Augusto Montisci

Dr. Augusto Montisci

University of Cagliari, Cagliari, Sardegna, Italy
orcid

Research Focus & Metrics

Academic Career Length

28Years (1999-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 November 2004 in Neural Computing and Applications
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Journal Article
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BCBarbara Cannas
Barbara Cannas
Alessandra FanniAlessandra Fanni
Alessandra Fanni
Stefano ManettiStefano Manetti
Stefano Manetti
Augusto MontisciAugusto Montisci
Augusto Montisci
MPMaria Cristina Piccirilli
Maria Cristina Piccirilli

1 October 2006 in Engineering Applications of Artificial Intelligence
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Journal Article
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FCFrancesca Cau
Francesca Cau
AFAlessandra Fanni
Alessandra Fanni
Augusto MontisciAugusto Montisci
Augusto Montisci
PTPietro Testoni
Pietro Testoni
MUMariangela Usai
Mariangela Usai

20 May 2008 in IEEE Transactions on Magnetics
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Journal Article
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Sara CarcangiuSara Carcangiu
Sara Carcangiu
Alessandra FanniAlessandra Fanni
Alessandra Fanni
Augusto MontisciAugusto Montisci
Augusto Montisci
In this paper, an original algorithm to solve multiobjective optimization problems, which makes use of the tabu search meta-heuristic, is presented. Scalarization of the vector problem is performed by introducing fitness functions that take under control both the Pareto optimality of the solutions, and the uniformity in the Pareto front sampling. The performance of the proposed algorithm is compared with that of a scalar tabu search method, coupled with the -constraint strategy. The results on analytical and electromagnetic problems demonstrate the effectiveness of the method.

1 January 2008 in Neurocomputing
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Journal Article
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RDRita Delogu
Rita Delogu
AFAlessandra Fanni
Alessandra Fanni
Augusto MontisciAugusto Montisci
Augusto Montisci

16 May 2005 in IEEE Transactions on Magnetics
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Journal Article
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DCD. Cherubini
D. Cherubini
AFA. Fanni
A. Fanni
Augusto MontisciAugusto Montisci
Augusto Montisci
PTP. Testoni
P. Testoni
In this work, a neural-based approach for inverse problems in the field of electromagnetic devices design is presented. A multilayer perceptron neural network is first trained to solve the analysis problem of the studied system. As a design problem can be formulated as an inverse problem, i.e., starting from the design requirements the optimal values of the design parameters have to be obtained, the input of the neural network will correspond to the design parameters while the output is the objective function of the optimization problem. In this work, a procedure is presented which performs the inversion of the trained neural network when the design requirements are assigned to the output.

24 October 2005
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Conference Paper
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ABA.N. Belbachir
A.N. Belbachir
MLM. Lera
M. Lera
AFA. Fanni
A. Fanni
Augusto MontisciAugusto Montisci
Augusto Montisci