1 Reported on EGS XXIV General Assembly, 22 April 1999, The Hague, The Netherlands Published: Veselovsky I.S., Dmitriev A.V., Orlov Yu.V., Ryazantseva M.O., and Tarsina M.V., Solar activity forecasting on 2000- 2003 by means Artificial Neural Networks, Proc. of "Large Scale of the Solar Activity: Achievements and Perspectives, Pulkovo, 1999, Snt-Petersburg, 61-65, 1999 (in Russian). Solar Activity Forecasting on 2000-2003 by Means of Artificial Neural Networks A. Dmitriev, Yu. Minaeva, Yu. Orlov, M. Riazantseva, I. Veselovsky SINP MSU, 119899, Moscow, Russia, dalex@srdlan.npi.msu.su Geomagnetic conditions are controlled effectively by solar activity. Three representative parameters characterizing the solar activity are sunspot number (W), 10.7cm radio solar flux (F10.7) and mean solar magnetic field value (SF). These parameters are responsible for different sides of solar activity manifestation and influence on the Earth. Sunspot number is associated with solar flares strongly affecting on Earth's magnetosphere and radiation environment. Earth's ionosphere conditions are tightly related to the F10.7 solar radio flux reflecting solar ultraviolet radiation. Mean solar magnetic field value is related to the interplanetary magnetic field controlling the averaged Earth's magnetosphere conditions. Practically persistent time series of these parameters are appropriate data sets for modelling and forecasting by means of Artificial Neural Networks (ANN). Methods of optimization of ANN input data sets and selection of training, testing and examination sets are discussed in the paper. The dynamical models of the mentioned above parameters are developed by means of ANN. The results and reliability of ANN forecasting for period 1999-2002 are presented and discussed. Introduction For modeling of self-consistent time series the recurrent ANN is very powerful method. These models take the information about prehistory of the system dynamics into account and hence they may be used for forecasting. The models forecasting sunspot number and average solar wind conditions are excellent examples of this kind of ANN applying. The prognoses of time-series of geomagnetic indexes (Dst, Kp, AP, etc.) are generated using Recurrent Neural Networks (RNN) [e.g. Wu, 1996]. Last report by Conway et al. [1998] presents the results of the feed forward neural networks applying for the long-term prediction of sunspot number. The authors estimate the maximum and the duration of the present XXIII solar cycle as 13030 (in 2001) and 11 years respectively. Nevertheless the solar activity is realized by different ways that affect to the Earth in different manners. The most representative time series of parameters that characterized some sides of solar activity are sunspot number (Wolf number) W, 10.7cm radio solar flux (F10.7) and mean solar magnetic field value (SF). In this study we try to forecast these parameters on 1999-2003 by means of RNN. Initial Data As initial data of sunspot number W we use the database on daily Wolf number on ftp-directory ftp://ftp.ngdc.noaa.gov/STP/SOLAR_DATA/SUNSPOT_NUMBERS/. We use only regular data of sunspot number that begins from 1850 (13 last solar cycles). The www-archive www.drao.nrc.ca/icarus/data/current.txt is used for obtaining daily 10.7cm radio solar flux values recorded by the National Research Council's Solar Radio Patrol since 1947. We use regular F10.7 flux adjusted to 1 Astronomical Unit since 1949. Daily means of mean solar magnetic field value (SF) for period 1975-1998 is obtained from NOAA NGDC Data base ftp://ftp.ngdc.noaa.gov/STP/SOLAR_DATA/SUN_AS_A_STAR/STANFORD/. As shown in [Veselovskiy et. al., 1998] usually the best correlation of sunspot number with other parameters are obtained with annular averaged values of these parameters. The best correlation of