Gabriele Maria Lozito

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The present paper documents the research towards the development of an efficient algorithm to compute the result from a multiple-input-single-output Neural Network using floating-point arithmetic on FPGA. The proposed algorithm focus on optimizing pipeline delays by splitting the "Multiply and accumulate" algorithm into separate steps using partial(More)
A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training,(More)
This paper covers the study towards the implementation of a Neural Network based approach for the efficiency control of Photovoltaic systems. The algorithm aims to track the maximum power point for the PV device whenever abrupt changes in climatic conditions occur. The core of the algorithm is a Neural Network (NN) trained by using a suitable mathematical(More)
Knowing solar irradiance value allows an optimized management of photovoltaic (PV) power plants in terms of produced energy. Unfortunately, although sensing temperature is easy, the measurement of solar irradiance is expensive. In this paper, two circuit architectures for the estimation of the solar irradiance based on simple measurements are proposed. They(More)
This work proposes a maximum power point tracking algorithm based on neural networks embedded in a low-cost 8-bit microcontroller. The obtained device can correctly track the maximum power point even under abrupt changes in solar irradiance and improves the dynamic performance of the power converter that connects photovoltaic power plants into the ac grid.(More)