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Luigi Fortuna

Industrial University of Ho Chi Minh City

ORCID: 0000-0003-2285-2979

Publishes on Neural Networks and Applications, Neural Networks Stability and Synchronization, Chaos control and synchronization. 906 papers and 16.5k citations.

906Publications
16.5kTotal Citations

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Top publicationsby citations

Fractional Order Systems: Modeling and Control Applications
Cited by 587

This book aims to propose the implementation and application of Fractional Order Systems (FOS). It is well known that FOS can be utilized in control applications and systems modeling, and their effectiveness has been proven in many theoretical works and simulation routines. A further and mandatory step for FOS real world utilization is their hardware implementation and applications on real systems modeling. With this viewpoint, introductory chapters are included on the definition of stability region of Fractional Order PID Controller and Chaotic FOS, followed by the practical implementation based on Microcontroller, Field Programmable Gate Array, Field Programmable Analog Array and Switched Capacitor. Another section is dedicated to FO modeling of Ionic Polymeric Metal Composite (IPMC). This new material will have applications in robotics, aerospace and biomedicine

Chaotic sequences to improve the performance of evolutionary algorithms
Riccardo Caponetto, Luigi Fortuna, S. Fazzino et al.|IEEE Transactions on Evolutionary Computation|2003
Cited by 511

This paper proposes an experimental analysis on the convergence of evolutionary algorithms (EAs). The effect of introducing chaotic sequences instead of random ones during all the phases of the evolution process is investigated. The approach is based on the substitution of the random number generator (RNG) with chaotic sequences. Several numerical examples are reported in order to compare the performance of the EA using random and chaotic generators as regards to both the results and the convergence speed. The results obtained show that some chaotic sequences are always able to increase the value of some measured algorithm-performance indexes with respect to random sequences. Moreover, it is shown that EAs can be extremely sensitive to different RNGs. Some t-tests were performed to confirm the improvements introduced by the proposed strategy.