One of issues related to videoconferencing is addressed in the paper, namely the problem of sound source localization. A new neural approach for estimation direction of arrival (DOA) is presented and discussed therein. The introduced algorithms are based on various types and structures of neural networks, including feedforward and recurrent ones. Considerations of the proposed DOA estimation ar...

& Ersue, 1992) published a few years back on the subject of neurocontrol. The subject of neural networks for modeling and control of dynamical systems represents an important area of applications of artiÿcial neural networks that reemerged in 1980s. On the other hand, to control engineers , neurocontrol, or neural networks for controls, brings about some new and fresh ideas to the modeling and ...

The paper is focused especially on presenting possibilities of applying off-line trained artificial neural networks at creating the system inverse models that are used at designing control algorithm for non-linear dynamic system. The ability of cascade feedforward neural networks to model arbitrary non-linear functions and their inverses is exploited. This paper presents a quasi-inverse neural ...

Local linear function approximators are often preferred to feedforward neural networks to estimate value functions in reinforcement learning. Still, motor tasks usually solved by this kind of methods have a low-dimensional state space. This article demonstrates that feedforward neural networks can be applied successfully to high-dimensional problems. The main difficulties of using backpropagati...

This study forecasts trading signals of the Australian All Ordinary Index (AORD), one day ahead. These forecasts were based on the current day’s relative return of the Close price of the US S&P 500 Index, the UK FTSE 100 Index, French CAC 40 Index and German DAX Index as well as the AORD. The forecasting techniques examined were feedforward and probabilistic neural networks. Performance of the ...

It is often hypothesized that a crucial role for recurrent connections in the brain is to constrain the set of possible response patterns, thereby shaping the neural code. This implies the existence of neural codes that cannot arise solely from feedforward processing. We set out to find such codes in the context of one-layer feedforward networks and identified a large class of combinatorial cod...

Feedforward neural networks (FFNN) have been utilised for various research in machine learning and they have gained a significantly wide acceptance. However, it was recently noted that the feedforward neural network has been functioning slower than needed. As a result, it has created critical bottlenecks among its applications. Extreme Learning Machines (ELM) were suggested as alternative learn...

In this paper, the combination of unsupervised clustering algorithms with feedforward neural networks in exchange rate time series forecasting is studied. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Combi...

The Mixture of Neural Networks (MixNN) is a Multi-Net System based on the Modular Approach. The MixNN employs a neural network to weight the outputs of the expert networks. This method decompose the original problem into subproblems, and the final decision is taken with the information provided by the expert networks and the gating network. The neural networks used in MixNN are quite simple so ...