3,181 research outputs found
Intelligent feature selection for neural regression : techniques and applications
Get PDFFeature Selection (FS) and regression are two important technique categories in
Data Mining (DM). In general, DM refers to the analysis of observational datasets
to extract useful information and to summarise the data so that it can be more
understandable and be used more efficiently in terms of storage and processing.
FS is the technique of selecting a subset of features that are relevant to the
development of learning models. Regression is the process of modelling and
identifying the possible relationships between groups of features (variables).
Comparing with the conventional techniques, Intelligent System Techniques
(ISTs) are usually favourable due to their flexible capabilities for handling realālife
problems and the tolerance to data imprecision, uncertainty, partial truth, etc.
This thesis introduces a novel hybrid intelligent technique, namely Sensitive
Genetic Neural Optimisation (SGNO), which is capable of reducing the
dimensionality of a dataset by identifying the most important group of features.
The capability of SGNO is evaluated with four practical applications in three
research areas, including plant science, civil engineering and economics.
SGNO is constructed using three key techniques, known as the core modules,
including Genetic Algorithm (GA), Neural Network (NN) and Sensitivity Analysis
(SA). The GA module controls the progress of the algorithm and employs the NN
module as its fitness function. The SA module quantifies the importance of each
available variable using the results generated in the GA module. The global
sensitivity scores of the variables are used determine the importance of the
variables. Variables of higher sensitivity scores are considered to be more important than the variables with lower sensitivity scores. After determining the
variablesā importance, the performance of SGNO is evaluated using the NN module
that takes various numbers of variables with the highest global sensitivity scores
as the inputs. In addition, the symbolic relationship between a group of variables
with the highest global sensitivity scores and the model output is discovered
using the MultipleāBranch Encoded Genetic Programming (MBEāGP).
A total of four datasets have been used to evaluate the performance of SGNO.
These datasets involve the prediction of shortāterm greenhouse tomato yield,
prediction of longitudinal dispersion coefficients in natural rivers, prediction of
wave overtopping at coastal structures and the modelling of relationship between
the growth of industrial inputs and the growth of the gross industrial output.
SGNO was applied to all these datasets to explore its effectiveness of reducing the
dimensionality of the datasets. The performance of SGNO is benchmarked with
four dimensionality reduction techniques, including Backward Feature Selection
(BFS), Forward Feature Selection (FFS), Principal Component Analysis (PCA) and
Genetic Neural Mathematical Method (GNMM).
The applications of SGNO on these datasets showed that SGNO is capable of
identifying the most important feature groups of in the datasets effectively and
the general performance of SGNO is better than those benchmarking techniques.
Furthermore, the symbolic relationships discovered using MBEāGP can generate
performance competitive to the performance of NN models in terms of regression
accuracies
Petri Networks in the Planning of Discrete Manufacturing Processes
Get PDFThis chapter puts forward characteristics of selected issues of manufacturing processes planning using the Petri networks technique. It includes references to the extensive literature concerning the use of Petri networks in computer aided planning of discrete production processes. Diversity of these problems is high as it refers both to the methods of modeling and simulation of the course of manufacturing processes, the issue of optimizing these processes and production systems, representation of knowledge on production parts of equipment and integration of planning and production activities in general. The work puts forward example use of a temporary, priority Petri network for modeling and optimizing production systems and manufacturing operations as well as an example of fuzzy interference using the Petri network mechanism
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