F
Francesco Piazza
Researcher at Marche Polytechnic University
Publications - 356
Citations - 5470
Francesco Piazza is an academic researcher from Marche Polytechnic University. The author has contributed to research in topics: Artificial neural network & Adaptive filter. The author has an hindex of 31, co-authored 356 publications receiving 4833 citations.
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Journal ArticleDOI
On the complex backpropagation algorithm
Nevio Benvenuto,Francesco Piazza +1 more
TL;DR: A recursive algorithm for updating the coefficients of a neural network structure for complex signals is presented and the method yields the complex form of the conventional backpropagation algorithm.
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Optimal Home Energy Management Under Dynamic Electrical and Thermal Constraints
Francesco De Angelis,Matteo Boaro,Danilo Fuselli,Stefano Squartini,Francesco Piazza,Qinglai Wei +5 more
TL;DR: An approach based on the mixed-integer linear programming paradigm, which is able to provide an optimal solution in terms of tasks power consumption and management of renewable resources, is developed and yields an optimal task scheduling under dynamic electrical constraints.
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On-line learning algorithms for locally recurrent neural networks
TL;DR: A new gradient-based procedure called recursive backpropagation (RBP) is proposed whose on-line version, causal recursive back propagation (CRBP), presents some advantages with respect to the other on- line training methods.
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Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models
Roberto Bonfigli,Emanuele Principi,Marco Fagiani,Marco Severini,Stefano Squartini,Francesco Piazza +5 more
TL;DR: A NILM algorithm based on the joint use of active and reactive power in the Additive Factorial Hidden Markov Models framework is proposed, which outperforms AFAMAP, Hart’s algorithm, and Hart's with MAP respectively.
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Learning and approximation capabilities of adaptive spline activation function neutral networks
TL;DR: This paper studies the theoretical properties of a new kind of artificial neural network, which is able to adapt its activation functions by varying the control points of a Catmull-Rom cubic spline, and shows that its architecture presents several advantages.