Abu-mostafa YS. (1989). The Vapnik-Chervonenkis dimension. Information versus complexity in learning Neural Comput. 1
Azoff EM. (1993). Reducing error in neural network time series forecasting Neural Computation And Applications. 1
Bienenstock E, Geman S, Doursat R. (1992). Neural networks and the bias-variance dilemma Neural Comput. 4
Billings S, Zheng GL. (1995). Radial basis function network configuration using genetic algorithms Neural Netw. 8
Bishop CH. (1991). Improving the generalization properties of radial basis function neural networks Neural Comput. 3
Bishop CH. (1995). Training with noise is equivalent to Tikhonov regularization Neural Comput. 7
Ch_bishop . (1995). Neural networks for pattern recognition.
Chen D et al. (1995). Constructive learning of recurrent neural networks: Limitations of recurrent cascade correlation and a simple solution IEEE Trans Neural Networks. 6
Czernichow T. (1997). A double gradient algorithm to optimize regularization Proc Artificial Neural Networks-ICANN.
Doering A, Galicki M, Witte H. (1997). Structure optimization of neural networks with the A*-algorithm. IEEE transactions on neural networks. 8 [PubMed]
Fabri S, Kadirkamanathan V. (1996). Dynamic structure neural networks for stable adaptive control of nonlinear systems. IEEE transactions on neural networks. 7 [PubMed]
Fahlman S. (1991). The recurrent cascade-correlation architecture Advances in neural information processing systems. 3
Fedorenko RR. (1978). Approximate solutions of optimal control problems.
Filippov AF. (1962). On certian questions in the theory of optimal control SIAM J Control. 1
Fukunaga K. (1990). Introduction to statistical pattern recognition (2nd ed).
Galicki M. (1998). The planning of robotic optimal motions in the presence of obstacles Int J Robot Res. 17
Galicki M, Leistritz L, Witte H. (1999). Learning continuous trajectories in recurrent neural networks with time-dependent weights. IEEE transactions on neural networks. 10 [PubMed]
Hammer B. (1997). Generalization of Elman networks Proc Artificial Neural Networks-ICANN.
Hinton GE. (1987). Learning translation invariant recognition in a massively parallel network PARLE: Parallel architectures and languages Europe .
Holden SB, Niranjan M. (1995). On the practical applicability of VC dimension bounds. Neural computation. 7 [PubMed]
Leung CS, Tsoi AC, Chan LW. (2001). Two regularizers for recursive least squared algorithms in feedforward multilayered neural networks. IEEE transactions on neural networks. 12 [PubMed]
Ma S, Ji C. (1998). Fast training of recurrent networks based on the EM algorithm. IEEE transactions on neural networks. 9 [PubMed]
Marom E, Saad D, Cohen B. (1997). Efficient Training of Recurrent Neural Network with Time Delays. Neural networks : the official journal of the International Neural Network Society. 10 [PubMed]
Mcavoy TJ, Bhat NV. (1990). Use of neural nets for dynamic modelling and control of chemical process systems Computers And Chemical Engineering. 14
Mcavoy TJ, Bhat NV. (1992). Determining model structure for neural models by network stripping Computers And Chemical Engineering. 16
Mischenko EF, Pontryagin LC, Boltyansky VC, Gamkrelidze RV. (1961). Mathematical theory of optimal problem.
Moody J, Wu L. (1996). A smoothing regularizer for feedforward and recurrent neural networks Neural Comput. 8
Morozov VA. (1984). Methods for solving incorrectly posed problems.
Murata N, Yoshizawa S, Amari S. (1994). Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE transactions on neural networks. 5 [PubMed]
Pearlmutter BA. (1995). Gradient calculation for dynamic recurrent neural networks: A survey IEEE Trans Neural Networks. 6
Poggio T, Girosi F, Jones M. (1995). Regularization theory and neural network architectures Neural Comput. 7
Rasmussen CE, Hansen LK. (1994). Pruning from adaptive regularization Neural Comput. 6
Rementeria S, Olabe XB. (2000). Predicting sunspots with a self-configuring neural system Proc. of the 8th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems.
Sakawa Y, Shindo Y. (1980). On global convergence of an algorithm for optimal control IEEE Trans Automatic Control. 25
Siegelmann HT, Kiliom J. (1996). The dynamic universality of sigmoidal neural networks Information And Computation. 128
Sontag ED, Dasgupta B. (1996). Sample complexity for learning recurrent perceptron mappings IEEE Trans Inform Theory. 42
Sridhar DV, Bartlett EB, Seagrave RC. (1998). Information theoretic subset selection for neural network models Computers And Chemical Engineering. 22
Srochko VA. (1986). Application of maximum principle for numerical solution of optimal control problems with terminal state constraints Kibernetika. 1
Strend S, Balden J. (1990). A comparison of constrained optimal control algorithms Prep 11th IFAC World Congress.
Sudareshan MK, Condarcure TA. (1998). Recurrent neural-network training by a learning automaton approach for trajectory learning and control system design. IEEE transactions on neural networks. 9 [PubMed]
Tikhonov AN, Arsenin VY. (1977). Solution of ill-posed problems.
Tresp V, Taniguchi M. (1997). Combining regularized networks Proc Artificial Neural Networks-ICANN.
Vapnik VN, Chervonenkis AY. (1971). On the uniform convergence of relative frequencies of events to their probabilities Theory Of Probability And Its Applications. 16
Victorri B, Le_Cun Y, Simard P, Denker J. (1991). Tangent Prop-A formalism for specifying selected invariances in an adaptive network Advances in neural information processing systems. 4
Wan EA. (1997). Combining fossil and sunspot data: Committee predictions Proc. of the 1997 International Conference on Neural Networks.
Witte H, Galicki M, Leistritz L. (1998). Training continuous trajectories by means of dynamic neural networks with time dependent weights Proc Int ICSC-IFAC Symp Neural Networks.
Witte H, Galicki M, Leistritz L. (2000). Improved learning of multiple continuous trajectories with initial network state Proc 2000 Int Joint Conference on Neural Networks.