Evolving Neuro - Modules and their Interfacesto Control

نویسنده

  • F. Pasemann
چکیده

An evolutionary algorithm for the creation of recurrent network structures is presented. The aim is to develop neural networks controlling the behaviour of miniature robots. Two neuro-modules are created separately using this evolutionary approach. The rst neuro-module gives the agents the ability to move within an environment without colliding with obstacles. The second neuro-module provides the agents with a phototropic behaviour. The interaction of the neuro-modules is then investigated evolving the necessary interface to provide the agents with a coherent obstacle avoidance and phototropic behaviour. The evolution process is carried out in a simulated environment and individuals with high performance are also tested on a physical environment with the use of Khepera robots.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Search Space Restriction of Neuro-evolution through Constrained Modularization of Neural Networks

Evolving recurrent neural networks for behavior control of robots equipped with larger sets of sensors and actuators is difficult due to the large search spaces that come with the larger number of input and output neurons. We propose constrained modularization as a novel technique to reduce the search space for such evolutions. Appropriate neural networks are divided manually into logically and...

متن کامل

Evolving Humanoid Behaviours for Language Games

Evolutionary techniques are applied to develop the neural control of humanoid robots. These robots were designed to act as agents in language games played in the context of the EU-project ALEAR. The basic ingredients needed to bring forth the desired behaviours are described: an appropriate physical simulator of the robots, an interactive evolution environment and various analysis tools. A modu...

متن کامل

Evolving Computation Offers Potential for Estimation of Pest Establishment

This paper introduces an evolving computational and a statistical model for quantitatively estimating the establishment potential of a pest insect and compares their performances. The models were used to predict the establishment potential of Planocuccus citri (Risso), the citrus mealybug. They have the common clustering and probability evaluation modules, but very different regression modules....

متن کامل

Evolving fuzzy optimally pruned extreme learning machine for regression problems

This paper proposes an approach to the identification of evolving fuzzy Takagi–Sugeno systems based on the optimally pruned extreme learning machine (OP-ELM) methodology. First, we describe ELM, a simple yet accurate learning algorithm for training single-hidden layer feed-forward artificial neural networks with random hidden neurons. We then describe the OP-ELM methodology for building ELM mod...

متن کامل

An Evolving Cascade Neuro-Fuzzy System for Data Stream Fuzzy Clustering

An evolving cascade neuro-fuzzy system and its online learning procedure are proposed in this paper. The system is based on nodes of a special type. A quality estimation process is defined by finding an optimal value of the used cluster validity index. Keywords— Evolving cascade system, neuro-fuzzy network, data stream, fuzzy clustering.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2001