… State Networks (ESN). We investigate the computational complexity for each network in …
In this work, the computational complexity is formulated per layer, and the output layer is not …

… digital signal processing remains a challenge. This paper … designing and evaluating
low-complexity neural network equalizers… review of existing methods for reducing complexity in …

… computational complexity that requires special hardware especially for training. Hence, 2D
CNNs are not suitable for real-time applications … training and performance evaluation. There …

… application of artificial neural networks to signal processing followed by a background and
review … low computational complexity in relation to network design, learning, adaptation, and …

… techniques to evaluate neural network's performance with real-… and performance evaluation
of neural network algorithms … (which have larger computational complexity) for linear and …

… The treatment of signal processing is of fundamental importance … theory with signal processing
applications together with the … cost high-density parallel computational hardware makes …

… In what follows we review the fundamentals of Neural Networks in Signal … high computational
complexity. An alternate structure, functional link ANN (FLANN) provides large reduction in …

… complex network, but can instead be treated with classifier-directed feature selection methods.
Evaluation … Applications of Neural-Network Signal Processing to Brain Research Over the …

… In this section, we briefly review the basic ingredients of multilayered, feedforward artificial
neural networks. The reader is encouraged to consult other sources with more complete …

… signal processing and pattern recognition techniques are combined to diagnose the severity
of bearing faults. The signals were pre-processed … , the computational cost of neural network …