Current Issue : April - June Volume : 2013 Issue Number : 2 Articles : 6 Articles
We propose a novel Cultural Algorithm for the representation of mitochondrial population in mammalian cells as an autonomous\r\nculture. While mitochondrial dysfunctions are highly associated with neurodegenerative diseases and related disorders, an\r\nalternative theoretical framework is described for the representation of mitochondrial dynamics. A new perspective of bioinspired\r\nalgorithm is produced, combining the particle-based Brownian dynamics simulation and the combinatorial representation of\r\nmitochondrial population in the lattice, involving the optimization problem of ATP production in mammalian cells....
This study presents a review of biodegradability modeling efforts including a\r\ndetailed assessment of two models developed using an artificial intelligence based\r\nmethodology. Validation results for these models using an independent, quality reviewed\r\ndatabase, demonstrate that the models perform well when compared to another\r\ncommonly used biodegradability model, against the same data. The ability of models\r\ninduced by an artificial intelligence methodology to accommodate complex interactions\r\nin detailed systems, and the demonstrated reliability of the approach evaluated by this\r\nstudy, indicate that the methodology may have application in broadening the scope of\r\nbiodegradability models. Given adequate data for biodegradability of chemicals under\r\nenvironmental conditions, this may allow for the development of future models that\r\ninclude such things as surface interface impacts on biodegradability for example....
Many information processing problems can be transformed into some form of eigenvalue or singular value problems. Eigenvalue\r\ndecomposition (EVD) and singular value decomposition (SVD) are usually used for solving these problems. In this paper, we\r\ngive an introduction to various neural network implementations and algorithms for principal component analysis (PCA) and\r\nits various extensions. PCA is a statistical method that is directly related to EVD and SVD. Minor component analysis (MCA)\r\nis a variant of PCA, which is useful for solving total least squares (TLSs) problems. The algorithms are typical unsupervised\r\nlearning methods. Some other neural network models for feature extraction, such as localized methods, complex-domain methods,\r\ngeneralized EVD, and SVD, are also described. Topics associated with PCA, such as independent component analysis (ICA) and\r\nlinear discriminant analysis (LDA), are mentioned in passing in the conclusion. These methods are useful in adaptive signal\r\nprocessing, blind signal separation (BSS), pattern recognition, and information compression....
This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is better than RS and RW in identification the forward dynamics and provides good results in the Direct Inverse Neuro- Controller (DINC)....
This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks\r\nInversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the\r\npassive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and\r\nthe network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the\r\nposition of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded\r\nexperimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such\r\nas ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the\r\nsuccess of the proposed control strategy....
We propose a Cooperative Question Answering System that takes as input natural language queries and is able to return a\r\ncooperative answer based on semantic web resources, more specifically DBpedia represented in OWL/RDF as knowledge base\r\nand WordNet to build similar questions. Our system resorts to ontologies not only for reasoning but also to find answers and is\r\nindependent of prior knowledge of the semantic resources by the user. The natural language question is translated into its semantic\r\nrepresentation and then answered by consulting the semantics sources of information. The system is able to clarify the problems\r\nof ambiguity and helps finding the path to the correct answer. If there are multiple answers to the question posed (or to the similar\r\nquestions for which DBpedia contains answers), they will be grouped according to their semantic meaning, providing a more\r\ncooperative and clarified answer to the user....
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