Neural networks, such as multi-layer perceptron (MLP) networks which converge slowly, have been applied for tra c and congestion control in ATM networks. In this paper, we present a Connection Admission Control (CAC) scheme using modular and hierarchical neural networks for predicting the resulting cell loss rate (CLR) when calls are accepted. The fast learning and accurate predictions obtained...

-Fuzzy ARTMAP is one of the recently proposed neural network paradigm where the fuzzy logic is incorporated. In this paper, we compare the Fuzzy ARTMAP neural network and the well-known back-propagation based Multi-layer perceptron (MLP), in the context of hand-written character recognition problem. The results presented in this paper shows that the Fuzzy ARTMAP out-performs its counterpart, bo...

A globally convergent homotopy method is defined that is capable of sequentially producing large numbers of stationary points of the multi-layer perceptron mean-squared error surface. Using this algorithm large subsets of the stationary points of two test problems are found. It is shown empirically that the MLP neural network appears to have an extreme ratio of saddle points compared to local m...

Adaptive systems research is mainly concentrated around optimizing cost functions suitable to problems. Recently, Principe et al. proposed a particle interaction model for information theoretical learning. In this paper, inspired by this idea, we propose a generalization to the particle interaction model for learning and system adaptation. In addition, for the special case of supervised multi-l...

This paper proposes a new hybrid approach which combines simulated annealing and standard backpropagation for optimizing Multi Layer Perceptron Neural Networks for time series prediction. Experimental results have shown that this approach selects the appropriate time series lags and builds an MLP with adequate number of hidden neurons required for achieving good performance on the task. The per...

In this paper, a method of classification of handwritten signature based on neural networks, and FPGA implementation is proposed. The designed architecture is described using Very High Speed Integrated Circuits Hardware Description Language (VHDL). The proposed application consists of features extraction from handwritten digit images, and classification based on Multi Layer Perceptron (MLP). Th...

Following Kiperwasser and Goldberg (2016), we present a multilingual dependency parser with a bidirectionalLSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingu...

In the paper a new structure of Multi-Layer Perceptron, able to deal with quaternion-valued signal, is proposed. A learning algorithm for the proposed Quaternion MLP (QMLP) is also derived. Such a neural network allows to interpolate functions of quaternion variable with a smaller number of connections with respect to the corresponding real valued MLP. INTRODUCTION In the last few years, neural...

This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, t...