Computer Science and Information Technology

To implement the specialized learning of the inverse dynamic neuro-controller for controlling nonlinear plants with noise, it is strongly desirable that an on-line trained neural plant emluator may provide a reasonably good estimation of the plant Jacobian under noise environments. This paper presents an approach for enhancing the estimation of the plant Jacobian which is on-line used in direct adaptive neural inverse control schemes. The estimated teaching signals are obtained by using the input–output data available at each time step, and then they are used to train the neural plant emluator by a new cost function introduced in this work for training the plant emluator. Convergence theorem for the adaptive back-propagation algorithm and stability of the closed-loop control system are established by using the Lyapunov theory. Simulations are conducted for demonstrating the effectiveness of the proposed strategy.

This correspondence considers a problem of robust impulse control of uncertain singular systems by decentralized output-feedback. It derives a perturbation upper bound that ensures the existence of decentralized output-feedback control law such that the closed-loop system remains both regularity and impulse free for any allowable perturbations. Algebraic sufficient conditions are also presented that guarantee the existence of so-called decentralized robust impulse controllers.

This paper studies the structural properties of both ÿnite poles and the inÿnite pole of linear time-invariant singular systems under output feedback. Three main problems are studied, namely, (1) the algebraic structures of both ÿnite poles and the inÿnite pole; (2) the assignability of ÿnite poles and the elimination of the inÿnite pole by output feedback; and (3) the controllability and observability of the system with minimal number of inputs and outputs. New generic solutions to these problems are presented in terms of some new concepts deÿned in this paper including the geometric multiplicity of the inÿnite pole, the ÿnite and impulsive output feedback cycle indices of the system. Determination of these multiplicities and indices are discussed. An assignability equivalence is established between the variable ÿnite poles and the poles of a controllable and observable non-singular system. The number of the independent inÿnite poles that can be reduced is given in terms of the system matrices. The minimal number of inputs and outputs that guarantee controllability and observability are shown to be the output feedback cycle indices. ?

Background: Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes to effectively and efficiently evaluate the similarity between a k-mer (a k-length subsequence) and a motif model, without assuming the independence of nucleotides in motif models or without employing computationally expensive Markov chain models to estimate the background probabilities of k-mers. Also, it is interesting and beneficial to use a priori knowledge in developing advanced searching tools.

Computational discovery of DNA motifs is one of the major challenges in bioinformatics, which helps in understanding the mechanism of gene regulation. It has been reported that computational approaches have good potential for problem solving in terms of cost and time saving. Based on our previous studies, this paper aims to develop an evolutionary computation scheme to provide an alternative approach for motif discovery. To work on the framework of our previously developed GAPK, a small sized collection of k-mers is extracted and utilized as “prior knowledge” in algorithm development. Our technical contributions in this paper mainly include a novel fitness function carrying information on conservation and rareness of DNA motifs, and a path to access GAPK-like solutions using seed concept and filtering techniques. The proposed algorithm in this paper has been evaluated by using eight benchmarked datasets, with comparisons to well-known tools such as MEME, MDScan, AlignACE and two GA-based techniques. Results show that our proposed method favorably outperforms other algorithms for these testing datasets.

Abstract Generic results regarding the structural properties of LTI singular system are presented in this paper. These include the structure of the system poles (both finite and infinite), assignment of the finite poles, elimination of the impulse mode, and controllability and observability of the closed-loop system. These properties are characterized by some new concepts defined in this paper.

Abstract. For content-based image retrieval techniques, query image is used to pick up and rank some relevant images from a database using some certain similarity metric. If semantic features are not involved in the modeling of visual data, the resulting system may demonstrate a disability of retrieving images likely associated with interesting semantic concepts of objects in the images.

This paper aims at developing a hybrid scheme for intelligent image retrieval using neural nets. Each item in an image database is indexed by a visual feature vector, which is extracted using color moments and discrete cosine transform coefficients. Query is characterized by a set of semantic labels, which are predefined by system designers and associated with domain concerns. The proposed hybrid image retrieval (HIR) system utilizes the image content features as the system input, and the semantic labels as its output.

Background Discrimination of transcription factor binding sites (TFBS) from background sequences plays a key role in computational motif discovery. Current clustering based algorithms employ homogeneous model for problem solving, which assumes that motifs and background signals can be equivalently characterized. This assumption has some limitations because both sequence signals have distinct properties.

Abstract DNA datasets demonstrate considerably low signal-to-noise ratio that constrains the computational motif discovery tools to achieve satisfactory performances. Thus, reducing the search space and increasing the signal-to-noise ratio (by the means of filtering) can be useful to facilitate computational motif discovery tools with better performing environments.

Abstract Extraction of fuzzy rules from numerical data for fuzzy modeling and control is significant. Such a problem has received considerable attention in the past and some algorithms, termed as the WM algorithm and the iWM algorithm, have been proposed in literature. Research on the WM algorithm and the iWM algorithm showed that some improvements on robustness and completeness of these algorithms can be done.

Abstract This paper investigates algebraic properties of decentralized singular systems (DSSs) under local static output feedback. New concepts of the geometric multiplicity (GM) of the finite decentralized fixed mode (DFM), the decentralized output feedback variable polynomial (DVP), and the finite decentralized output feedback cycle index (DCI) of the DSS are defined. The formulas for determining the GM and the DCI are given in terms of the DFM and the system matrices.

Abstract—Traditionally, two protein sequences are classified into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. Neural network approaches, while reasonably accurate at classification, give no information about the relationship between the unseen case and the classified items that is useful to biologist.

Abstract Friction-induced self-excited vibration is harmful to engineering systems. Understanding this phenomenon and developing some strategies to effectively control the vibration, has theoretical and practical significances. The reasons of friction self-excited vibration are explored through analyzing some numerical characteristics of two kinds of friction models.

It is important to identify DNA motifs in promoter
regions to understand the mechanism of gene regulation.
Computational approaches for finding DNA motifs are well
recognized as useful tools to biologists, which greatly help
in saving experimental time and cost in wet laboratories.
Self-Organizing Maps, as a powerful clustering tool, have
demonstrated good potential for problem solving. However, the
current SOM-based motif discovery algorithms unfairly treat
data samples, lying around the cluster boundaries, by assigning
them to one of the nodes, which may result in unreliable system
performance. This paper aims to develop a robust framework
for discovering DNA motifs, where fuzzy Self-Organizing Maps,
with an integration of fuzzy c-means membership functions and a
standard batch-learning scheme, are employed to extract putative
motifs with varying length in a recursive manner. Experimental
results on eight real datasets show that our proposed algorithm
outperforms the other searching tools such as SOMBRERO,
SOMEA, MEME, AlignACE and WEEDER in terms of the
F-measure and algorithm reliability. It is observed that a
remarkable 24.6%improvement can be achieved compared to the
state-of-the-art SOMBRERO. Furthermore, our algorithm can
produce a 20% and 6.6% improvement over SOMBRERO and
SOMEA, respectively in finding multiple motifs on five artificial
datasets.

Modeling of friction forces has been a challenging task in mechanical engineering. Parameterized approaches for modeling friction find it difficult to achieve satisfactory performance due to the presence of nonlinearity and uncertainties in dynamical systems. This paper aims to develop adaptive fuzzy friction models by the use of data-mining techniques and system theory. Our main technical contributions are twofold: extraction of fuzzy rules and formulation of a static fuzzy friction model and adaptation of the fuzzy friction model by the use of the Lyapunov stability theory, which is associated with a control compensation of a typical motion dynamics. The proposed framework in this paper shows a successful application of adaptive data-mining techniques in engineering. A single-degree-of-freedom mechanical system is employed as an experimental model in simulation studies. Results demonstrate that our proposed fuzzy friction model has promise in the design of uncertain mechanical control systems.