By Simone Bassis, Anna Esposito, Francesco Carlo Morabito
This ebook collects learn works that take advantage of neural networks and desktop studying ideas from a multidisciplinary viewpoint. topics coated comprise theoretical, methodological and computational issues that are grouped jointly into chapters dedicated to the dialogue of novelties and strategies relating to the sector of synthetic Neural Networks in addition to using neural networks for purposes, trend reputation, sign processing, and specific subject matters similar to the detection and popularity of multimodal emotional expressions and day-by-day cognitive features, and bio-inspired memristor-based networks.
Providing insights into the newest learn curiosity from a pool of overseas specialists coming from diverse study fields, the quantity turns into invaluable to all people with any curiosity in a holistic method of enforce plausible, self sustaining, adaptive and context-aware info communique Technologies.
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Additional info for Advances in Neural Networks: Computational and Theoretical Issues
4) 42 D. Comminiello et al. Thereby, the nonlinear error signal is: eFL [n] = d [n] − yFL [n] (5) which is used for the adaptation of wFL,n . In (5), d [n] represents the desired signal for the nonlinear model. Being wFL,n a conventional linear ﬁlter, it can be adapted by any adaptive algorithm based on the minimization of the mean square error . The use of an adaptive ﬁlter after the expansion allows to apply the FLAF model to several online learning applications, such as active noise reduction, acoustic echo cancellation [19,2,17,3].
By considering the three experiments above, it is worth noting that the proposed MPNLMS-FLAF achieves an improvement as large as higher the nonlinearity degree. This is due to the fact that a strong nonlinearity needs a larger expansion buﬀer, whose eﬀectiveness of the nonlinear elements is not uniform but sparse. Moreover, it can be noticed that, unlike the MPNLMS-FLAF, the NLMS-FLAF considers also the useless functional links, which generates overﬁtting. Therefore, we can conclude that the performance gap of the NLMS-FLAF from the proposed MPNLMS-FLAF is essentially due to the overﬁtting.
2 0 2 6 4 Power and Delay of Polynomial 8 Fig. 2. Average MSE in the four cases under consideration, when increasing simultaneously the delay and power of the polynomial from 1 to 9 Table 1. Resulting number of neurons and synapses when using the different pruning strategies Case Neurons Synapses Original 250 62500 Synapse pruning 250 7700 Neuron pruning 110 12000 Full pruning 50 1700 110, with approximately 12000 connections. This means that, together with an increase in performance, the ESN is also faster to train, and easier to eventually implement on an hardware platform.
Advances in Neural Networks: Computational and Theoretical Issues by Simone Bassis, Anna Esposito, Francesco Carlo Morabito