ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR

Martin Ruzek

doi:10.14311/app.2017.12.0099

Abstract

This paper presents a new approach to mental functions modeling with the use of artificial neural networks. The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly inspired by the biological neuron. On the other hand, the classical paradigms of artificial neural networks are not suitable because they simplify too much the real processes in biological neural network. The search for a compromise between the complexity of biological neural network and the practical feasibility of the artificial network led to a new learning algorithm. This algorithm is based on the classical multilayered neural network; however, the learning rule is different. The neurons are updating their parameters in a way that is similar to real biological processes. The basic idea is that the neurons are competing for resources and the criterion to decide which neuron will survive is the usefulness of the neuron to the whole neural network. The neuron is not using "teacher" or any kind of superior system, the neuron receives only the information that is present in the biological system. The learning process can be seen as searching of some equilibrium point that is equal to a state with maximal importance of the neuron for the neural network. This position can change if the environment changes. The name of this type of learning, the homeostatic artificial neural network, originates from this idea, as it is similar to the process of homeostasis known in any living cell. The simulation results suggest that this type of learning can be useful also in other tasks of artificial learning and recognition.

Highlights

For many practical applications it would be useful to have a model of a human operator, as for example in transport engineering where the human operator plays a principal role
The human factor causes most of the accidents; it would be helpful to understand the processes in the human brain
The artificial neural networks (ANN) seems to be a promising method for these models because the architecture of the ANN is directly inspired by the biological neuron and because it is data driven method

Summary

Introduction

For many practical applications it would be useful to have a model of a human operator, as for example in transport engineering where the human operator plays a principal role. Due to the complex nature of the human brain it is very difficult to model and predict its behavior He need to dispose of the model of a human operator, as the most complex part of many transport systems, stays behind this research. The expectation is not to model mental processes, but to use the principles of biological neural network to improve the computer architecture. The expectation of SyNAPSE project is to understand the advantages of this architecture and to use them in formation of novel computer architecture. With respect to both above mentioned researches, the target of this paper is to find such an equilibrium point where the model of the neuron is still faithful enough so that it can model the strong processes and. Acta Polytechnica CTU Proceedings yet be simple enough to be practically realizable

New solution – homeostatical neural network

Criterions for optimization

Searching the one neuron maximum – the second extreme

Variants of homeostatical neural network

Problems of the idea of homeostatical learning

Conclusions