Route Finding by Neural Nets

نویسندگان

  • Guido Bugmann
  • John G. Taylor
  • Michael J. Denham
چکیده

This paper describes a neural implementation of the resistive grid technique for route finding. The resistive grid, or Laplacian planning technique, is not plagued by local minima problems and guaranties and existing route to be found. The neural network comprises 2 layers. In the upper layer, lateral connections between neurons communicate information on the potentials of neighbouring nodes in the grid. The lower layer represents a spatial memory in which information on the positions of obstacles an the target is stored. Each neuron in the upper layer receives a single input from a node in the lower layer corresponding to the same spatial location. This input is used to constrain the potentials in selected nodes in the resistive grid. The interplay between resistive grid and spatial memory results in a very flexible architecture easily adaptable to new environments. Its properties are demonstrated with a 2-dimensional path-planning problem for a mobile robot. The Dirichlet and Neumann boundary conditions are compared in terms of routes found and computational costs. Limitations and possible developments of the resistive grid technique are discussed.

برای دانلود 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

Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm

Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...

متن کامل

Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm

Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...

متن کامل

New Generation Routing Protocol over Mobile Ad Hoc Wireless Networks based on Neuro-Fuzzy-Genetic Paradigm

There is a vast amount of researched literature available on Route Finding and Link Establishment in MANET protocols based on various concepts such as “pro-active”, “reactive”, “power awareness”, “cross-layering” etc. Most of these techniques are rather restrictive, taking into account a few of the several aspects that go into effective route establishment. When we look at practical implementat...

متن کامل

Prediction of Gain in LD-CELP Using Hybrid Genetic/PSO-Neural Models

In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden layers of El...

متن کامل

Simplifying Neural Nets by Discovering Flat Minima

We present a new algorithm for finding low complexity networks with high generalization capability. The algorithm searches for large connected regions of so-called ''fiat'' minima of the error function. In the weight-space environment of a "flat" minimum, the error remains approximately constant. Using an MDL-based argument, flat minima can be shown to correspond to low expected overfitting. Al...

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

برای دانلود 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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2000