A general class of Computationally Efficient locally Recurrent Networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the-parameters single-hiddenlayered feedforward neural networks such as the Radial Basis Function (RBF) network, the Volterra Neural Network (VNN) and the recently developed Functionally Expanded Neural Network (FEN...

This postulate was experimentally confirmed in the hippocampus, where high-frequency stimulation (HFS) of a presynaptic neuron causes long-term potentiation (LTP) in the synapses connecting it to the postsynaptic neurons (Bliss and Lomo, 1973). LTP takes place only if the postsynaptic cell is also active and sufficiently depolarized (Kelso et al., 1986). In many brain areas, this is due to the ...

The initial set of weights to be used in supervised learning for multilayer neural networks has a strong influence in the learning speed and in the quality of the solution obtained after convergence. An inadequate initial choice of the weight values may cause the training process to get stuck in a poor local minimum or to face abnormal numerical problems. Nowadays, there are several proposed te...

In cortical neural networks, connections from a given neuron are either inhibitory or excitatory but not both. This constraint is often ignored by theoreticians who build models of these systems. There is currently no general solution to the problem of converting such unrealistic network models into biologically plausible models that respect this constraint. We demonstrate a constructive transf...

Predicting the age of a blogger based on the text of their writing is a difficult task due to the fluidity of age identity and the effect of aging on writing styles. We propose feedforward and recurrent neural network frameworks to address this problem without enforcing human-generated features and find that shallow networks suffice for this problem. Results suggest that a scaled bag-of-words f...

Unlike feedforward neural networks (FFNN) which can act as universal function approximaters, recursive neural networks have the potential to act as both universal function approximaters and universal system approximaters. In this paper, a globally recursive neural network least mean square (GRNNLMS) gradient descent or a real time recursive backpropagation (RTRBP) algorithm is developed for a s...

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were ...

The importance of the electric power quality (PQ) demands new methodologies and measurement tools in the power industry for the analysis and measurement of the basic electric magnitudes necessary. This paper presents a new measurement procedure based on neural networks for the estimation of harmonic amplitudes of current/voltage and respective harmonic powers. The measurement scheme is built wi...

As various kinds of output devices emerged, such as highresolution printers or a display of PDA(Personal Digital Assistant), the importance of high-quality resolution conversion has been increasing. This paper proposes a new method for enlarging image with high quality. One of the largest problems on image enlargement is the exaggeration of the jaggy edges. To remedy this problem, we propose a ...