Neural Computation
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Feature selection with neural networks
Pattern Recognition Letters
An introduction to variable and feature selection
The Journal of Machine Learning Research
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
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
Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments
Applied Soft Computing
Information and Software Technology
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This study presents a novel hybrid intelligent system which focuses on the optimisation of machine parameters for dental milling purposes based on the following phases. Firstly, an unsupervised neural model extracts the internal structure of a data set describing the model and also the relevant features of the data set which represents the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques from relevant features of the data set. This model constitutes the goal function of the production process. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The reliability of the proposed novel hybrid system is validated with a real industrial use case, based on the optimisation of a highprecision machining centre with five axes for dental milling purposes.