Achelis, S.B. Technical Analysis from A to Z. Second printing, McGraw-Hill, 1995.

Ajith, A., Sajith, N., & Sarathchandran, P.P. (2003). Modelling chaotic behaviour of stock indices using Intelligent Paradigms. Neural, Parallel & Scientific Computations Archive, 11, 143–160.

Atsalakis, G.S, & Valavanis, K.P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36, 5932–5941.

Baek, J., & Cho, S. (2002). Time to jump. Long rising pattern detection in KOSPI 200 future using an auto-associative neural network. Lecture Notes in Computer Science , 2412. Springer.

Baykal, N., & Erkmen, A.E. (2000). Resilient Backpropagation for RBF Networks. IEEE Fourth International Conference on knowledge-Based Intelligent Engineering Systems 6, Brighton,UK. 624-627.

Boukadoum, M., Trabelsi, A., & Fayomi, C. (2009). FPGA-Based Multispectral Fluorometer using CDMA and Embedded Neural Network. IEEE International Conference on Microelectronics, 199-202.

Brent, R.P. (1991). Fast Training Algorithms for Multi-layer Neural Nets. IEEE Transactions on Neural Networks, 2, 346–354.

Chen, Y., Dong, X., & Zhao, Y. (2005). Stock index modelling using EDA based local linear wavelet neural network. Proceedings of International Conference on Neural Networks and Brain, 1646–1650.

Chun, S., & Park, Y. (2005). Dynamic adaptive ensemble case-based reasoning: Application to stock market prediction. Expert Systems with Applications, 28, 435–443.

Cybenko, G. (1989). Approximation by Superpositions of Sigmoidal Function. Math. Contr. Signals Syst., 2, 303-314.

Doesken, B., Abraham, A., Thomas, J., & Paprzycki, M. (2005). Real stock trading using soft computing models. Proceedings of International Symposium on Information Technology: Coding and Computing ITCC, 2, 162–167.

Dutta, M., Chatterjee, A., & Rakshit, A. (2006). Intelligent phase correction in automatic digital ac bridges by resilient backpropagation neural network. Measurement, 39, 884–891.

Esugasini, S., Mashor, M.Y., Isa, N.A.M., & Othman, N.H. (2005). Performance Comparison for MLP Networks Using Various Back Propagation Algorithms for Breast Cancer Diagnosis. Lecture Notes in Artificial Intelligence, 3682, 123-130.

Funahashi, K.-I. (1989). On the Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks, 2, 183-192.

Hornik, K. (1991). Approximation Capabilities of Multilayer Feedforward Networks. Neural Networks, 4, 251-257.

Jaruszewicz, M., & Mandziuk, J. (2004). One day prediction of NIKKEI Index considering information from other stock markets. Lecture Notes in Computer Science, 3070. Springer.

Riedmiller, M, & Braun, H. (1993). A Direct Adaptive Method for Faster backpropagation learning: The RPROP Algorithm. Proceedings of the IEEE Int. Conf. On Neural Networks, San Francisco, CA, March 28.

Rumelhart, D.E., Hinton G.E., & Williams R.J. (1986). Learning Representations by Back–Propagating Errors. Nature, 323, 533-536.

Temurtas, F., Yumusak, N., Gunturkun, R., Temurtas, H., & Cerezci., O. (2004). Elman’s Recurrent Neural Networks Using Resilient Back Propagation for Harmonic Detection. Lecture Notes in Artificial Intelligence, 3157, 422–428.

Zaqoot, H.A., Baloch, A., Ansari, A.K., & Unar, M.A. (2010). Application of artificial neural networks for predicting pH in seawater along Gaza beach. Applied Artificial Intelligence, 24, 667–679.

Zhang, G. P. (2001). An investigation of neural networks for linear time-series forecasting. Computers and Operations Research, 28, 1112–1183.

Zhang, G., Patuwo, B.E., & Hu, M.Y. (1998). Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting, 14 (1), 35–62.