In this paper, a new approach for Self-evolving PArameter-free fuzzy Rule-based Controller (SPARC) is proposed. Two illustrative examples are provided aiming a proof of concept. The proposed controller can start with ...
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ISBN:
(纸本)9781467315074
In this paper, a new approach for Self-evolving PArameter-free fuzzy Rule-based Controller (SPARC) is proposed. Two illustrative examples are provided aiming a proof of concept. The proposed controller can start with no pre-defined fuzzy rules, and does not need to pre-define the range of the output or control variables. This SPARC learns autonomously from its own actions while performing the control of the plant. It does not use any parameters, explicit membership functions, any off-line pre-training nor the explicit model (e.g. in a form of differential equations) of the plant. It combines the relative older concept of indirect adaptive control with the newer concepts of (self-)evolving fuzzy rule-based systems (and controllers, in particular) and with the very recent concept of parameter-free, data cloud and data density based fuzzy rule based systems (and controllers in particular). It has been demonstrated that a fully autonomously and in an unsupervised manner (based only on the data density and selecting representative prototypes/focal points from the control hyper-surface acting as a data space) it is possible generate a parameter-free control structure and evolve it in on-line mode. Moreover, the results demonstrate that this autonomous controller is effective (has comparative error and performance characteristics) to other known controllers, including self-learning ones, but surpasses them with its flexibility and extremely lean structure (small number of prototypes/focal points which serve as seeds to form parameter-free and membership function-free fuzzy rules based on them). The illustrative examples aim primarily proof of concept.
Protein structure prediction (PSP) is an open problem with many useful applications in disciplines such as Medicine, Biology and Biochemistry. As this problem presents a vast search space where the analysis of each pr...
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ISBN:
(纸本)9789898425362
Protein structure prediction (PSP) is an open problem with many useful applications in disciplines such as Medicine, Biology and Biochemistry. As this problem presents a vast search space where the analysis of each protein structure requires a significant amount of computing time, it is necessary to propose efficient search procedures in this very large space of possible protein conformations. Thus, an important issue is to add vital information (such as rotamers) to the process to decrease its active search space -rotamers give statistical information about torsional angles and conformations. In this paper, we propose a new method to refine the torsional angles of a protein to remake/reconstruct its structures with more resemblance to its original structure. This approach could be used to improve the accuracy of the rotamer libraries and/or to extract information from the Protein Data Bank to facilitate solution of the PSP problem.
Affymetrix High Oligonucleotide expression arrays are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biologi...
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Affymetrix High Oligonucleotide expression arrays are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biologi...
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Affymetrix High Oligonucleotide expression arrays are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biological variations and systematic biases that must be removed as much as possible through the pre-processing step before an absolute expression level for every gene is assessed. It is important to evaluate microarray pre-processing procedures not only to the detection of differentially expressed genes, but also to classification, since a major use of microarrays is the expression-based phenotype classification. Thus, in this paper, we use several cancer microarray datasets to assess the influence of five different pre-processing methods in Support Vector Machine-based classification methodologies with different kernels: linear, Radial Basis Functions (RBFs) and polynomial.
In this paper a study of two approaches of a meta-algorithm, Meta-CHC-RBF, is presented. The main goal of this algorithm is to automatically design Radial Basis Function Networks (RBFNs) finding a suitable configurati...
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