His primary areas of investigation include Artificial neural network, Algorithm, Artificial intelligence, Multilayer perceptron and Mathematical optimization. His studies link Adaptive system with Artificial neural network. His biological study spans a wide range of topics, including Signal reconstruction, Blind equalization and Intersymbol interference.
Francesco Piazza has included themes like Machine learning, Maximum a posteriori estimation and Pattern recognition in his Artificial intelligence study. His work is dedicated to discovering how Multilayer perceptron, Signal processing are connected with Structure and other disciplines. The various areas that he examines in his Mathematical optimization study include Quantization, Energy management, Algorithm design, Smart grid and Power of two.
The scientist’s investigation covers issues in Algorithm, Artificial intelligence, Artificial neural network, Speech recognition and Electronic engineering. His research in Algorithm intersects with topics in Spline, Mathematical optimization, Blind signal separation and Signal processing. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition.
In most of his Artificial neural network studies, his work intersects topics such as Nonlinear system. His Speech recognition research incorporates elements of Speech enhancement, Noise, Feature extraction and Decorrelation. His studies in Electronic engineering integrate themes in fields like Loudspeaker, Frequency domain, Audio signal processing and Equalization.
Francesco Piazza focuses on Artificial intelligence, Speech recognition, Artificial neural network, Context and Pattern recognition. The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence. Francesco Piazza combines subjects such as Decorrelation, Echo, Mel-frequency cepstrum and Audio signal with his study of Speech recognition.
Francesco Piazza specializes in Artificial neural network, namely Deep belief network. His Pattern recognition study combines topics in areas such as Autoencoder and Deep learning. His AC power study incorporates themes from Algorithm and Mathematical optimization.
Francesco Piazza mainly investigates Artificial intelligence, Artificial neural network, Pattern recognition, Context and Speech recognition. His research on Artificial intelligence frequently connects to adjacent areas such as Data mining. His Artificial neural network research includes elements of Dynamic programming, Smart grid and Energy management.
His work investigates the relationship between Pattern recognition and topics such as Deep learning that intersect with problems in Class. His Context research includes themes of Home automation, Algorithm, Focus and Audio signal. His research investigates the connection between Speech recognition and topics such as Mel-frequency cepstrum that intersect with problems in Classifier.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
On the complex backpropagation algorithm
N. Benvenuto;F. Piazza.
IEEE Transactions on Signal Processing (1992)
Optimal Home Energy Management Under Dynamic Electrical and Thermal Constraints
Francesco De Angelis;Matteo Boaro;Danilo Fuselli;Stefano Squartini.
IEEE Transactions on Industrial Informatics (2013)
On-line learning algorithms for locally recurrent neural networks
P. Campolucci;A. Uncini;F. Piazza;B.D. Rao.
IEEE Transactions on Neural Networks (1999)
Learning and approximation capabilities of adaptive spline activation function neutral networks
Lorenzo Vecci;Francesco Piazza;Aurelio Uncini.
Neural Networks (1998)
Fast neural networks without multipliers
M. Marchesi;G. Orlandi;F. Piazza;A. Uncini.
IEEE Transactions on Neural Networks (1993)
Action dependent heuristic dynamic programming for home energy resource scheduling
Danilo Fuselli;Francesco De Angelis;Matteo Boaro;Stefano Squartini.
International Journal of Electrical Power & Energy Systems (2013)
Multilayer feedforward networks with adaptive spline activation function
S. Guarnieri;F. Piazza;A. Uncini.
IEEE Transactions on Neural Networks (1999)
Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models
Roberto Bonfigli;Emanuele Principi;Marco Fagiani;Marco Severini.
Applied Energy (2017)
Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview
Roberto Bonfigli;Stefano Squartini;Marco Fagiani;Francesco Piazza.
international conference on environment and electrical engineering (2015)
Denoising autoencoders for Non-Intrusive Load Monitoring: Improvements and comparative evaluation
Roberto Bonfigli;Andrea Felicetti;Emanuele Principi;Marco Fagiani.
Energy and Buildings (2018)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: