Browsing by Author "Silva, S. M."
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- Application of Levenberg-Marquardt method to the training of spiking neural networksPublication . Silva, S. M.; Ruano, AntonioOne of the basic aspects of some neural networks is their attempt to approximate as much as possible their biological counterparts. The goal is to achieve a simple and robust network, easy to comprehend and capable of simulating the human brain at a computational level. This paper presents improvements to the Spikepro algoritm, by introduting a new encoding scheme, and illustrates the application of the Levenberg Marquardt algorithm to this third generation of neural network.
- Application of the Levenberg-Marquardt method to the training of spiking neural networksPublication . Silva, S. M.; Ruano, AntonioOne of the basic aspects of some neural networks is their attempt to approximate as much as possible their biological counterparts. The goal is to achieve a simple and robust network, easy to comprehend and capable of simulating the human brain at a computational level. This paper presents improvements to the Spikepro algoritm, by introduting a new encoding scheme, and illustrates the application of the Levenberg Marquardt algorithm to this third generation of neural network.
- Energy savings in HVAC systems using discrete model-based predictive controlPublication . Ferreira, P. M.; Silva, S. M.; Ruano, AntonioThe paper addresses the problem of controlling an heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most operating conditions are conflicting goals requiring some sort of optimisation method to find appropriate solutions over time. In this work a discrete model based predictive control methodology is applied to the problem. It consists of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, and experimental results obtained within a classroom will be presented, demonstrating the feasibility and performance of the approach. Finally the energy savings resulting from the application of the method are estimated.
- Neural network PMV estimation for model-based predictive control of HVAC systemsPublication . Ferreira, P. M.; Silva, S. M.; Ruano, Antonio; Negrier, Aldric T.; Conceição, EusébioHeating, Ventilating and Air Conditioning (HVAC)systems are used to provide adequate comfort to occupants of spaces within buildings. One important aspect of comfort, the thermal sensation, is commonly assessed by computation of the Predicted Mean Vote (PMV) index. Model-based predictive control may be applied to HVAC systems in existing buildings in order to provide a desired degree of thermal comfort and simultaneously achieve significant energy savings. This control strategy may be formulated as a discrete optimisation problem and solved by means of structured search techniques. Finding the optimal solution depends on the ability of computing many PMV values in a small amount of time. As the PMV formulation involves iterative computations consuming variable time, it is crucial to have a method for fast, possibly constant execution time, computation of the PMV index. In this paper it is experimentally shown that an Artificial Neural Network (ANN) can estimate the PMV index with varying degrees of efficiency over the trade-off of accuracy versus computational speed-up.
- Neural networks based predictive control for thermal comfort and energy savings in public buildingsPublication . Ferreira, P. M.; Ruano, Antonio; Silva, S. M.; Conceição, EusébioThe paper addresses the problem of controlling a Heating Ventilation and Air Conditioning (HVAC) system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most conditions are conflicting goals requiring an optimisation method to find appropriate solutions over time. A discrete model-based predictive control methodology is applied, consisting of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm; the cost function that will be optimised to minimise energy consumption and maintain thermal comfort; and the optimisation method, a discrete branch and bound approach. Each component will be described, with special emphasis on a fast and accurate computation of the PMV indices. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer and winter conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.
- Supervised training approach using spiking neural networksPublication . Silva, S. M.; Ruano, AntonioOne of the basic aspects of some neural networks is their attempt to approximate as much as possible their biological counterparts. The goal is to achieve a simple and robust network, easy to understand and able of simulating the human brain at a computational level. Recently a third generation of neural networks (NN) [1], called Spiking Neural Networks(SNN) was appeared. This new kind of networks use the time of a electrical pulse, or spike, to encode the information. In the first and second generation of NN analog values are used in the communication between neurons.