In the paper a two-layer encoder is proposed. The nodes of encoder under consideration are neo-fuzzy neurons, which are characterised by high speed of learning process and effective approximation properties. The proposed architecture of neo-fuzzy encoder has a two-layer bottle neck” structure and its learning algorithm is based on error backpropagation. The learning algorithm is characterised by a high rate of convergence because the output signals of encoder’s nodes (neo-fuzzy neurons) are linearly dependent on the tuning parameters. The proposed learning algorithm can tune both the synaptic weights and centres of membership functions. Thus, in the paper the hybrid neo-fuzzy system-encoder is proposed that has essential advantages over conventional neurocompressors.
 J. Han and M. Kamber, Data Mining: Concepts and Techniques. Amsterdam: Morgan Kaufman Publ., 2006.
 C. C. Aggarwal, Data Mining. N.Y.: Springer, 2015.
 А. Cichocki and R. Unbehauen, Neural networks for optimization and signal processing. Stuttgart: Teubner, 1993.
 S. Haykin, Neural Networks and Learning Machines. Upper Saddle River, New Jersey: Pearson, Prentice Hall, 2009.
 Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436-444, May 2015. https://doi.org/10.1038/nature14539
 J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85-117, Jan. 2015. https://doi.org/10.1016/j.neunet.2014.09.003
 I. Goodfellow, Y. Bengio and A. Courville, Deep learning. MIT Press, 2016.
 A. Bifet, Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams. Amsterdam: IOS Press, 2010.
 C. C. Aggarwal, Data Streams: Models and Algorithms. Kluwer Academic Publishers Boston/Dordrecht/London, 2007.
 V. Kolodyazhniy and Y. Bodyanskiy, “Fuzzy Kolmogorov’s Network,” Knowledge-Based Intelligent Information and Engineering Systems, pp. 764-771, 2004. https://doi.org/10.1007/978-3-540-30133-2_100
 Y. Bodyanskiy, V. Kolodyazhniy, and P. Otto, “Neuro-Fuzzy Kolmogorov’s Network for Time Series Prediction and Pattern Classification,” Lecture Notes in Computer Science, pp. 191-202, 2005. https://doi.org/10.1007/11551263_16
 V. Kolodyazhniy, Y. Bodyanskiy, and P. Otto, “Universal Approximator Employing Neo-Fuzzy Neurons,” Computational Intelligence, Theory and Applications, pp. 631-640. https://doi.org/10.1007/3-540-31182-3_58
 V. Kolodyazhniy, Ye. Bodyanskiy, V. Poyedyntseva, and A. Stephan, “Neuro-fuzzy Kolmogorov’s network with a modified perceptron learning rule for classification problems,” Computational Intelligence, Theory and Applications, pp. 41-49. https://doi.org/10.1007/3-540-34783-6_6
 Ye. Bodyanskiy, Ye. Gorshkov, V. Kolodyazhniy, and V. Poyedyntseva “Neuro-fuzzy Kolmogorov's network,” in Lecture Notes in Computer Science, vol. 3697, W. Duch, J. Kacprzyk, E. Oja, and S. Zadrozny, Eds., Berlin-Heidelberg: Springer-Verlag, pp. 1-6, 2005.
 T. Yamakawa, “A novel nonlinear synapse neuron model guaranteeing a global minimum-Wavelet Neuron,” Proceedings. 1998 28th IEEE International Symposium on Multiple- Valued Logic (Cat. No. 98CB36138). https://doi.org/10.1109/ismvl.1998.679510
 E. Uchino and T. Yamakawa, “Soft Computing Based Signal Prediction, Restoration, and Filtering,” Intelligent Hybrid Systems, pp. 331-351, 1997. https://doi.org/10.1007/978-1-4615-6191-0_14
 T. Yamakawa, E. Uchino, T. Miki and H. Kusanagi, “A neo-fuzzy neuron and its applications to system iden-tification and prediction of the system behavior,” in Proc. of 2-nd Int. Conf. on Fuzzy Logic and Neural Networks “IIZUKA-92”, Iizuka, Japan, pp. 477-483, 1992.
 J. -S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. N.J.: Prentice Hall, 1997.
 Y. Yam, H. T. Nguyen, and V. Kreinovich, “Multi-resolution techniques in the rules-based intelligent control systems: a universal approximation result,” Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No. 99CH37014), 1999. https://doi.org/10.1109/isic.1999.796657
 UCI Repository of machine learning databases. CA: University of California, Department of Information and Computer Science. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html