Luc Neuberg

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In training feed-forward neural networks using the backpropagation algorithm, a sensitivity to the values of the parameters of the algorithm hasbeen observed. In particular, it has been observed that this sensitivity with respect to the values of the parameters, such as thelearning rate, plays an important role in the final outcome. In thistutorial paper,(More)
In this paper, we present a multi agent system (MAS) simulation of a financial market and investigate the requirements to obtain realistic data. The model consists of autonomous, interactive agents that buy stock on a financial market. Transaction decisions are based on a number of individual and collective elements. The former being risk aversion and a set(More)
In this paper, we present a classi®cation model to evaluate the performance of companies on the basis of qualitative criteria, such as organizational and managerial variables. The classi®cation model evaluates the eligibility of the company to receive state subsidies for the development of high tech products. We furthermore created a similar model using the(More)
In this paper, we investigate the dynamic behavior of a backpropagation neural network while learning the XOR-boolean function. It has been shown that the backpropagation algorithm exhibits chaotic behavior and this implies an highly irregular and virtually unpredictable evolution. We study the chaotic behavior as learning progresses. Our investigation(More)
In this paper, we present a model that simulates the behaviour of a heterogenous collection of financial traders on a market. Each trader is modelled as an autonomous, interactive agent and the agregation of their behavior results in market behaviour. 1 We specifically look at the role of information arriving at the market and the influence of(More)
In this paper, we investigate the impact of chaos on the learning process of the XOR-boolean function by backpropagation neural networks. It has been shown previously that such networks exhibit chaotic behavior but it has never been studied whether chaos enhances or prohibits learning. We show that chaos (when learning the XOR-boolean function) does indeed(More)
The purpose of this paper is to analyze how heterogeneous behaviors of agents influence the exchange rate dynamic in the short and long term. We will examine how agents use information and what kind of information, in order to make their decisions to anticipate the exchange rate. We will investigate methodology based on interactive agent simulations,(More)
O ne of the best-known results of the sciences of complexity is that complex systems learn on the edge of chaos, by which is meant that both chaotic and orderly states coexist and that the system remains close to this borderline and may switch from one state to the other. In this article, we take a look inside the learning process of neural networks, and we(More)
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