By Simone Bassis, Anna Esposito, Francesco Carlo Morabito
This ebook collects study works that take advantage of neural networks and laptop studying suggestions from a multidisciplinary standpoint. topics coated contain theoretical, methodological and computational issues that are grouped jointly into chapters dedicated to the dialogue of novelties and strategies regarding the sector of man-made Neural Networks in addition to using neural networks for purposes, development attractiveness, sign processing, and precise subject matters resembling the detection and popularity of multimodal emotional expressions and day-by-day cognitive services, and bio-inspired memristor-based networks.
Providing insights into the newest study curiosity from a pool of foreign specialists coming from assorted learn fields, the amount turns into worthwhile to all people with any curiosity in a holistic method of enforce plausible, self reliant, adaptive and context-aware details communique Technologies.
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Additional info for Advances in Neural Networks: Computational and Theoretical Issues
Panella, L. Liparulo, and A. Proietti Several data driven modelling techniques have been taken into consideration: a linear predictor determined by the well-known least-squares (LSE) technique; an RBF neural network; an MoG neural network. All the predictors are trained on the ﬁrst 2000 samples of S(n). The same set of samples is used to compute the embedding dimension D and the time lag T by the AMI and FNN methods in the classical embedding technique. The performance of the resulting predictors, in terms of prediction accuracy, is tested on the successive 1000 samples of the sequence.
The FLAF with memory is characterized by a memory order K . The expanded buﬀer gn is then fed into a linear adaptive ﬁlter wFL,n ∈ RMe = wFL,0 [n] wFL,1 [n] . . wFL,Me −1 [n]]T , thus providing the nonlinear output: yFL [n] = gnT wFL,n−1 . (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 .
For these reasons, we will propose in Sect. 4 a diﬀerent approach, which is based on a genetic algorithm as an advanced embedding technique. In this way, each individual in a generation represents a possible solution for the vector of past samples of S(n) to be used in the approximation task. The use of a genetic algorithm allows the automatic determination of past samples without using the classical techniques for estimating the embedding parameters, Time Series Analysis 23 which are often characterized by a critical accuracy when applied to real-world data sequences.