In this work a temperature predictor has been designed. The prediction is made by an artificial neural network multilayer perceptron. Initially, the floating point algorithm was evaluated. Afterwards, the fixed point algorithm was designed on a Field Programmable Gate Array (FPGA). The architecture was fully parallelized and a maximum delay of 74 ns was obtained. The design tool used is System ...

In this paper, we propose a deep neural network based natural language processing system for semantic textual similarity prediction. We leverage multi-layer bidirectional LSTM to learn sentence representation. After that, we construct matching features followed by Highway Multilayer Perceptron to make predictions. Experimental results demonstrate that this approach can’t get better results on s...

The cooperative behaviour of interacting neurons and synapses is studied using models and methods from statistical physics. The competition between training error and entropy may lead to discontinuous properties of the neural network. This is demonstrated for a few examples: Perceptron, associative memory, learning from examples, generalization, multilayer networks, structure recognition, Bayes...

In the last few years a lot of research has been carried out in the field of deliverance of information for improving its efficiency and reliability. However, the systematic analysis and verification of channel performance triggered wide interest of new researchers. The popular technique for transmission of signals over wireless channels was orthogonal frequency division multiplexing (OFDM). In...

In this work, linear and nonlinear feature transformations have been experimented in ASR front end. Unsupervised transformations were based on principal component analysis and independent component analysis. Discriminative transformations were based on linear discriminant analysis and multilayer perceptron networks. The acoustic models were trained using a subset of HUB5 training data and they ...

The training sample is an important issue in the learning process of the multilayer perceptron neuronal network. For this reason at the present work the behavior of multilayer perceptron (back propagation algorithm) generalization accuracy using different pre-processing methods of training sample was investigated. In the experiments, diverse techniques were used. These were separated in two gro...

The attempts for solving linear unseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning algorithm. Also called the generalized delta algorithm because it expands the training way of the adaline network, it is based on minimizing the difference between the ...