This paper surveys various implementation of a memory efficient second order (Broyden, Fletcher, Goldfard and Shanno) BFGS training algorithms which includes novel optimal memory (OM) BFGS neural network training algo...
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This paper surveys various implementation of a memory efficient second order (Broyden, Fletcher, Goldfard and Shanno) BFGS training algorithms which includes novel optimal memory (OM) BFGS neural network training algorithm, proposed by the present authors, which optimises performance in relation to available memory. Simulation results using a control benchmark problems show that OM BFGS, which is mathematically equivalent to full memory (FM) BFGS training when there are no constraints on memory, have performance gain compared to other memory efficient BFGS training algorithms.
This work investigates novel sequential learning methods applied on a decomposed form of training algorithms using radial basis function (RBF) network. The dynamic expansion of RBF network by adding neurons to the hid...
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This work investigates novel sequential learning methods applied on a decomposed form of training algorithms using radial basis function (RBF) network. The dynamic expansion of RBF network by adding neurons to the hidden layer during the course of training facilitates the weight update to be decomposed on neuron by neuron basis. The fast or minimal update approach which can be adopted with ease on a decomposed algorithms are also presented in This work.
Direct adaptive control algorithms using state variables in control structure are known for their good adaptation capability and tracking performances that result from the fact that they use plant state variables in c...
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Direct adaptive control algorithms using state variables in control structure are known for their good adaptation capability and tracking performances that result from the fact that they use plant state variables in control law as well as in adaptation law. The implementation of standard model reference adaptive control (MRAC) with state variable control structure requires some a priori knowledge about the plant to be controlled including the plant order. This information is crucial for the proper choice of reference model describing the desired closed loop dynamical behavior and consequently for the adaptive system performances. The aim of our paper is to propose the fuzzy adaptation law for MRAC with state variable structure of control law that is able to ensure the adaptation process convergence and tracking capability even in the presence of unmodelled dynamics.
Traditionally, when approaching controller design with the Youla-Kucera parametrization of all stabilizing controllers, the denominator of the rational parameter is fixed to a given stable polynomial, and optimization...
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Traditionally, when approaching controller design with the Youla-Kucera parametrization of all stabilizing controllers, the denominator of the rational parameter is fixed to a given stable polynomial, and optimization is carried out over the numerator polynomial. In this work, we revisit this design technique, allowing to optimize simultaneously over the numerator and denominator polynomials. Stability of the denominator polynomial, as well as fixed-order controller design with H/sub /spl infin// performance are ensured via the notion of a central polynomial and LMI conditions for polynomial positivity.
We introduce some CNN and analogic feature extraction algorithms for artificial immune systems, which are able to convert grayscale or color to binary images storing as much information as possible for further process...
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We introduce some CNN and analogic feature extraction algorithms for artificial immune systems, which are able to convert grayscale or color to binary images storing as much information as possible for further processing. We define a statistical property called immune histogram based on sub-patterns of these images. Our results and measurements show that these algorithms can be implemented in real-time applications. A sample application, which detects new textures in a familiar environment, is also presented.
A systems re-engineering technique to integrated control and supervision for applications to industrial multi-zone furnaces has been elaborated by using known theories on generalized predictive control and nonlinear p...
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A systems re-engineering technique to integrated control and supervision for applications to industrial multi-zone furnaces has been elaborated by using known theories on generalized predictive control and nonlinear programming. This paper presents the derivation of optimizing equations and inequalities. The design is based on the use of general predictive control to provide optimized set-points under the presumption a well designed regulatory control was implemented at the executive level. Digital implementation of control functions are sought within standard computer process control platform for practical engineering and maintenance reasons.
The aim of this paper is to present a new method for the nonlinear controller design. The underlying theory evolves from the introduced transfer function of nonlinear systems, that can be used in similar way as transf...
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The high practical significance of the I1Td-plant approximations has been shown by the perhaps most frequently used method for controller tuning by (Ziegler and Nichols, 1942), modified later for sampled data systems ...
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The paper deals with an application of neural networks with orthogonal activation functions (OAFNN) in the sensorless field oriented control structure with induction motor (lM). The OAFNN has been trained to estimate ...
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New developments in computer networks and communications provide new possibilities also for control purposes. control systems for highly complex plants are themselves very complex and heterogeneous. A new software and...
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