Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
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[1] A. D. Hestenes. The extrapolation, interpolation and smoothing of stationary time series with engineering applications: by Norbert Wiener. 163 pages, 15 × 24 cm. New York, John Wiley & Sons, Inc., 1949. Price, $4.00 , 1950 .
[2] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[3] E. Lorenz. Deterministic nonperiodic flow , 1963 .
[4] J. H. Wilkinson. The algebraic eigenvalue problem , 1966 .
[5] D. Sprecher. On the structure of continuous functions of several variables , 1965 .
[6] E. M. Wright,et al. Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.
[7] Marvin Minsky,et al. Perceptrons: An Introduction to Computational Geometry , 1969 .
[8] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[9] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[10] J. Makhoul,et al. Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.
[11] T. Apostol. Modular Functions and Dirichlet Series in Number Theory , 1976 .
[12] E. Jury. Stability of multidimensional scalar and matrix polynomials , 1978, Proceedings of the IEEE.
[13] Stephen A. Billings,et al. Identi cation of nonlinear systems-A survey , 1980 .
[14] R. Mañé,et al. On the dimension of the compact invariant sets of certain non-linear maps , 1981 .
[15] Philip E. Gill,et al. Practical optimization , 1981 .
[16] M. Schetzen,et al. Nonlinear system modeling based on the Wiener theory , 1981, Proceedings of the IEEE.
[17] Stephen Grossberg,et al. Classical and Instrumental Learning by Neural Networks , 1982 .
[18] Lennart Ljung,et al. Theory and Practice of Recursive Identification , 1983 .
[19] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[20] John E. Dennis,et al. Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.
[21] Lennart Ljung. Analysis of stochastic gradient algorithms for linear regression problems , 1984, IEEE Trans. Inf. Theory.
[22] Harold J. Kushner,et al. Approximation and Weak Convergence Methods for Random Processes , 1984 .
[23] R. Nitzberg. Application of the Normalized LMS Algorithm to MSLC , 1985, IEEE Transactions on Aerospace and Electronic Systems.
[24] Floris Takens,et al. On the numerical determination of the dimension of an attractor , 1985 .
[25] I. J. Leontaritis,et al. Input-output parametric models for non-linear systems Part II: stochastic non-linear systems , 1985 .
[26] J. P. Lasalle. The stability and control of discrete processes , 1986 .
[27] Neil J. Bershad,et al. Analysis of the normalized LMS algorithm with Gaussian inputs , 1986, IEEE Trans. Acoust. Speech Signal Process..
[28] R. Devaney. An Introduction to Chaotic Dynamical Systems , 1990 .
[29] Henry Stark,et al. Probability, Random Processes, and Estimation Theory for Engineers , 1995 .
[30] R. Hecht-Nielsen. Kolmogorov''s Mapping Neural Network Existence Theorem , 1987 .
[31] H. Szu,et al. Nonconvex optimization by fast simulated annealing , 1987, Proceedings of the IEEE.
[32] P. Vaidyanathan,et al. A unified structural interpretation of some well-known stability-test procedures for linear systems , 1987, Proceedings of the IEEE.
[33] R. Lippmann,et al. An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.
[34] C. Johnson,et al. Theory and design of adaptive filters , 1987 .
[35] Fernando J. Pineda,et al. GENERALIZATION OF BACKPROPAGATION TO RECURRENT AND HIGH-ORDER NETWORKS. , 1987 .
[36] Bernard Widrow,et al. Adaptive switching circuits , 1988 .
[37] M. B. Priestley,et al. Non-linear and non-stationary time series analysis , 1990 .
[38] E. Zeidler. Nonlinear functional analysis and its applications , 1988 .
[39] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.
[40] Arie Feuer,et al. Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data , 1988, IEEE Trans. Inf. Theory.
[41] J. Shynk,et al. Analysis of the data-reusing LMS algorithm , 1989, Proceedings of the 32nd Midwest Symposium on Circuits and Systems,.
[42] Ken-ichi Funahashi,et al. On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.
[43] S. Sastry,et al. Adaptive Control: Stability, Convergence and Robustness , 1989 .
[44] Ronald J. Williams,et al. Experimental Analysis of the Real-time Recurrent Learning Algorithm , 1989 .
[45] Tomaso A. Poggio,et al. Representation Properties of Networks: Kolmogorov's Theorem Is Irrelevant , 1989, Neural Computation.
[46] Yu He,et al. Asymptotic Convergence of Backpropagation: Numerical Experiments , 1989, NIPS.
[47] V.Z. Marmarelis,et al. Signal transformation and coding in neural systems , 1989, IEEE Transactions on Biomedical Engineering.
[48] Fernando J. Pineda,et al. Recurrent Backpropagation and the Dynamical Approach to Adaptive Neural Computation , 1989, Neural Computation.
[49] Geoffrey E. Hinton,et al. Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..
[50] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[51] P. G. Ciarlet,et al. Introduction to Numerical Linear Algebra and Optimisation , 1989 .
[52] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[53] Sheng Chen,et al. Representations of non-linear systems: the NARMAX model , 1989 .
[54] Jia Zhang,et al. Convergence and limit points of neural network and its application to pattern recognition , 1989, IEEE Trans. Syst. Man Cybern..
[55] J. Shynk. Adaptive IIR filtering , 1989, IEEE ASSP Magazine.
[56] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[57] V. Gholkar. Mean square convergence analysis of LMS algorithm (adaptive filters) , 1990 .
[58] Kumpati S. Narendra,et al. Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.
[59] John J. Shynk,et al. Convergence properties and stationary points of a perceptron learning algorithm , 1990 .
[60] Paul J. Werbos,et al. Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.
[61] A. Khotanzad,et al. Non-parametric prediction of AR processes using neural networks , 1990, International Conference on Acoustics, Speech, and Signal Processing.
[62] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[63] Kurt Hornik,et al. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.
[64] Russell C. Eberhart. Standardization of neural network terminology , 1990, IEEE Trans. Neural Networks.
[65] C.F.N. Cowan,et al. Performance comparison of RLS and LMS algorithms for tracking a first order Markov communications channel , 1990, IEEE International Symposium on Circuits and Systems.
[66] Neil E. Cotter,et al. The Stone-Weierstrass theorem and its application to neural networks , 1990, IEEE Trans. Neural Networks.
[67] Bernard Widrow,et al. 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.
[68] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[69] Thomas L. Clarke,et al. Generalization of neural networks to the complex plane , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[70] Jacek M. Zurada,et al. Sufficient condition for convergence of a relaxation algorithm in actual single-layer neural networks , 1990, IEEE Trans. Neural Networks.
[71] Heinz Unbehauen,et al. Structure identification of nonlinear dynamic systems - A survey on input/output approaches , 1990, Autom..
[72] Clark C. Guest,et al. Modification of backpropagation networks for complex-valued signal processing in frequency domain , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[73] L. Jones. Constructive approximations for neural networks by sigmoidal functions , 1990, Proc. IEEE.
[74] Les E. Atlas,et al. Recurrent Networks and NARMA Modeling , 1991, NIPS.
[75] C. N. Manikopoulos,et al. Neural net nonlinear prediction for speech data , 1991 .
[76] Kurt Hornik,et al. Convergence of learning algorithms with constant learning rates , 1991, IEEE Trans. Neural Networks.
[77] Jonathan J. Kaufman,et al. Volterra characterization of neural networks , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.
[78] V. J. Mathews. Adaptive polynomial filters , 1991, IEEE Signal Processing Magazine.
[79] K. P. Unnikrishnan,et al. Nonlinear prediction of speech signals using memory neuron networks , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.
[80] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[81] Richard A. Davis,et al. Time Series: Theory and Methods , 2013 .
[82] Javier R. Movellan,et al. Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks , 1991, IEEE Trans. Syst. Man Cybern..
[83] L. Personnaz,et al. Neural network training schemes for non-linear adaptive filtering and modelling , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[84] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[85] Visakan Kadirkamanathan,et al. A nonlinear model for time series prediction and signal interpolation , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[86] J. J. Shynk,et al. Statistical analysis of the single-layer backpropagation algorithm , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[87] B. Townshend,et al. Nonlinear prediction of speech , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[88] Mark E. Oxley,et al. Comparative Analysis of Backpropagation and the Extended Kalman Filter for Training Multilayer Perceptrons , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[89] Emile Fiesler,et al. Neural Network Formalization , 1992 .
[90] Cris Koutsougeras,et al. Complex domain backpropagation , 1992 .
[91] Peter J. Gawthrop,et al. Neural networks for control systems - A survey , 1992, Autom..
[92] L. K. Li. Approximation theory and recurrent networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.
[93] William A. Sethares,et al. Adaptive algorithms with nonlinear data and error functions , 1992, IEEE Trans. Signal Process..
[94] Shengwei Zhang,et al. Lagrange programming neural networks , 1992 .
[95] Ronald J. Williams,et al. Training recurrent networks using the extended Kalman filter , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.
[96] Markus Höhfeld,et al. Learning with limited numerical precision using the cascade-correlation algorithm , 1992, IEEE Trans. Neural Networks.
[97] A. Uncini Piazza,et al. Artificial neural networks with adaptive polynomial activation function , 1992 .
[98] S Z Qin,et al. Comparison of four neural net learning methods for dynamic system identification , 1992, IEEE Trans. Neural Networks.
[99] L. Ljung,et al. A system identification perspective on neural nets , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.
[100] Stephen A. Billings,et al. Properties of neural networks with applications to modelling non-linear dynamical systems , 1992 .
[101] Jhing-Fa Wang,et al. A data-reuse architecture for gray-scale morphologic operations , 1992 .
[102] Hideaki Sakai,et al. A real-time learning algorithm for a multilayered neural network based on the extended Kalman filter , 1992, IEEE Trans. Signal Process..
[103] Vera Kurková,et al. Kolmogorov's theorem and multilayer neural networks , 1992, Neural Networks.
[104] W. Wallace. Galileo’s Logical Treatises , 1992 .
[105] Giovanni Soda,et al. Local Feedback Multilayered Networks , 1992, Neural Computation.
[106] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[107] Francesco Piazza,et al. On the complex backpropagation algorithm , 1992, IEEE Trans. Signal Process..
[108] A. Tsoi,et al. Implementation issues of sigmoid function and its derivative for VLSI digital neural networks , 1992 .
[109] C. Micchelli,et al. Approximation by superposition of sigmoidal and radial basis functions , 1992 .
[110] C. P. Sheppard,et al. Predicting time series by a fully connected neural network trained by back propagation , 1992 .
[111] Andrew R. Barron,et al. Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.
[112] Henry Leung,et al. Rational Function Neural Network , 1993, Neural Computation.
[113] Pierre Roussel-Ragot,et al. Neural Networks and Nonlinear Adaptive Filtering: Unifying Concepts and New Algorithms , 1993, Neural Computation.
[114] Russell Reed,et al. Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.
[115] M. M. Gupta,et al. Dynamic neural unit and function approximation , 1993, IEEE International Conference on Neural Networks.
[116] Andreas S. Weigend,et al. The Future of Time Series: Learning and Understanding , 1993 .
[117] Yuichi Nakamura,et al. Approximation of dynamical systems by continuous time recurrent neural networks , 1993, Neural Networks.
[118] Andrzej Cichocki,et al. Neural networks for optimization and signal processing , 1993 .
[119] W. K. Jenkins,et al. New data-reusing LMS algorithms for improved convergence , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.
[120] Michael T. Manry,et al. Conventional modeling of the multilayer perceptron using polynomial basis functions , 1993, IEEE Trans. Neural Networks.
[121] V. John Mathews,et al. A stochastic gradient adaptive filter with gradient adaptive step size , 1993, IEEE Trans. Signal Process..
[122] Luigi Fortuna,et al. On the capability of neural networks with complex neurons in complex valued functions approximation , 1993, 1993 IEEE International Symposium on Circuits and Systems.
[123] Francesco Piazza,et al. Neural networks with digital LUT activation functions , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).
[124] Eric A. Wan,et al. Time series prediction by using a connectionist network with internal delay lines , 1993 .
[125] John J. Shynk,et al. Statistical analysis of the single-layer backpropagation algorithm. II. MSE and classification performance , 1993, IEEE Trans. Signal Process..
[126] Kurt Hornik,et al. Some new results on neural network approximation , 1993, Neural Networks.
[127] David A. Sprecher,et al. A universal mapping for kolmogorov's superposition theorem , 1993, Neural Networks.
[128] M. Mansour,et al. Stability of polynomials with time-variant coefficients , 1993 .
[129] James Theiler,et al. Detecting Nonlinearity in Data with Long Coherence Times , 1993, comp-gas/9302003.
[130] M. Sarhadi,et al. Performance aspects of a novel neuron activation function in multi-layer feed-forward networks , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).
[131] M. Sarhadi,et al. A modified neuron activation function which enables single layer perceptrons to solve some linearly inseparable problems , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).
[132] S. Haykin,et al. A cascaded recurrent neural network for real-time nonlinear adaptive filtering , 1993, IEEE International Conference on Neural Networks.
[133] Allan Pinkus,et al. Multilayer Feedforward Networks with a Non-Polynomial Activation Function Can Approximate Any Function , 1991, Neural Networks.
[134] José Carlos Príncipe,et al. The gamma-filter-a new class of adaptive IIR filters with restricted feedback , 1993, IEEE Trans. Signal Process..
[135] David L. Elliott,et al. A Better Activation Function for Artificial Neural Networks , 1993 .
[136] Mahesan Niranjan,et al. A dynamic neural network architecture by sequential partitioning of the input space , 1993, IEEE International Conference on Neural Networks.
[137] James Ting-Ho Lo,et al. Synthetic approach to optimal filtering , 1994, IEEE Trans. Neural Networks.
[138] Liang Jin,et al. Absolute stability conditions for discrete-time recurrent neural networks , 1994, IEEE Trans. Neural Networks.
[139] Lizhong Wu,et al. On the design of nonlinear speech predictors with recurrent nets , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.
[140] Hidefumi Katsuura,et al. Computational aspects of Kolmogorov's superposition theorem , 1994, Neural Networks.
[141] Donald E. Waagen,et al. Evolving recurrent perceptrons for time-series modeling , 1994, IEEE Trans. Neural Networks.
[142] P. Regalia. Adaptive IIR Filtering in Signal Processing and Control , 1994 .
[143] A. Tesi,et al. Conditions for global stability of some classes of nonsymmetric neural networks , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.
[144] Andreas S. Weigend,et al. Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .
[145] Xuan Kong,et al. Adaptive Signal Processing Algorithms: Stability and Performance , 1994 .
[146] Amir F. Atiya,et al. How delays affect neural dynamics and learning , 1994, IEEE Trans. Neural Networks.
[147] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[148] Pierre Roussel-Ragot,et al. Training recurrent neural networks: why and how? An illustration in dynamical process modeling , 1994, IEEE Trans. Neural Networks.
[149] S. Douglas. A family of normalized LMS algorithms , 1994, IEEE Signal Processing Letters.
[150] Les E. Atlas,et al. Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.
[151] Martin Brown,et al. Aspects of instantaneous on-line learning rules , 1994 .
[152] Uwe Helmke,et al. Neural networks, rational functions, and realization theory , 1995, Math. Control. Signals Syst..
[153] Uwe Helmke,et al. Existence and uniqueness results for neural network approximations , 1995, IEEE Trans. Neural Networks.
[154] Ronald J. Williams,et al. Gradient-based learning algorithms for recurrent networks and their computational complexity , 1995 .
[155] M. Mansour,et al. Robust stability of time-variant discrete-time systems with bounded parameter perturbations , 1995 .
[156] G. U. Yule,et al. The Foundations of Econometric Analysis: On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer's Sunspot Numbers ( Philosophical Transactions of the Royal Society of London , A, vol. 226, 1927, pp. 267–73) , 1995 .
[157] Scott C. Douglas,et al. Exact expectation analysis of the LMS adaptive filter , 1995, IEEE Trans. Signal Process..
[158] Tamer Başar,et al. H1-Optimal Control and Related Minimax Design Problems , 1995 .
[159] L. Ljung,et al. Exponential stability of general tracking algorithms , 1995, IEEE Trans. Autom. Control..
[160] William Fornaciari,et al. Behavior-driven minimal implementation of digital ANNs , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.
[161] Eduardo Sontag,et al. Computational power of neural networks , 1995 .
[162] Marios M. Polycarpou,et al. High-order neural network structures for identification of dynamical systems , 1995, IEEE Trans. Neural Networks.
[163] Liang Li,et al. Nonlinear adaptive prediction of nonstationary signals , 1995, IEEE Trans. Signal Process..
[164] R. Pearson. Nonlinear Input/Output Modeling , 1994 .
[165] John H. Mathews,et al. Complex analysis for mathematics and engineering , 1995 .
[166] Kazuo Tanaka,et al. Stability analysis of neural networks via Lyapunov approach , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.
[167] Sailes K. Sengijpta. Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .
[168] Shaun-Inn Wu. Mirroring our thought processes [recurrent neural network and time series in forecasting] , 1996 .
[169] Vincenzo Piuri,et al. Experimental neural networks for prediction and identification , 1996 .
[170] S. Zakeri. On Critical Points of Proper Holomorphic Maps on The Unit Disk , 1996, math/9606221.
[171] Masahiro Nakagawa,et al. An autonomously controlled chaos neural network , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).
[172] Emile Fiesler,et al. The Interchangeability of Learning Rate and Gain in Backpropagation Neural Networks , 1996, Neural Computation.
[173] Gene H. Golub,et al. Matrix computations (3rd ed.) , 1996 .
[174] K. S. Narendra,et al. Neural networks for control theory and practice , 1996, Proc. IEEE.
[175] M. Glesner,et al. Searching for robust chaos in discrete time neural networks using weight space exploration , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).
[176] Gaetan Libert,et al. Dynamic recurrent neural networks: a dynamical analysis , 1996, IEEE Trans. Syst. Man Cybern. Part B.
[177] Liang Jin,et al. Globally asymptotical stability of discrete-time analog neural networks , 1996, IEEE Trans. Neural Networks.
[178] Simon Haykin,et al. Neural networks expand SP's horizons , 1996, IEEE Signal Process. Mag..
[179] Yoshua Bengio,et al. Neural networks for speech and sequence recognition , 1996 .
[180] B. Kosko,et al. What is the best shape for a fuzzy set in function approximation? , 1996, Proceedings of IEEE 5th International Fuzzy Systems.
[181] Russell Beale,et al. Handbook of Neural Computation , 1996 .
[182] Peter Tiño,et al. Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.
[183] Francesco Piazza,et al. Approximation capabilities of adaptive spline neural networks , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).
[184] Hava T. Siegelmann,et al. Computational capabilities of recurrent NARX neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.
[185] Emile Fiesler,et al. Optimal Setting of Weights, Learning Rate, and Gain , 1997 .
[186] V. Lazarescu,et al. On viewing the transform performed by a hidden layer in a feedforward ANN, as a complex Mobius mapping , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).
[187] Scott C. Douglas,et al. Adaptive filters employing partial updates , 1997 .
[188] Toru Yamaguchi,et al. Necessary and Sufficient Condition for Absolute Exponential Stability of a Class of Nonsymmetric Neural Networks , 1997 .
[189] Sun-Yuan Kung,et al. A delay damage model selection algorithm for NARX neural networks , 1997, IEEE Trans. Signal Process..
[190] A. Tikhonov,et al. Nonlinear Ill-Posed Problems , 1997 .
[191] Gilles Pagès,et al. Approximations of Functions by a Multilayer Perceptron: a New Approach , 1997, Neural Networks.
[192] K. Aihara,et al. Chaos and asymptotical stability in discrete-time neural networks , 1997, chao-dyn/9701020.
[193] Bart Kosko,et al. Adaptive joint fuzzy sets for function approximation , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).
[194] Po-Rong Chang,et al. Optimal Nonlinear Adaptive Prediction and Modeling of MPEG Video in ATM Networks Using Pipelined Recurrent Neural Networks , 1997, IEEE J. Sel. Areas Commun..
[195] K. M. Al-Ruwaihi. CMOS analogue neurone circuit with programmable activation functions utilising MOS transistors with optimised process/device parameters , 1997 .
[196] Emile Fiesler,et al. High-order and multilayer perceptron initialization , 1997, IEEE Trans. Neural Networks.
[197] R. de Figueiredo. Optimal neural network realizations of nonlinear FIR and IIR filters , 1997, Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97.
[198] R.J. Cohen,et al. Linear and nonlinear ARMA model parameter estimation using an artificial neural network , 1997, IEEE Transactions on Biomedical Engineering.
[199] L. Ljung,et al. Necessary and sufficient conditions for stability of LMS , 1997, IEEE Trans. Autom. Control..
[200] Sandro Ridella,et al. Circular backpropagation networks for classification , 1997, IEEE Trans. Neural Networks.
[201] Jose C. Principe,et al. The past, present, and future of neural networks for signal processing , 1997 .
[202] James R. Zeidler,et al. Adaptive tracking of linear time-variant systems by extended RLS algorithms , 1997, IEEE Trans. Signal Process..
[203] S. Douglas,et al. A posteriori updates for adaptive filters , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).
[204] Rafik A. Goubran,et al. Nonlinear adaptive filtering with FIR synapses and adaptive activation functions , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[205] Michael I. Jordan. Serial Order: A Parallel Distributed Processing Approach , 1997 .
[206] Yu Wei,et al. Combining adaptive sigmoid packet and trace neural network for fast invariance-learning , 1998 .
[207] C A Bailer-Jones,et al. A recurrent neural network for modelling dynamical systems. , 1998, Network.
[208] Sung-Suk Kim. Time-delay recurrent neural network for temporal correlations and prediction , 1998, Neurocomputing.
[209] Danilo P. Mandic,et al. A posteriori real-time recurrent learning schemes for a recurrent neural network based nonlinear predictor , 1998 .
[210] Danilo P. Mandic,et al. Advanced PRNN based nonlinear prediction/system identification , 1998 .
[211] I. Hlavácek,et al. A posteriori error estimates for three-dimensional axisymmetric elliptic problems , 1998 .
[212] David A. Medler. A Brief History of Connectionism , 1998 .
[213] K. Rose. Deterministic annealing for clustering, compression, classification, regression, and related optimization problems , 1998, Proc. IEEE.
[214] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[215] Jonathon A. Chambers,et al. From an a priori RNN to an a posteriori PRNN nonlinear predictor , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).
[216] Danilo P. Mandic,et al. Non-Linear Adaptive Prediction of Speech with a Pipelined Recurrent Neural Network and Advanced Learning Algorithms , 1998 .
[217] K. Nakayama,et al. A Cascade Form Predictor of Neural and FIR Filters and Its Minimum Size Estimation Based on Nonlinearity Analysis of Time Series , 1998 .
[218] P. Arena,et al. Neural Networks in Multidimensional Domains: Fundamentals and New Trends in Modelling and Control , 1998 .
[219] Alexander N Gorban,et al. The general approximation theorem , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[220] Jacques Sombrin,et al. Neural network modeling and identification of nonlinear channels with memory: algorithms, applications, and analytic models , 1998, IEEE Trans. Signal Process..
[221] Gérard Dreyfus,et al. Comment on "Discrete-time recurrent neural network architectures: A unifying review" , 1998, Neurocomputing.
[222] Jenq-Neng Hwang,et al. Neural networks for intelligent multimedia processing , 1998 .
[223] B. Ermentrout. Neural networks as spatio-temporal pattern-forming systems , 1998 .
[224] Yizhak Idan,et al. The Canonical Form of Nonlinear Discrete-Time Models , 1998, Neural Computation.
[225] Yong Soo Cho,et al. Adaptive precompensation of Wiener systems , 1998, IEEE Trans. Signal Process..
[226] Majid Ahmadi,et al. Model validation and determination for neural network activation function modeling , 1998, 1998 Midwest Symposium on Circuits and Systems (Cat. No. 98CB36268).
[227] Javier Calpe-Maravilla,et al. An easy demonstration of the optimum value of the adaptation constant in the LMS algorithm [FIR filt , 1998 .
[228] Valeriu Beiu,et al. On Kolmogorov's superpositions and Boolean functions , 1998, Proceedings 5th Brazilian Symposium on Neural Networks (Cat. No.98EX209).
[229] Danilo P. Mandic,et al. Toward an optimal PRNN-based nonlinear predictor , 1999, IEEE Trans. Neural Networks.
[230] Andrew Chi-Sing Leung,et al. On the Kalman filtering method in neural network training and pruning , 1999, IEEE Trans. Neural Networks.
[231] R. Devaney. The Mandelbrot Set, the Farey Tree, and the Fibonacci Sequence , 1999 .
[232] Danilo P. Mandic,et al. Global asymptotic convergence of nonlinear relaxation equations realised through a recurrent perceptron , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[233] J. A. Chambers,et al. Exploiting inherent relationships in RNN architectures , 1999, Neural Networks.
[234] Allan Pinkus,et al. Lower bounds for approximation by MLP neural networks , 1999, Neurocomputing.
[235] S. Haykin,et al. Lessons on adaptive systems for signal processing, communications, and control , 1999, IEEE Signal Processing Magazine.
[236] Norbert Jankowski,et al. Survey of Neural Transfer Functions , 1999 .
[237] Kenji Nakayama,et al. A Hybrid Nonlinear Predictor: Analysis of Learning Process and Predictability for Noisy Time Series (Special Section on Digital Signal Processing) , 1999 .
[238] Danilo P. Mandic,et al. A Nonlinear Adaptive Predictor Realised Via Recurrent Neural Networks With Annealing , 1999 .
[239] Francesco Piazza,et al. Multilayer feedforward networks with adaptive spline activation function , 1999, IEEE Trans. Neural Networks.
[240] Simon Haykin,et al. A dynamic regularized radial basis function network for nonlinear, nonstationary time series prediction , 1999, IEEE Trans. Signal Process..
[241] Alan V. Oppenheim,et al. Discrete-time signal processing (2nd ed.) , 1999 .
[242] Persi Diaconis,et al. Iterated Random Functions , 1999, SIAM Rev..
[243] Danilo P. Mandic,et al. A posteriori error learning in nonlinear adaptive filters , 1999 .
[244] Paul C. Kainen,et al. Approximation by neural networks is not continuous , 1999, Neurocomputing.
[245] Kurt Kreith,et al. Iterative Algebra and Dynamic Modeling: A Curriculum for the Third Millennium , 1999 .
[246] F. Becker,et al. Detecting nonlinearities in time series of machining processes , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).
[247] Thomas Elsken,et al. Smaller nets may perform better: special transfer functions , 1999, Neural Networks.
[248] Igor R. Krcmar,et al. Global asymptotic stability for RNNs with a bipolar activation function , 2000, Proceedings of the 5th Seminar on Neural Network Applications in Electrical Engineering. NEUREL 2000 (IEEE Cat. No.00EX287).
[249] Danilo P. Mandic,et al. On global asymptotic stability of fully connected recurrent neural networks , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[250] D. Mandic,et al. On stability of relaxive systems described by polynomials with time-variant coefficients , 2000 .
[251] John N. Tsitsiklis,et al. A survey of computational complexity results in systems and control , 2000, Autom..
[252] D. P. Mandic,et al. NNGD algorithm for neural adaptive filters , 2000 .
[253] Eduardo D. Sontag,et al. Neural Systems as Nonlinear Filters , 2000, Neural Computation.
[254] Danilo P. Mandic,et al. A normalized gradient algorithm for an adaptive recurrent perceptron , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[255] Misha Tsodyks,et al. Chaos in neural networks with dynamic synapses , 2000, Neurocomputing.
[256] Peter B. Luh,et al. Lagrangian relaxation neural networks for job shop scheduling , 2000, IEEE Trans. Robotics Autom..
[257] Danilo P. Mandic,et al. Relationships Between the A Priori and A Posteriori Errors in Nonlinear Adaptive Neural Filters , 2000, Neural Computation.
[258] Danilo P. Mandic,et al. Advanced RNN Based NARMA Predictors , 2000, J. VLSI Signal Process..
[259] S. Haykin. Unsupervised adaptive filtering, vol. 1: Blind source separation , 2000 .
[260] Danilo P. Mandic,et al. On fixed points of a general neural network via Mobius transformations , 2000 .
[261] Hung T. Nguyen,et al. Fuzzy systems are universal approximators for a smooth function and its derivatives , 2000 .
[262] D. Mandic. The use of Mobius transformations in neural networks and signal processing , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).
[263] Danilo P. Mandic,et al. A normalised real time recurrent learning algorithm , 2000, Signal Process..
[264] Danilo P. Mandic,et al. On training with slope adaptation for feedforward NNs , 2000, Proceedings of the 5th Seminar on Neural Network Applications in Electrical Engineering. NEUREL 2000 (IEEE Cat. No.00EX287).
[265] D. Mandic,et al. On robust stability of time-variant discrete-time nonlinear systems with bounded parameter perturbations , 2000 .
[266] D. Mandic,et al. Stability of NNGD algorithm for nonlinear system identification , 2001 .
[267] Gavin C. Cawley,et al. On Nonlinear Processing of Air Pollution Data , 2001 .
[268] G. Lewicki,et al. Approximation by Superpositions of a Sigmoidal Function , 2003 .