We suggest to apply the hybrid neural network based on multi layer perceptron (MLP) and adaptive resonance theory (ART-2) for solving of navigation task of mobile robots. This approach provides semi supervised learning in unknown environment with incremental learning inherent to ART and capability of adaptation to transformation of images inherent to MLP. Proposed approach is evaluated in exper...

| In this paper, we describe important improvements that were recently introduced in our Discriminative Stochastic Segment Model (DSSM) speech recognizer. We propose a new presegmen-tation algorithm and we optimize the structure of the Multi-Layer Perceptron (MLP) that estimates the phone probabilities. Additionally, we describe a cascade MLP combination technique that relaxes the drawbacks of ...

this paper peresents a mathematical model for the thin layer drying of the viliamz cultivar of soybean. the thin layer drying behaviour of soybean was experimentally investigated and the mathematical modelling performed by using thin layer drying models provided in the literature. experiments were conducted at inlet drying air temperatures of 30, 40, 50, 60 and 70ºc and at a fixed drying air ve...

A general problem in model selection is to obtain the right parameters that make a model t observed data. If the model selected is a Multilayer Perceptron (MLP) trained with Backpropagation (BP), it is necessary to nd appropriate initial weights and learning parameters. This paper proposes a method that combines Simulated Annealing (SimAnn) and BP to train MLPs with a single hidden layer, terme...

An unreasonable allocation of resources has led to a low rate output in the industry–university–research collaboration network. A solution this problem is control and predict input output. However, network characteristics strong nonlinearity insufficient samples. It difficult for existing methods migrate networks because traditional methods, including Proportional–Integral–Derivative (PID) Mode...

In this paper, we present a new method for epilepsy seizure detection based on autoregressive modelling. The method, termed linear prediction coding (LPC), is used to model ictal and seizure-free EEG signals. It is found that the modeling error energy is substantially higher for ictal EEG signals compared to seizure-free EEG signals. Moreover, it is known that ictal EEG signals have higher ener...