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Adaptative time constants improve the prediction capability of recurrent neural networks., , , and . Neural Processing Letters, 2 (3): 12-16 (1995)Adaptative time constants improve the dynamic features of recurrent neural networks., , , and . ESANN, (1996)Evidence of efficiency of recurrent neural networks with ARMA-like units., , , and . ESANN, D-Facto public, (1997)An efficient learning model for the neural integrator of the oculomotor system., , , and . ESANN, (1993)Parallel Givens Factorization on a Shared Memory Multiprocessor., and . CONPAR, volume 457 of Lecture Notes in Computer Science, page 131-142. Springer, (1990)Negative initial weights improve learning in recurrent neural networks., , , , , and . ESANN, (1996)An inhibitory weight initialization improves the speed and quality of recurrent neural networks learning., , , and . Neurocomputing, 16 (3): 207-224 (1997)Distributed implementations of communicating objects., and . Concurr. Pract. Exp., 7 (6): 515-541 (1995)Combining pattern recognition techniques with Akaike's information criteria for identifying ARMA models., and . IEEE Trans. Signal Process., 42 (6): 1388-1396 (1994)Multicriteria approach for intelligent decision support in supervisory control., , and . Ann. Oper. Res., 51 (7): 327-347 (1994)