Neural Computation
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
The rectified Gaussian distribution
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Feature selection with neural networks
Pattern Recognition Letters
An introduction to variable and feature selection
The Journal of Machine Learning Research
Ant Colony Optimization
Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit
Data Mining and Knowledge Discovery
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Dynamic data mining technique for rules extraction in a process of battery charging
Applied Soft Computing
Retrieval parameter optimization using genetic algorithms
Information Processing and Management: an International Journal
Weighted feature extraction with a functional data extension
Neurocomputing
A soft computing method for detecting lifetime building thermal insulation failures
Integrated Computer-Aided Engineering
A meta-heuristic framework for forecasting household electricity consumption
Applied Soft Computing
Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments
Applied Soft Computing
A generic optimising feature extraction method using multiobjective genetic programming
Applied Soft Computing
Information and Software Technology
Neural visualization of network traffic data for intrusion detection
Applied Soft Computing
Preface: The impact of soft computing for the progress of artificial intelligence
Applied Soft Computing
Evolutionary selection of hyperrectangles in nested generalized exemplar learning
Applied Soft Computing
Prediction of dental milling time-error by flexible neural trees and fuzzy rules
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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This study presents a novel soft computing procedure based on the application of artificial neural networks, genetic algorithms and identification systems, which makes it possible to optimise the implementation conditions in the manufacturing process of high precision parts, including finishing precision, while saving both time and financial costs and/or energy. This novel intelligent procedure is based on the following phases. Firstly, a neural model extracts the internal structure and the relevant features of the data set representing the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques. This constitutes the model for the fitness function of the production process, using relevant features of the data set. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The proposed novel approach was tested under real dental milling processes using a high-precision machining centre with five axes, requiring high finishing precision of measures in micrometres with a large number of process factors to analyse. The results of the experiment, which validate the performance of the proposed approach, are presented in this study.