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This paper considers the problem of using approximate methods for realizing the neural controllers for nonlinear multivariable systems. In [1] the NARMA-L1 and NARMA-L2 models were introduced as approximations of he NARMA model used for the representation of a SISO nonlinear dynamical systems. The advantage obtained from using NARMA-L1 and NARMA-L2 models(More)
The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods(More)
In information-filtering environments, uncertainties associated with changing interests of the user and the dynamic document stream must be handled efficiently. In this article, a filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties.(More)
Research in bioinformatics in the past decade has generated a large volume of textual biological data stored in databases such as MEDLINE. It takes a copious amount of effort and time, even for expert users, to manually extract useful information embedded in such a large volume of retrieved data and automated intelligent text analysis tools are increasingly(More)
Science Several machine learning approaches have been proposed in the literature to automatically learn user interests for information filtering. However, many of them are ill-equipped to deal with changes in user interests that may occur due to changes in the user's personal and proikssionai situations. If undetected over a long time, such changes may(More)
Accurate and computationally efficient approaches in discovering relationships between biological objects from text documents are important for biologists to develop biological models. This paper presents a novel approach to extract relationships between multiple biological objects that are present in a text document. The approach involves object(More)
Associations among biological objects such as genes, proteins, and drugs can be discovered automatically from scientific literature. This paper describes 'TransMiner' a system for finding interesting associations among objects by mining the Medline database of scientific literature. The direct associations among the objects are discovered based on the(More)
Neural networks with different architectures have been successfully used for the identification and control of a wide class of nonlinear systems. The problem of rejection of input disturbances, when such networks are used in practical problems is considered. A large class of disturbances, which can be modeled as the outputs of unforced linear or nonlinear(More)