Antonello Rizzi
Professore Associato
E-mail:
| Titolo | Pubblicato in | Anno |
|---|---|---|
| ANFIS synthesis by clustering for microgrids EMS design | Proc. of 9th International Joint Conference on Computational Intelligence – IJCCI 2017 | 2017 |
| Efficient approaches for solving the large-scale k-medoids problem | 2017 | |
| Design and validation of a contactless charging system for electric bicycles | RTSI 2017 - IEEE 3rd International Forum on Research and Technologies for Society and Industry, Conference Proceedings | 2017 |
| IL PROGETTO “HI-ZEV” | Memorie ET2017 | 2017 |
| IL PROGETTO “LIFE FOR SILVER COAST” | Memorie ET2017 | 2017 |
| A Smoothing Technique for the Multifractal Analysis of a Medium Voltage Feeders Electric Current | INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS IN APPLIED SCIENCES AND ENGINEERING | 2017 |
| Properties and training in recurrent neural networks | Recurrent Neural Networks for Short-Term Load Forecasting. An Overview and Comparative Analysis | 2017 |
| Recurrent neural network architectures | Recurrent Neural Networks for Short-Term Load Forecasting. An Overview and Comparative Analysis | 2017 |
| Other recurrent neural networks models | Recurrent Neural Networks for Short-Term Load Forecasting - An Overview and Comparative Analysis | 2017 |
| Synthetic time series | Recurrent Neural Networks for Short-Term Load Forecasting - An Overview and Comparative Analysis | 2017 |
| Real-world load time series | Recurrent Neural Networks for Short-Term Load Forecasting. An Overview and Comparative Analysis | 2017 |
| Experiments | Recurrent Neural Networks for Short-Term Load Forecasting. An Overview and Comparative Analysis | 2017 |
| Conclusions | Recurrent Neural Networks for Short-Term Load Forecasting. An Overview and Comparative Analysis | 2017 |
| Introduction | Recurrent Neural Networks for Short-Term Load Forecasting. An Overview and Comparative Analysis | 2017 |
| Two density-based k-means initialization algorithms for non-metric data clustering | PATTERN ANALYSIS AND APPLICATIONS | 2016 |
| Granular computing techniques for classification and semantic characterization of structured data | COGNITIVE COMPUTATION | 2016 |
| On the impact of topological properties of smart grids in power losses optimization problems | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS | 2016 |
| A dissimilarity learning approach by evolutionary computation for faults recognition in smart grids | Computational intelligence | 2016 |
| Noise sensitivity of an information granules filtering procedure by genetic optimization for inexact sequential pattern mining | Computational intelligence | 2016 |
| Occupational diseases risk prediction by genetic optimization. Towards a non-exclusive classification approach | Computational intelligence | 2016 |
Non-linear circuits and systems, Soft Computing, Pattern Recognition, Computational Intelligence, Granular Computing and Knowledge Discovery, Complex Systems modelling and control, Supervised and unsupervised data driven modeling techniques, neural networks, fuzzy systems, evolutionary algorithms. Design of automatic modeling systems, clustering, classification, function approximation, prediction.Computational Intelligence techniques for Smart Grids and Micro Grids modelling, monitoring and control, Intelligent Systems for Sustainable Mobility, Energy Storage Systems modeling and control, Predictive Diagnostc systems for Condition Based Maintenance, Big Data for Industrial Applications, Classification and clustering systems in structured domains, Machine learning in non-metric spaces, graph and sequence matching, agent-based clustering and substructures mining, parallel and distributed computing. Machine learning for natural language processing, cybersecurity, bioinformatics, medical diagnosis.
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