.T. Endocrine networks // The RNNS/IEEE Symposium on Neuroinformatics and Neurocomputers, Rostov-on-Don, Russia, October 7-10, 1992.- Rostov/Don, 1992.- V.2.- P.729-737.
259. Carpenter G.A., Grossberg S. A Massivly Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine. - Computer Vision, Graphics, and Image Processing, 1987. Vol. 37. PP. 54-115.
260. Connectionism in Perspective/Ed. by R. Pfeifer, Z. Schreter, F.Fogelman-Soulie and L. Steels. North-Holland, 1989. 518 p.
261. Cybenko G. Approximation by superposition of a sigmoidal function. - Mathematics of Control, Signals, and Systems, 1989. Vol. 2. PP. 303–314.
262. Diday E., Simon J.C. Clustering analysis, (dans Digital Pattern Recognition), Redacteur: K.S.F.U., Springer Verlag, Berlin, 1980, P. 47-93.
263. Disordered Systems and biological Organization/Ed. by Bienenstock F., Fogelman-Soulie G. Weisbuch. Springer, 1986. 405 p.
264. Dorrer M.G., Gorban A.N., Kopytov A.G., Zenkin V.I. Psychological intuition of neural networks. Proceedings of the WCNN'95 (World Congress on Neural Networks'95, Washington DC, July 1995). PP. 193-196.
265. Dorrer M.G., Gorban A.N., Zenkin V.I. Neural networks in psychology: classical explicit diagnoses // Neuroinformatics and Neurocomputers, Proceedings of the second RNNS-IEEE Simposium, Rostov-na-Donu, September 1995. PP. 281-284.
266. Draper N. R. Applied regression analysis bibliographi update 1988-89 // Commun. Statist. Theory and Meth.- 1990.1990.- 19, № 4.- PP. 1205-1229.
267. Ercal F., Chawla A., Stoeker W.V. et al. Neural network diagnosis of malignant melanoma from color images // IEEE Trans. Biomed. Eng.- 1994.- V.41, N.9.- P.837-845.
268. Ferretti C., Mauri G. NNET: some tools for neural Networks simulation // 9th Annu. Int. Phoenix Conf. Comput. and Commun., Scottsdate, Ariz., March 21-23, 1990.- Los Alamitos (Calif.) etc., 1990.- PP. 38-43.
269. Filho E.C.D.B.C., Fairhurst M.C., Bisset D.L. Adaptive pattern recognition using goal seeking neurons // Pattern Recogn. Lett.- 1991.- 12, № 3.- PP. 131-138.
270. Floyd C.E.Jr., Lo J.Y., Yun A.J. et al. Prediction of breast cancer malignancy using an artificial neural network // Cancer.- 1994.- V.74, N.11.- P.2944-2948.
271. Forbes A.B., Mansfield A.J. Neural implementation of a method for solving systems of linear algebraic equations // Nat. Phys. Lab. Div. Inf. Technol. and Comput. Rept.- 1989.№ 155.- PP. 1-14.
272. Fu H.C., Shann J.J. A fuzzy neural network for knowledge learning // Int. J. Neural Syst.- 1994.- V.5, N.1.- P.13-22.
273. Fukushima K. Neocognitron: A self-organizing Neural Network model for a Mechanism of Pattern Recognition uneffected by shift in position // Biological Cybernetics.1980. V. 36, № 4. PP. 193-202.
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