Condition monitoring of the cutting process using a self-organizing spiking neural network map

Authors:
Rui G. Silva
Affiliations:
Universidade Lusíada de VN Famalicão, Vila Nova de Famalicão, Portugal 4760-108
Venue:
Journal of Intelligent Manufacturing
Year:
2010

Citing 6
Cited 1

Multilayer feedforward networks are universal approximators

Neural Networks
On computation with pulses

Information and Computation
Spiking neurons

Pulsed neural networks
Computing with spiking neurons

Pulsed neural networks
Multi-step-ahead prediction using dynamic recurrent neural networks

Neural Networks
Spiking neurons and the induction of finite state machines

Theoretical Computer Science - Natural computing

Tool wear estimation using an analytic fuzzy classifier and support vector machines

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new approach to sensor based condition monitoring using a self-organizing spiking neuron network map. Experimental evidence suggests that biological neural networks, which communicate through spikes, use the timing of these spikes to encode and compute information in a more efficient way. The paper introduces the basis of a simplified version of the Self-Organizing neural architecture based on Spiking Neurons. The fundamental steps for the development of this computational model are presented as well as some experimental evidence of its performance. It is shown that this computational architecture has a greater potential to unveil embedded information in tool wear monitoring data sets and that faster learning occurs if compared to traditional sigmoidal neural networks.