Publications​

@article{turchettiRepresentationNonlinearRandom2008,

title = {Representation of Nonlinear Random Transformations by Non-Gaussian Stochastic Neural Networks},

author = {Claudio Turchetti and Paolo Crippa and Massimiliano Pirani and Giorgio Biagetti},

url = {https://ieeexplore.ieee.org/document/4460850},

doi = {10.1109/TNN.2007.2000055},

issn = {1941-0093},

year  = {2008},

date = {2008-06-01},

urldate = {2024-10-09},

journal = {IEEE Transactions on Neural Networks},

volume = {19},

number = {6},

pages = {1033–1060},

abstract = {The learning capability of neural networks is equivalent to modeling physical events that occur in the real environment. Several early works have demonstrated that neural networks belonging to some classes are universal approximators of input-output deterministic functions. Recent works extend the ability of neural networks in approximating random functions using a class of networks named stochastic neural networks (SNN). In the language of system theory, the approximation of both deterministic and stochastic functions falls within the identification of nonlinear no-memory systems. However, all the results presented so far are restricted to the case of Gaussian stochastic processes (SPs) only, or to linear transformations that guarantee this property. This paper aims at investigating the ability of stochastic neural networks to approximate nonlinear input-output random transformations, thus widening the range of applicability of these networks to nonlinear systems with memory. In particular, this study shows that networks belonging to a class named non-Gaussian stochastic approximate identity neural networks (SAINNs) are capable of approximating the solutions of large classes of nonlinear random ordinary differential transformations. The effectiveness of this approach is demonstrated and discussed by some application examples.},

keywords = {Approximation, Artificial Intelligence, Biology Computing, Computer Networks, Lee-Schetzen Method, Neural Computation, Neural networks, Nonlinear systems, Signal Processing, Stochastic Processes, Stochastic Resonance, Stochastic Systems},

pubstate = {published},

tppubtype = {article}

}

@article{orcioniAdvancesLeeSchetzen2005,

title = {Advances in Lee–Schetzen Method for Volterra Filter Identification},

author = {Simone Orcioni and Massimiliano Pirani and Claudio Turchetti},

url = {https://doi.org/10.1007/s11045-004-1677-7},

doi = {10.1007/s11045-004-1677-7},

issn = {1573-0824},

year  = {2005},

date = {2005-07-01},

urldate = {2024-10-09},

journal = {Multidimensional Systems and Signal Processing},

volume = {16},

number = {3},

pages = {265–284},

abstract = {This paper concerns the identification of nonlinear discrete causal systems that can be approximated with the Wiener–Volterra series. Some advances in the efficient use of Lee–Schetzen (L–S) method are presented, which make practical the estimate of long memory and high order models. Major problems in L–S method occur in the identification of diagonal kernel elements. Two approaches have been considered: approximation of gridded data, with interpolation or smoothing, and improved techniques for diagonal elements estimation. A comparison of diagonal elements estimated, with different methods has been shown with extended tests on fifth order Volterra systems.},

keywords = {Artificial Intelligence, Lee-Schetzen Method, Nonlinear System Identification, Volterra Filters, Wiener Kernels},

pubstate = {published},

tppubtype = {article}

}