Aggleton JP (eds) (1992). The Amygdala: neurobiological aspects of emotion, memory and mental dysfunction. Wiley-Liss, New York
Google Scholar
Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H, Shah N, Habel U, Schneider F and Zilles K (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat Embryol (Berl) 210(5–6): 343–352
Article
Google Scholar
Babaie T, Karimizandi R, Lucas C (2005) A spectral analysis and neurofuzzy approach to prediction of geomagnetic activity. In: 8th conference of Intelligent Systems, Tehran, Iran
Babaie T, Karimizandi R, Lucas C (2005) A neuro-emotional approach to prediction. In: Workshop on cognitive sciene and neurophilosophi. Institude Philosophyof Iran, Tehran, Iran, pp 3–6
Babaie T, Karimizandi R and Lucas C (2006). Prediction of solar conditions by emotional learning. Intell Data Anal 10(6): 583–597
Google Scholar
Baker DN (1986). Statistical analyses in the study of solar wind-magnetosphere coupling. In: Kamide, JA and Slavin, Y (eds) Solar wind-magnetosphere coupling, pp 17–38. Terra Scientific Pub, Tokyo
Google Scholar
Barto A, Sutton R and Watkins C (1990). Learning and sequential decision making, in Learning and Computational Neuroscience. MIT, Cambridge
Google Scholar
Bay JS (1997) Behavior learning in large homogeneous populations of robots. In: IASTED International Conference on Artificial Intelligence and Soft Computing, pp 137–140
Boberg F, Wintoft P and Lundstedt H (2000). Real time Kp predictions from solar wind data using neural networks. Phys Chem Earth 25(4): 275–280
Google Scholar
Broca P (1878). Anatomie comparée des circonvolutions cérébrales: le grand lobe limbique. Rev Anthropol 1: 385–498
Google Scholar
Chen S, Wu Y and Luk BL (1999). Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Trans Neural Netw 10(5): 1239–1243
Article
Google Scholar
Detman TR and Vassiliadis D (1997). Review of techniques for magnetic storm forecasting. In: Tsurutani, BT, Gonzalez, WD, Kamide, Y and Arballo, JK (eds) Magnetic storms, pp 253–266. AGU, Washington DC
Google Scholar
Fatourechi M, Lucas C, Khaki Sedigh A (2001) Reducing control effort by means of emotional learning. In: Proceedings of 9th Iranian conference on Electrical Engineering, ICEE’01, Tehran, Iran (41):1–8
Freeman J, Nagai A, Reiff P, Denig W, Gussenhoven Shea S, Heinermann M, Rich F, Hairston M (1994) The use of neural networks to predict magnetospheric parameters for input to a magnetospheric forecast model. In: Joselyn J, Lundstedt H, Trollinger (eds) Artificial intelligence applications in solar terrestrial physics, 167. Natl Oceanic and Atmos Admin, Boulder, Colorado
Gholipour A, Abbaspour A, Araabi BN, Caro Lucas (2003) Enhancements in the prediction of solar activity by locally linear model tree. In: Proceedings of MIC2003: 22nd international conference on Modeling. Identification and Control, Innsbruck, Austria, pp 158–161
Gholipour A, Araabi B and Lucas C (2006). Predicting chaotic time series using neural and neurofuzzy models: a comparative study. Neural Process Lett 24(3): 217–239(23)
Article
Google Scholar
Gleisner H (2000). Solar wind and Geomagnetic activity: predictions using neural networks. Lund University, Lund, PhD thesis
Google Scholar
Gleisner H, Lundstedt H and Wintoft P (1996). Predicting geomagnetic storms from solar wind data using time delay neural networks. Ann Geophys 14: 679–686
Article
Google Scholar
Gleisner H and Lundstedt H (1999). Ring current influence on auroral electrojet predictions. Ann Geophys 17: 1268–1275
Article
Google Scholar
Gleisner H and Lundstedt H (1997). Response of the auroral electrojets to the solar wind modeled with neural networks. J Geophys Res 102: 14269–14278
Article
Google Scholar
Goleman D (1995). Emotional intelligence. Bantam Books, New York
Google Scholar
Haykin S (1998). Neural networks—a comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs
Google Scholar
Haykin S (1999). Neural networks: a comprehensive foundation. Prentice-Hall, NewYork
MATH
Google Scholar
Hernandez JV, Tajima T and Horton W (1993). Neural net forecasting for geomagnetic activity. Geophys Res Lett 20(23): 2707–2710
Article
Google Scholar
Horton W (1997). Chaos and Structures in the Magnetosphere. Phys Rep 283: 265–302
Article
Google Scholar
Horton W, Doxas I, Goode B and Cary J (1998). A low-dimensional dynamical model for the solar wind driven geotail-ionosphere system. J Geophys Res 103(A3): 4561–4572
Article
Google Scholar
Horton W, Smith JP, Weigel R, Crabtree C, Doxas I, Goode B and Cary J (1999). The solar wind driven magnetosphere-ionosphere as a complex dynamical system. Phys Plasmas 6(11): 4178–4184
Article
Google Scholar
Hussain A (2002) Physical time-series prediction using second-order pipelined recurrent neural network. IEEE international conference on Artificial Intelligence Systems (ICAIS 2002), 2002, pp 219–223
Inoue K, Kawabata K, Kobayashi H (1996) On a decision making system with emotion. In: Proceedings of the 5th IEEE international workshop on robot and human communication, pp 461–465
Inoue K, Kawabata K, Kobayashi H (1996) On a decision making system with emotion. In: Proceedings 5th IEEE international workshop on Robot and Human Communication, pp 461–465
Iyemori T, Maeda H and Kamei T (1979). Impulse response of geomagnetic indices to interplanetary magnetic fields. J Geomag Geoelectr 31: 1–9
Google Scholar
Izeman AJ and Wolf JR (1985). The Zurich sunspot relative numbers. Math Intell 7(1): 27–33
Article
Google Scholar
Joselyn JA (1995). Geomagnetic activity forecasting: the state of the art. Rev Geophys 33: 383
Article
Google Scholar
Jang J-S, sun C-T and Mizutani E (1997). Neuro-fuzzy and soft computing. Prentice-Hall, NewYork
Google Scholar
Kamide Y, Yokoyama N, Gonzalez W, Tsurutani BT, Daglis IA, Brekke A and Masuda S (1998). Two step development of geomagnetic storms. J Geophys Res 103: 6917–6921
Article
Google Scholar
Kandel A (1988). Fuzzy expert systems. Addison-Wesley, Reading
MATH
Google Scholar
Kandel A (eds) (1992). Fuzzy expert systems. CRC, Boca Raton
Google Scholar
LeDoux JE (1992). Emotion and the Amygdala. In: Aggleton, JP (eds) The Amygdala:neurobiological aspects of emotion, memory and mental dysfunction, pp 339–351. Wiley-Liss, New York
Google Scholar
Lee C-C (1990a). Fuzzy logic in control systems: fuzzy logic controller-part 1. IEEE Trans Syst Man Cybern 20(2): 404–418
MATH
Article
Google Scholar
Lee C-C (1990b). Fuzzy logic in control systems: fuzzy logic controller-part 2. IEEE Trans Syst Man Cybern 20(2): 419–435
MATH
Article
Google Scholar
Leung H, Lo T and Wang S (2001). Prediction of noisy chaotic time series using an optimal radial basis function neural network. IEEE Trans Neural Netw 12(5): 1163–1172
Article
Google Scholar
Lorenz EN (1963). Deterministic non-periodic flow. J Atmos Sci 20: 130–141
Article
Google Scholar
Lucas C, Abbaspour A, Gholipour A, Araabi BN and Fatourechi M (2003). Enhancing the performance of neurofuzzy predictors by emotional learning algorithm. Informatica 27(2): 165–174
Google Scholar
Lundstedt H (1992). Neural networks and predictions of solar-terrestrial effects. Planet Space Sci 40: 457–464
Article
Google Scholar
Lundstedt H (1998) AI Techniques in Geomagnetic Storm Forecasting. In: Magnetic Storms, Geophysical Monograph 98, AGU
Lundstedt H (1998) Lund space weather model: status and future plans. In: Proceedings of the second workshop on AI Applications in Solar-Terrestrial Physics, July 29–31, 1997, Lund, Sweden, ESA WPP-148
Lundstedt H and Wintoft P (1994). Prediction of geomagnetic storms from solarwind data with the use of a neural network. Ann Geophys 12: 19–24
Article
Google Scholar
Maclean PD (1952). Some psychiatric implications of physiological studies on frontotemporal portion of limbic system (visceral brain). Electroencephalogr Clin Neurophysiol 4(4): 407–18
Article
Google Scholar
Moren J (2002). Emotion and learning: a computational model of the amygdala. Lund University, Lund, PhD thesis
Google Scholar
Moren J and Balkenius C (2000). A computational model of emotional learning in the amygdale. In: Mayer, JA, Berthoz, A, Floreano, D, Roitblat, HL and Wilson, SW (eds) From animals to animats 6, pp 383–391. MIT, Cambridge
Google Scholar
Papez JW (1995). A proposed mechanism of emotion, 1937. J Neuropsychiatry Clin Neurosci 7(1): 103–12
Google Scholar
Pedrycz W (1989). Fuzzy control and fuzzy systems. Wiley, New York
MATH
Google Scholar
Perlovsky LI (1999) Emotions, learning and control. In: Proceedings of IEEE international symposium on Intelligent control/Intelligent systems and semiotics, Cambridge, MA, pp 132–137
Picard RW, Vyzas E and Healey J (2001). Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell 23(10): 1175–1191
Article
Google Scholar
Picard RW, Vyzas E and Healey J (2001). Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell 23(10): 1175–1191
Article
Google Scholar
Roger Jang J-S (1992). Self-learning fuzzy controller based on temporal back-propagation. IEEE Trans Neural Netw 3: 714–723
Article
Google Scholar
Roger Jang J-S (1993). ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23: 665–685
Article
Google Scholar
Rolls ET (1986). A theory of emotion and its application to understanding the neural basis of emotion. In: Oomura, Y (eds) Emotions: neural and chemical control, pp 325–344. Japan Scientific Societies Press, Tokyo
Google Scholar
Rolls ET (1995). A theory of emotion and consciousness and its application to understanding the neural basis of emotion. In: Michael Gazzaniga, S (eds) The cognitive neurosciences, pp 1091–1106. MIT, Cambridge
Google Scholar
Schatten KH and Pesnell WD (1993). An early solar dynamo prediction: Cycle 23 ~ Cycle 22. Geophysical Res Lett 20: 2257–2278
Article
Google Scholar
Sloman A (1981) Why robots will have emotions. In: Proceedings University of Sussex, UK
Sugeno M (eds) (1985). Industrial applications of fuzzy control. Elsevier, Amsterdam
Google Scholar
Sutton RS (1989). Learning to predict by the method of temporal differences. Mach Learn 3: 9–44
Google Scholar
Sutton RS and Barto AG (1998). Introduction to reinforcement learning. MIT, Cambridge
Google Scholar
Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. In: Proceedings of the IFAC Symp on Fuzzy Information, Knowledge Representation and Decision Analysis, pp 55–60
Takagi T and Sugeno M (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15: 116–132
MATH
Google Scholar
Takalo J and Timonen J (1999). Neural network prediction of the AE index from the PC index. Physics and chemistry of the earth. Part C. Solar Terr Planet Sci 24(1): 89–92(4)
Google Scholar
Takalo J and Timonen J (1997). Neural network prediction of AE data. Geophys Res Lett 24(19): 2403–2406
Article
Google Scholar
Thompson RJ (1993). A technique for predicting the amplitude of solar cycle. Solar Phys 148: 383
Article
Google Scholar
Uluyol O, Ragheb M, Ray SR (1998) Local output Gamma Feedback neural network. In: Proceedings of the IEEE international conference on Neural Networks: IJCNN (1):337–342
Vassiliadis D (2000). System identification, modeling and prediction for space weather environments. IEEE Trans Plasma Sci 28(6): 1944–1955
Article
MathSciNet
Google Scholar
Vassiliadis D, Klimas AJ, Baker DN and Roberts DA (1995). Adescription of solar wind magnetosphere coupling based on nonlinear filters. J Geophys Res 100: 3495–3512
Article
Google Scholar
Vassiliadis D, Sharma AS, Eastman TE and Papadopulos K (1990). Low-dimensional chaos in magnetospheric activity from AE time series. Geophys Res Lett 17(11): 1841–1844
Article
Google Scholar
Ventura R, Pinto Ferreira C (1999) Emotion based control systems. In: Proceedings of IEEE international symposium on Intelligent control/Intelligent systems and semiotics. Cambridge, MA, pp 64–66
Watkins C (1989). Learning from delayed rewards. University of Cambridge, England, PhD thesis
Google Scholar
Weigend A, Berman BH and Rumelhart D (1990). Predicting the future: a connectionist approach. Int J Neural Syst 1(3): 193–209
Article
Google Scholar
Wu JG (1997). Dynamic neural network studies of solar wind magnetosphere coupling. Lund Observatory, Lund, PhD thesis
Google Scholar